Reviewerly/exHarmony huggingface dataset:
UAV patrol path planning based on machine vision and multi-sensor fusion

Abstract With the rapid development of unmanned aerial vehicle (UAV) technology, there are more and more fields of UAV application. This research mainly discusses the UAV patrol path planning based on machine vision and multi-sensor fusion. This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human–computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100 m is 99.80 m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.
Towards the Links of Cryptanalytic Methods on MPC/FHE/ZK-Friendly Symmetric-Key Primitives

Symmetric-key primitives designed over the prime field Fp with odd characteristics, rather than the traditional Fn2 , are becoming the most popular choice for MPC/FHE/ZK-protocols for better efficiencies. However, the security of Fp is less understood as there are highly nontrivial gaps when extending the cryptanalysis tools and experiences built on Fn2 in the past few decades to Fp.At CRYPTO 2015, Sun et al. established the links among impossible differential, zero-correlation linear, and integral cryptanalysis over Fn2 from the perspective of distinguishers. In this paper, following the definition of linear correlations over Fp by Baignères, Stern and Vaudenay at SAC 2007, we successfully establish comprehensive links over Fp, by reproducing the proofs and offering alternatives when necessary. Interesting and important differences between Fp and Fn2 are observed.- Zero-correlation linear hulls can not lead to integral distinguishers for some cases over Fp, while this is always possible over Fn2proven by Sun et al..- When the newly established links are applied to GMiMC, its impossible differential, zero-correlation linear hull and integral distinguishers can be increased by up to 3 rounds for most of the cases, and even to an arbitrary number of rounds for some special and limited cases, which only appeared in Fp. It should be noted that all these distinguishers do not invalidate GMiMC’s security claims.The development of the theories over Fp behind these links, and properties identified (be it similar or different) will bring clearer and easier understanding of security of primitives in this emerging Fp field, which we believe will provide useful guides for future cryptanalysis and design.
Design of Log Analysis System Based on Multi-Object Clustering Algorithm

Abstract With the rapid development of the information age and the continuous expansion of data, all walks of life have begun to collect and analyze massive amounts of data to extract information that is valuable to them. Clustering technology in data mining is an important means of data analysis. Because different data sets have different distribution characteristics, traditional single-object clustering cannot adapt to the effective processing of different data sets, so multi-object clustering has gradually become a research hotspot. The purpose of this paper is the design of log analysis system based on a multi-objective clustering algorithm. This article first determines the main goals of the log analysis system and performs a detailed analysis of the system’s demanding functional analysis and demanding non-functionality respectively. The log analysis system is mainly divided into five modules for the detailed design of the log analysis system. After the system design is completed, various functional modules are completed through corresponding technologies, the entire log system is tested for attacks, and the system is further improved. Finally, the system can completely analyze the attack type through log analysis and carry out relevant early warnings to meet the needs of users. When the data set is 1.8MB, the convergence time of the K-medoid algorithm is 3678.49, and the convergence time of the algorithm in this paper is 2536.42. When the data reaches 165MB, the convergence time of the algorithm in this paper is 4326.28, and the convergence time of the K-medoid algorithm is 8184. This shows that as the scale of data continues to increase, the processing time difference changes and the processing speed of this algorithm has obvious advantages.
Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment

Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.
Remote Technical Support for Localization Development of Confucius Institutes in Southeast Asia Based on Online Screen Sharing Cloud Algorithm

Based on the "Intelligent Multimedia Distance Learning System" developed by the department, this paper presents a new image block recognition algorithm and intelligent cloud HD sharing strategy, explores the development history of Confucius Institutes in Southeast Asia, and applies distributed storage and service-based reconstruction., data push and other technologies, realize the rapid write-back and extraction of data, and the flexible configuration of server nodes.By improving the teaching mechanism of the adult education function of the Confucius Institute, the technical support route for the localized development of the Confucius Institute in Southeast Asia is obtained.On this basis, the framework and research content of this study are determined, and the status quo of the adult education function of Confucius Institutes is investigated through case interviews.
Impact of Internet Use on Trade Openness: Evidence from Southeast Asian Countries

This research aims to examine the impact of Internet use on trade openness in Southeast Asian countries, with eight countries over the period of 1996-2020 as its samples. This study analyzed the data provided by World Bank using regression methods on random-effects, fixed-effects, and Driscoll-Kraay standard errors models. The research results show that the growth of the Internet positively affects trade openness in Southeast Asian countries. In addition, the result presents a positive relationship between trade openness with financial development, economic development, and urban growth rate. This study contributes to enhancing knowledge about the relationship between Internet usage and trade openness which is significant to the economic growth in several Southeast Asian countries
A Semantics-preserving Approach for Extracting RDF Knowledge from Object-oriented Databases

The Resource Description Framework and RDF Schema recommended by the World Wide Web Consortium provide a flexible model for semantically representing information about resources on the Web, which are playing an increasingly important role in intelligent processing of large-scale data. With the widespread acceptance and applications of RDF(S), construction of RDF(S) is of increasing importance. Automatic construction of RDF(S) with diverse data has attracted more attention. In this paper, we propose a novel approach for constructing an RDF(S) with object-oriented databases that are suitable for non-traditional applications. We propose the formal rules of mapping an object-oriented database model into a RDF(S) model based on the formal definitions of these two models. We develop a tool named OODB2RDF to verify our approach.
A reliable anchor regenerative-based transformer model for x-small and dense objects recognition

The past decade has witnessed significant progress in detecting objects by using enormous features of deep learning models. But, most of the existing models are unable to detect x-small and dense objects, due to the futility of feature extraction, and substantial misalignments between anchor boxes and axis-aligned convolution features, which leads to the discrepancy between the categorization score and positioning accuracy. This paper introduces an anchor regenerative-based transformer module in a feature refinement network to solve this problem. The anchor-regenerative module can generate anchor scales based on the semantic statistics of the objects present in the image, which avoids the inconsistency between the anchor boxes and axis-aligned convolution features. Whereas, the Multi-Head-Self-Attention (MHSA) based transformer module extracts the in-depth information from the feature maps based on the query, key, and value parameter information. This proposed model is experimentally verified on the VisDrone, VOC, and SKU-110K datasets. This model generates different anchor scales for these three datasets and achieves higher mAP, precision, and recall values on three datasets. These tested results prove that the suggested model has outstanding achievements compared with existing models in detecting x-small objects as well as dense objects. Finally, we evaluated the performance of these three datasets by using accuracy, kappa coefficient, and ROC metrics. These evaluated metrics demonstrate that our model is a good fit for VOC, and SKU-110K datasets.
Modern Distance Teacher Training in Narrowing the Gap on the Dual Structure Basic Education

With the development of technology, distance education has become an important tool to achieve educational equity.This paper aims to analyze the impact mechanism of modern distance teacher training on narrowing the gap between urban and rural education of China, and put forward a new path to improve the basic education.The research takes the China Education Panel Survey (CEPS) as the data source for analysis and find that the gap between urban and rural basic education does exist, but the academic achievement gap reflected by students at different quantiles is different; Distance teacher training is conducive to improving urban and rural students with different academic performance levels, and it has a higher promotion effect on students with better academic performance in rural areas; Different distance teacher training methods have different effects on students' academic performance, and the effects on mathematics and English subjects are more significant than those on Chinese subjects.Finally, this paper put forward some suggestions for the improvement of modern distance teacher training from the aspects of organization and management, training content, so as to provide a valuable reference for the balanced development of urban and rural education.
Pengaruh Strategi AMBT Terhadap Kemampuan Membaca Pemahaman Siswa Pada Mata Pelajaran Bahasa Indonesia

: This study aims to examine the effect of the AMBT strategy on students' reading comprehension skills in Indonesian subjects. This study uses a quantitative method of Quasi Experimental Design which is designed using a non-equivalent control group design. The subjects in this study were fourth grade students, totaling 40 students. Class IV A as the experimental group with a total of 20 students, and class IV B as a control group with a total of 20 students. Collecting data and instruments in this study used tests given before and after being given treatment. Data processing was carried out using a t-test with the help of Microsoft Excel 2016. The results of this study before being given treatment (pretest) showed the average reading comprehension ability of students was 61.62 and after being given treatment using the AMBT strategy, the average value was 80. .95. Based on the statistical results obtained tcount 2.874 and ttable 2.0373. Thus, it can be concluded that there is an effect of the AMBT strategy on students' reading comprehension skills in Indonesian class IV subjects at SDN 2 Banyuresmi.
DREAMSCAPES HOTEL MANAGEMENT SYSTEM Using HTML, CSS, JAVASCRIPT And PHP

Now a days every people are any system is searching in the online mode .In this project, on "Hotel Management System", we have tried to show how the Data/information in hotels is managed.This has been achieved by dividing the project into various modules.Customer is provided with different services like checking in, checking out, and editing entries or can be advance payments etc.If the customer wants he/she can cancel his/her booking.Enquiry about any customer or employee can be made either by customer Id or customer name.Enquiry about rooms available can also be made.Our project also includes the module for employee information.It will generate reports for customer, employees and Bill for customer is generated when the customer will check out from the hotel.We have included only few modules, as our purpose is to only have the idea or to study about how the management is done in hotels.By adding many more modules this type of project can have scope in various hotels.A lot of paperwork is generated and it is difficult for a supervisor to go through all these documents.Allocation of rooms based on expected vacancies is also difficult as it requires extra work on the part of the employees.
Feature Based Opinion Mining A Study

The continuous proliferation of user-generated material in the internet environment necessitates the development of tools to assist in making sense of the information.Despite a plethora of study on online customer evaluations, there is still a high demand for research to enhance feature extraction approaches.To that purpose, we presented extraction techniques based on extensive review feature classification.The suggested approaches not only detect new features but also filter out irrelevant features by taking into consideration the properties and patterns of different types of features.Experiment findings show that our proposed methods outperform state-of-the-art procedures.
A Voice-Activated, Deep-Learning-Based Emergency Alarm for Arabic Dialects

In this report, we will talk in almost every detail about our project, in order to cover most aspects of the project. We tried as much as possible to make the report suitable for two segments, the specialists and the public so that the reader can make the most of the report. We will talk about the hardware and software used in the project and how they work, in order to explain to the ordinary reader how some technical matters are done and to explain to the specialist some things that he or she might consider while developing. In our project, samples are the cornerstone of the project, and for this, we have focused on them in the report, from the methods of identifying samples to access to taking samples and analyzing them project to other dimensions. When we wrote the project, we took into account that development on the project is an important part of this academic process, so we paved the way for those who want to develop by collecting our own database in Arabic for scientific purposes so that development on it becomes easier and more productive.
All-fiber photometry based on a novel, low-autofluorescence wideband multimode circulator

Here, we present a wideband multimode circulator optimized for high transfer and transmittance of light, and low-autofluorescence. It is suitable for fiber photometry and optogenetics, allowing the inference of brain activity in awake mouse.
Perbandingan Metode Cost Sensitive pada Decision Tree dan NaÃ¯ve Bayes untuk Klasifikasi Data Multiclass

Abstrakâ€“ Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collection. Besides, the cost-sensitive decision tree C4.5Â and the cost-sensitive naive Bayes method were used in the research. On the glass, lympografi, vehicle, thyroid, and wine datasets, the accuracy of the test results was 72.34 %, 68.22 %, 75.68 %, 93.82 %, and 93.95 %, respectively, using the cost-sensitive decision tree C4.5. While the cost-sensitive naive Bayes method outperforms the others by 32.24 %, 82.61 %, 25.53 %, 97.67 %, and 94.94 % on the dataset, respectively.
Investigación del comportamiento de la información en América del Norte

This paper summarizes the history and contributions of information behaviour scholars in North America.Scholars who made foundational contributions to the field, as well as newer scholars, are highlighted.The paper also notes the roles played by North American scholars in important venues for information behaviour research, such as SIG USE in the Association for Information Science and Technology, and the Information Seeking in Context conference.
DTAIS: Distributed Trusted Active Identity Resolution Systems for the Industrial Internet

In recent years, the Industrial Internet and Industry 4.0 came into being. With the development of modern industrial intelligent manufacturing technology, digital twins, Web3 and many other digital entity applications are also proposed. These applications apply architectures such as distributed learning, resource sharing, and arithmetic trading, which make high demands on identity authentication, asset authentication, resource addressing, and service location. Therefore, an efficient, secure, and trustworthy Industrial Internet identity resolution system is needed. However, most of the traditional identity resolution systems follow DNS architecture or tree structure, which has the risk of a single point of failure and DDoS attack. And they cannot guarantee the security and privacy of digital identity, personal assets, and device information. So we consider a decentralized approach for identity management, identity authentication, and asset verification. In this paper, we propose a distributed trusted active identity resolution system based on the inter-planetary file system (IPFS) and non-fungible token (NFT), which can provide distributed identity resolution services. And we have designed the system architecture, identity service process, load balancing strategy and smart contract service. In addition, we use Jmeter to verify the performance of the system, and the results show that the system has good high concurrent performance and robustness.
Research on Neural Network Prediction System in Loose-leaf Textbook Course Assessment

The unique advantages of flexible and novel loose-leaf textbooks have become a new development direction, but the diversified assessment system of loose-leaf textbooks is still relatively imperfect, and the application of traditional curriculum assessment systems in loose-leaf textbooks is subject to certain constraints.Therefore, in order to improve the diversified assessment system of loose-leaf textbooks, this paper proposes a machine learning algorithm applied to the loose-leaf textbook assessment system.By comparing various neural network algorithms, a prediction model suitable for the diversified assessment system was found.The results show that the application of SVM radial basis function neural network in the assessment and prediction system for loose-leaf courses has high prediction accuracy and small error, can effectively play a prediction and reminder role, improve the diversified assessment system, and promote the application and promotion of loose-leaf textbooks.
Transformation of the Role of China’s Foreign Language Teachers in the Internet Era

Compared to foreign language education in other countries, China's foreign language education is a vast and complex project with a short history.What is even more impressive is that in the past half century, the country's higher foreign language education has a huge scale and demonstrated a level of prosperity that has been admired by the education community around the world.However, the problems and challenges it poses are also not obvious to everyone.At present, higher foreign language education cannot keep up with the pace of global integration and information society development at various levels.College foreign language teachers, as implementers of reversing the dilemma of foreign language education in universities, should strive to enrich their professional content in practical teaching work, and also need to have a keen insight into new trends in foreign language teaching and extraordinary courage to explore new teaching methods and try advanced teaching methodologies.In the context of new social needs, the teachers should complete a new transformation of one's own role and a new determination of positioning.
Research on the Mixed Teaching Mode of Business English Writing Based on SPOC

With the rapid development of the Internet, SPOC teaching mode, with its high flexibility, convenience, freedom, "low threshold" and "high quality" characteristics, has gradually become a trend of business English teaching reform in colleges and universities, and has broad application prospects in business English writing teaching.This paper will discuss the mixed teaching mode of business English writing based on SPOC.