Reviewerly/exHarmony huggingface dataset:
Cooperative Situation Awareness of Multi-UAVs Based on Multi-sensor Information Fusion

In recent years, situation awareness has been an essential research subject in the field of low altitude combat. However, previous work mainly focused on single unmanned aerial vehicle (UAV) detection, while it may obtain inaccurate information about the environment due to the limited detection ability and uncertainty of single airborne sensor. By contrast, this paper considers the cooperative detection of multi-UAVs and proposes a cooperative situation awareness method on the basis of multi-sensor information fusion. Firstly, obstacle information is detected and obtained by multiple airborne sensors considering the sensors’ uncertainty. Subsequently, D-S evidence theory is introduced to conduct the information fusion of obstacle with high confidence and more accurate obstacle coordinates are obtained. Next, the simulations of information fusion are conducted under fixed and flexible formations. Eventually, numerical results verify the validity of the proposed information fusion method, which can perceive the situation more accurately and reach at most 96.58% degree of confidence in a specific formation, thus achieving a precise cooperative situation awareness.
Attacking Satellite Remote Sensing Detection Using Saliency Constrained Adversarial Patch

Using deep learning technology to detect ground optical images captured by remote sensing satellites can identify high-confidence classes and location information of detection targets. However, deep neural networks are fragile against adversarial examples, which pose a huge threat to deep learning-based satellite remote sensing techniques. The detection of deep learning-based detectors can be evaded by adding subtle perturbations to the detection target, or even misleading the detection results of the detector. In this paper, we will attack satellite remote sensing detection with adversarial patch, which can achieve the attack effectiveness of adversarial example from the digital domain to the attack effectiveness of the physical domain. We use pairwise-distance loss to constrain the salience of adversarial patch. Experiments show that our method can effectively reduce the Recall rate of YOLOV3-based satellite remote sensing algorithm to about 1%, which can almost completely avoid the detection of the detector. The study of adversarial examples has great reference value for the study of the security of satellite remote sensing detection.
Search-Based Motion Planning for Quadrotor Transporting a Cable-Suspended Load

Trajectory planning plays an important part in studies related to aerial transportation systems implemented by suspending the payload beneath the quadrotor. However, generating a feasible trajectory for the cable suspended aerial transportation system in complex environments can be a challenge, considering the complicated dynamic of the system and obstacle avoidance. This paper addresses the aforementioned challenges by modeling the dynamical system with the help of differential flatness, and a novel search-based planning method for trajectory generation in cluttered environments is proposed. Specifically, by utilizing motion primitives to conduct the discretization on the state space, the optimization problem is converted to graph search which can be solved by A* algorithm. Subsequently, the search heuristic is defined by solving a linear quadratic minimum time problem, where both control effort and time consumption are considered. Relevant simulation results verify the feasibility of the generated trajectory.
Code Recognition of Aircraft Engine Fan Blades Based on Improved LeNet-5

Manual collection of engine fan blade code information has low efficiency and is not conducive to the digital management of civil aviation aircraft maintenance. This paper established a training set by acquiring aircraft engine fan blade coding images, and then pre-processed the images to segment the aircraft engine fan blade pressure surface coding characters. An improved LeNet-5 model was proposed for the characteristics of etched characters on metal surfaces, and its training accuracy can reach 98.77% through the calculation of the loss function. Comparing the traditional LeNet-5 and BP neural network models with the proposed network model, the Matlab simulation test results show that the code recognition accuracy rate can reach 97.63%. It is verified that the method can more accurately and effectively identify the code of aircraft engine fan blades, and has the characteristics of high identification efficiency.
Hierarchical Trajectory Planning for Multi-UAVs via Sparse A* Search and Linear Programming

This paper proposes a hierarchical trajectory planning method to generate dynamically-feasible cooperative trajectories rapidly for multi-UAVs. In the first stage, the efficient sparse A* search algorithm (SAS) is adopted to plan geometric path as the initial guess for trajectory iterations. Secondly, based on the geometric path, the nonlinear trajectory optimization problem is cast into linear programming problems (LP), and LP has low computational complexity and appealing convergence performance. Simulation experiment results manifest that the hierarchical trajectory planning has the ability to eliminate the sensitivity to initial guess and improve the convergence. The comparative results show that hierarchical trajectory planning method outperforms distributed sequential convex programming in terms of computation efficiency.
Parameters Setting of Firefly Algorithm

Parameter setting is one of the long-standing grand challenges of the metaheuristic algorithm filed. The effect of the optimization algorithm will mainly depend on the setting of the algorithm-dependent parameters. The best result for solving the optimization problems can be achieved by the optimal setting of the algorithm’s parameter. In this paper, we investigate the parameter setting problem of firefly algorithm. The sufficient simulation results are showed that with proper setting of the parameter firefly algorithm can converge efficiently and accurately.
Real-Time Trajectory Planning for Hypersonic Vehicle with Dynamic No-Fly Zone Constraints

This paper pays attention to the trajectory planning method of hypersonic glide vehicle. In the glide phase, the near-space vehicle is vulnerable to be detected and intercept. Therefore, an on-line obstacle avoidance trajectory planning method based on the improved artificial potential field method for the glide section was proposed. Combined with the geometric analysis, the prediction of valid no-fly zone is added to the repulsion potential field, which is beneficial for the vehicle to earlier detect the threat area. According to the multiple constraints and the existence of dynamic environment, the velocity factor is added as an improvement to modify the traditional artificial potential field in three-dimensional space, which can realize the online requirement as well. The simulation results verified the efficiency of proposed algorithm.
Event-Triggered Distributed Multisensor Multitarget Tracking with GM-PHD Filter

This paper focuses on a distributed event-triggered Gaussian mixture probability hypothesis density (GM-PHD) filtering algorithm. In this algorithm, sensors transmit their Gaussian components to neighbors basing on the event-triggered conditions. Then Gaussian components are fused for certain times by matrix reformulation method. This algorithm can reduce data transmission and energy consumption with guaranteeing the estimation performance. Simulation experiment verifies the performance of this algorithm.
Research on the Professional Development of International Chinese Teachers under the Background of Wisdom Education

With the rapid development of information technology, education has also undergone tremendous changes.The smart teaching model is one that integrates and integrates advanced science and technology such as modern information, network technology and computers in order to achieve students' self-construction in the learning space.In this paper, we study the concept and model of smart education, identify the problems in the development of international Chinese teachers, analyse the causes of these problems, and make suggestions to international Chinese teachers so that they can better adapt to the development of the times and continuously improve themselves under the guidance of the concept of "smart education".This is to help international Chinese teachers to better adapt to the development of the times, improve their own quality under the guidance of the concept of "smart education", and contribute to the promotion of international Chinese education.
Rapid face detection in complex environments based on the improved RetinaFace

Now the epidemic prevention and control has been continuing, frequent removal and wearing of masks in railway stations, subway stations and places such as commuting to and from work are prone to the spread of the virus, and face detection is an important part of the face recognition system. Aiming at the complex environmental factors such as partial occlusion, angle change, light intensity and face blur in face detection in these places, This paper improves the detection accuracy by improving the RetinaFace algorithm. Firstly, the lightweight GhostNet network is introduced to substitute the former MobileNet0.25 backbone network of RetinaFace, and a lightweight model improved version of RetinaFace is obtained, which not only ensures that the model is smaller but also ensures the speed of face detection; In addition, The efficient ECA channel attention mechanism is fused in the enhanced feature extraction network of the model to further enhance the detection performance of small face samples in complex environments. Finally, the simulation conclusion show that compared with the former RetinaFace algorithm, the detection performance of this method in the verification set of different levels of the reconstructed WIDER FACE dataset reaches 93.4% (Easy), 90.8% (Medium) and 77.1% (Hard), which is improved by 2.7 percentage points, 2.2 percentage points and 5 percentage points, respectively. It can be seen that after the introduction of GhostNet network and ECA attention mechanism, the recognition accuracy of faces in complex environments is further improved and network performance is improved.
BERT Model Compression With Decoupled Knowledge Distillation And Representation Learning

Pre-trained language models such as BERT have proven essential in natural language processing(NLP). However, their huge number of parameters and training cost make them very limited in practical deployment. To overcome BERT’s lack of computing resources, we propose a BERT compression method by applying decoupled knowledge distillation and representation learning, compressing the large model(teacher) into a lightweight network(student). Decoupled knowledge distillation divides the classical distillation loss into target related knowledge distillation(TRKD) and non-target related knowledge distillation(NRKD). Representation learning pools the Transformer output of each two layers, and the student network learns the intermediate features of the teacher network. It has better results on tasks of Sentiment Classification and Paraphrase Similarity Matching, retaining 98.9% performance of the large model.
Adaptive Threshold for Unknown Traffic Identification

Network traffic classification has become an important foundation of network security. However, as the types of protocols and applications of the network continue to increase, unknown network traffic is also emerging. In the face of unknown network threats, how to identify unknown network threats in a complex network environment to make corresponding preparations in advance has become extremely important. Aiming at the influence of unknown traffic on classification accuracy in the prediction process, this paper proposes an Adaptive Threshold for Unknown Traffic Identification (AT-UTI) algorithm using particle swarm optimization algorithm to optimize the search of the set threshold, to reduce the impact of unknown traffic on the accuracy of the model. We evaluated our model achieving an accuracy of 93.27%. Our results demonstrate the effectiveness of AT-UTI in unknown traffic identification.
Multi-Scale Deformable Context Fusion Pyramid Network for Object Detection in Aerial Images

In recent years, although many methods have made good progress in the object detection of aerial images, it is still difficult to improve the detection accuracy due to the large scale variation, exceptional aspect ratios and a large number of small and dense objects in aerial images. In this paper, we propose a Multi-Scale Deformable Context Fusion Pyramid Network (Multi-Scale DCFPN) for the object detection of aerial images. In the proposed Multi-Scale DCFPN, we design a Multi-Scale Deformable Context Fusion (MDCF) module, which injects context information into Feature Pyramid Network (FPN) in a residual manner. In particularly, MDCF module consists of a set of parallel Adaptive Deformable Context (ADC) module with special dilated rates and Adaptive Context Fusion (ACF) module. ADC module focus on the objects with exceptional aspect ratios by introducing a deformable paradigm, and ACF module adaptively combines the multi-scale context instead of summation. To verify the effectiveness of the proposed method, we execute some comparative experiments on aerial public datasets NWPU VHR-10 and DIOR, and the mAP increased by 3.5 and 1.0 points compared with the baseline, respectively. The experimental results also show that the proposed method outperforms the state-of-the-art methods.
Hiking Path Planning Algorithm Based on Dynamic Parameters and Anisotropy in Field Integrated Terrain

The purpose of this paper is to solve hiking path planning in the field integrated terrain and establish a method (DA-FM2) to find the shortest path based on anisotropy and dynamic parameters. In combination with geographical characteristics, the cost function of this method is creatively represented as the weighted sum of isotropic and anisotropic factors, which comprehensively uses the isotropic and anisotropic algorithms to meet the needs of path finding with the lowest cost. Simulation results show that DA-FM2 algorithm has good stability and reliability.
Regions detection in cervical cytology to help the diagnosis of cervical cancer

Cervical cancer is an important cause of mortality in women worldwide, it is the neoplasm with the greatest potential proven secondary prevention. This disease is completely preventable and curable. Many computer vision tools are being exploited in the biomedical field to discover patterns and analyze the morphology of cervical cancer among women. The main objective of this work is to show the detection of cells in a cervical cytology because the cytological screening analysis is based on the experience of the cytotechnologist. However, in this document the Gaussian Laplacian is used for the extraction of stains within a cytological image, converts the image to binary and applies a threshold range, which generates stains with properties that remain approximately constant.
Bandwidth Allocation to Traffic and QoE Evaluation for Video Streaming Services

Video streaming is one of the most popular and highest bandwidth-consuming media on the internet. However, network bandwidth is a limited and costly resource. Therefore, usage should be managed well in order to optimize the management of available bandwidth resources. In this work, it is proposed to evaluate the traffic of online streaming applications in a bandwidth-constrained environment by testing the performance of Disney+, Netflix, and YouTube websites. The experiment has been done with both unlimited and limited bandwidth. The experiment has displayed protocol types, and data size, has measured QoS including throughput, delay, packet loss, and jitter of online video services, and it has compared the aforementioned elements with TIPHON standard specification. As a result, when the bandwidth has been limited, it has been found out that throughput has been in a bad range. This study recommends a bandwidth limitation of 2 Mbps for the greatest results for effective bandwidth usage. The importance of evaluating user satisfaction with service providers has been considered by measuring QoE levels using an individual rating or MOS for assessing the quality of video streams. This research has provided a guideline for managing the available bandwidth resources for the optimized benefit of all ISPs and users. This is a cost-effective and efficient use of bandwidth resources.
Questo non è un manuale? Il <i>limen </i>(sottile) tra <i>pragmatical method </i>e didattica <i>per exempla</i>

Two loci critici (Ov. am. 3, 15, 18; AL 52 R. = 39 Sh. B. = Anon. vers. serp. 14 Zurli) examined by Loriano Zurli in the volume Il limen (sottile) tra congettura e restituzione (Hildesheim 2020) are illustrated and discussed from a philological point of view and by adopting also a methodological and didactic perspective.
Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers

Big Data, the Internet of Things and Machine Learning are only today starting to be widely used but are already attracting interest. They can generate a significant impact on business management. This article analyses use and exploitation of Big Data by business management, focusing on its role in reshaping accounting information systems. The Internet of Things and Machine Learning play a key role in obtaining insights and value in this complex world. Like other areas of business, the accounting function is showing growing interest in their possible applications. We analyze, from three perspectives, how big data impacts on the accounting role in supporting managers and decision-making process, also with the aim to define future research lines that scholars could explore. An internal perspective focuses on how big data can impact management accounting; an external perspective focuses on a new dimension of financial accounting and disclosure of information; and a third perspective, the control one, fo- cuses on the impact of big data on internal and external audit procedures.
Management control in inter-firm relationships: Opportunities and challenges of blockchain technology adoption

Accounting research has not yet focused on the potential contribution of blockchain technology (BT) to the management control of inter-firm relationships. This study explores to what extent BT addresses some of the key issues related to inter-organizational control identified in the previous literature on the topic. The research is based on the case study approach. In particular, in order to address the research objectives, the study develops the case study of ROUGE (Red Orange Upgrading Green Economy), a project implementing BT in the production and distribution of the Sicilian red orange. The study identifies, explores and discusses the key opportunities and challenges related to BT adoption in inter-firm settings management control. While BT may allow to improve data management and operations, build trust and foster collaboration, its implementation requires tackling technology and cultural resistance. The last years have been characterized by a significant increase in studies focusing on BT and accounting. However, few have developed empirical cases. Moreover, to the author's knowledge, this is the first study specifically focused on BT and inter-organizational management control.
Trend Analysis of Research Papers Related to Children’s Traditional Play

This study analyzed the trends of 80 research papers related to children's traditional play from1990 to June 2022. The data were divided into general research trends (research age, researchtopic) and research methods and research trends (research type, research subject, data collectionmethod, data analysis method, and research result), and analyzed in terms of frequency andpercentage. As a result of the study, it was found that the research period for children'straditional play was the most studied in the 3rd period (2011-2020), and the effect study was relatively the most researched on the research topic. Second, it was found that experimentalstudies of quantitative research were used a lot in the research type, infants in the studysubjects, the test method of quantitative research in the data collection method, and differenceverification in the data analysis method. As a result of the study, most of the studies suggestingthat traditional play has a positive effect on the development of children. The significance ofthis study is that it suggests the possibility of establishing a core play in the Nuri curriculumwhile comprehensively analyzing the current status and trend of research on children'straditional play.
Metaphorical Analysis of pre-service early childhood teachers' Perceptions of Parent and Parent-Child Relationships

This study conducted a metaphorical analysis of the parent-parent-child relationship targeting3rd graders in the early childhood education department. As a result, pre-primary earlychildhood teachers regard their parents as 'parents as emotional supporters', 'parents as victims','parents as absolute beings with strength', 'parents as guardians', 'parents in the process ofself-growth', 'guides of life' Parents as 'parents' and 'parents as examples of life' were recognized in that order. In addition, parent-child relationships were recognized in the order of'essential relationship', 'symbiotic relationship', 'equality relationship', 'trust relationship','relationship where love and hate coexist', and 'relationship that grows together' was doingThrough this, it will be an opportunity to derive the operational direction of parenteducation-related subjects opened in the early childhood teacher training course. In the future,effective parent education will be achieved by forming a trusting relationship between parentsand teachers.
Audio - Article Summaries Volume 12, Special Issue (Spanish)

As an added service to those visiting the journal, the HLRC provides in English and Spanish a brief audio summary of the articles published in the issue. This is the Spanish version.
Community Education Knowledge Graph Based on Co-Occurrence Analysis and Log-Likelihood Ratio Algorithm

Co-occurrence analysis based on keywords is able to generate a "document-keyword" matrix, which evaluates the relationship between documents; Log-likelihood Ratio algorithm can be used to calculate the similarity between high-frequency keywords, and to do cluster analysis of subject keywords. The research object of the paper is domestic and foreign community education research papers; the data sources are the "academic journals" of China National Knowledge Infrastructure and the "core collections" in Web of Science; the general research method is to use CiteSpace to carry out quantitative analysis of documents and generate a visual knowledge graph; do information extraction and co-occurrence analysis on the publication time, authors, research institutions, etc.; generate relationship network and cut it based on the density to improve the readability of the network; use the log-likelihood algorithm to extract keywords and generate cluster, and summarize three hotspots in domestic community education research; draw the knowledge graph of community education research at home and abroad. From the knowledge graph we find that foreign research focuses on the analysis of the actual teaching effect of community schools and the prevention and treatment of specific diseases in the community. It points out that domestic research should strengthen the theoretical basis of community education, combine theory and practice and summarize typical cases for promotion.