Reviewerly/exHarmony huggingface dataset:
Airborne Sensor and Perception Management

Despite algorithmic advances in the field of image processing as well as modern computer architectures, automated sensor data processing that guarantees reliable and robust information retrieval in dynamic environmental and varying flight conditions is still a challenging task within unmanned surveillance and reconnaissance missions. In our paper we will elaborate the reasons and propose a promising way out by adapting to variable environmental conditions and states of the UAS platform in terms of the dedicated usage of specialized sensor data processing chains. However, these specialized chains must be used within their operation space. Otherwise, their performance in terms of detection precision and recall will degrade. To overcome this drawback, we propose to apply chain performance models based on Bayesian Networks (BNs). The evaluation of the BNs takes place during the flight depending on environmental influences. Accordingly, a performance probability can be predicted for each chain, which is used for an automatic chain selection. We validate our approach within a real flight Search and Rescue scenario (SAR). To compare generalized and specialized chains, we conducted several flight experiments with an EO/IR mission sensor setup: a) to annotated and preselect/filter training-datasets, used for transfer-learning of the Convolutional Neural Networks (CNNs), and b) to derive test datasets in different environmental situations and with varying platform/sensor states. The sensor data comprises variations in illumination and meteorological conditions, photographic conditions (e.g., different sensor elevation angles and ground sample distances) as well as topographic conditions. We provide a comprehensive insight into the detection results derived. Based on these results we conclude that a targeted use of specialized CNNs can outperform generalized CNNs.
Abstractive Text Summarization with Fine-Tuned Transformer

In this paper, we investigate an encoder–decoder-based on the bi- directional transformers with a self-attention function to generate abstractive text summaries for an input document. In the proposed approach, we fine-tune the transformer by changing the activation function from the rectified linear activation unit (ReLU) to the parametric rectified linear activation unit (PReLU). By introducing the PReLU activation function, we can store the long-term dependencies from the input document by reducing the data loss which was more when we used the ReLU function. Apart from that, we introduce a self-attention function for keeping track of the important keywords and the out-of-vocabulary words presented in the input document. We employ CNN/DailyMail dataset, Inshorts dataset, and customized IndiaToday data to achieve abstractive text summarization. The proposed model achieves a better ROUGE score when compared with the sequence-to-sequence with long short-term memory network and the traditional transformers model.
A Study of OSMU for Henan Seolheon’s Works

The purpose of this paper was to study the contents of the work theory of the famous Korean ancient female writer Henan Seol-heon. When introducing classical works or classical poets to modern people, they are looking for content such as more fun and easier to receive, and storytelling. When referring to the keywords of classical literature and female poets, the name Henan Seol-heon appears a lot. It needs to know the works of female poets who have a great position in classical literature. During the Joseon Dynasty, when Heo Nan-seol-heon lived, most of the creators of literary works were male writers, claiming Confucian ideas. Due to Confucianism, women of that era were restricted in various fields such as status, recognition, freedom, and study. In this paper, we investigate the contents using the current works of Heo Nan-seol-heon and investigate the big direction of how to promote Heo Nan-seol-heon and how to proceed with the contents. In this era, almost everything is related to the database, but classical works seem to be difficult to relate to the database. In addition, it seems that to achieve this, we must support technology on many levels.
Configuration Method of Safety Link of Electric Power Communication Network

This paper studies a power communication link establishment method, resource allocation method and system. The power communication link establishment method includes: sending a configuration request to the configuration server until the configuration server determines that the sender meets the access conditions of the communication link to be accessed according to the security requirement list and the received configuration request, and the sender establishes a connection with the communication link to be accessed. By using this technology, the power service terminal can select the corresponding communication link according to the security preference, and send the configuration request to the configuration server; The configuration server then reversely determines whether the power business terminal meets the access conditions according to the security requirements list, and finally enables the power business terminal to access the communication link that meets its own security needs and preferences, meeting the differentiated security requirements of the power business terminal for the communication link resources.
English Online Learning System Based on Web

It is an inevitable trend of modern education to improve students’ learning efficiency, promote the reform of students’ learning methods and develop their autonomous learning ability. With the development of Internet and web technology and the deepening of its application in education, the learner centered online learning model provides a broad development space for students’ autonomous learning, and has become an important way and development direction of modern education. This web-based English online learning system is a free and interactive English teaching tool, which provides learners with real-time opportunities to practice English. Learners can interact with native speakers of the target language through video and audio dialogues. The project, developed by the University of California, Berkeley, has been used by thousands of students around the world. Provide learners with a variety of real dialogues and activities aimed at teaching them how to use simple everyday expressions in dialogues. The software allows users to choose different types of courses: listening, reading or writing exercises.
Application of Virtual Simulation Technology in Traditional Handicraft Protection

The protection of traditional handicraft industry is an important part of national industry. Handicrafts are handmade and require a lot of labor and time. In addition to the high cost, it also has some problems, such as: 1) non-standard handicrafts; 2) Handicrafts cannot be mass produced; 3) Handicrafts cannot meet the requirements of international trade. The protection of traditional handicrafts is mainly based on the inspection and classification of rival handicrafts. The detection rate of fake and inferior products in this field is very low, which is difficult to effectively protect the national economy. Therefore, it is necessary to use virtual simulation technology for product quality control. It can be used as a new tool to detect counterfeit products at any time, especially when there is no qualified inspector or there is a shortage of inspectors. It also provides an effective means to prevent counterfeit products from entering the circulation and selling in retail stores without being discovered by inspectors. Therefore, this paper studies the protection of traditional handicrafts based on virtual simulation technology.
Application of Data Mining Algorithm in Computer Mathematical Modeling

The application of data mining algorithm in computer mathematical modeling is the process of applying data mining algorithm to problems that can be modeled by mathematical models. The application of data mining algorithm in mathematical model problems is called “data mining application” or “data mining implementation”. The application of data mining algorithm in computer mathematical modeling is the process of using data mining algorithm to transform, summarize and model information. This is an important aspect of data analysis, because it enables us to extract useful knowledge from a large amount of data. The goal here is not only to discover patterns, but also to determine what can be done once these patterns are discovered. Data mining applications are used to solve many practical problems that do not have direct access to basic mathematics, such as automated decision-making processes (e.g., recommendation engines), generating new hypotheses based on existing hypotheses (e.g., hypothesis testing), and improving existing theories and models (e.g., improving classification).
Evaluation Data of Poor College Students Based on Improved Apriori Algorithm

In recent years, with the continuous expansion of college enrollment in China, more and more students have the opportunity to enter the university. However, due to the reform of the charging system in colleges and universities, the number of poor students in colleges and universities is increasing. In this worrying situation, our government and universities have taken a series of measures and made many achievements. In this study, we use the Apriori algorithm to extract evaluation data from poor college students’ compositions. The purpose of this study is to examine how poor college students’ writing performance is evaluated by taking their compositions as samples. We also investigated whether there were differences between male and female students in assessing their own performance. The results show that, compared with other college students, Apriori algorithm can effectively evaluate the paper quality of students whose academic ability is at least at the middle level. This is especially useful for papers with poor evaluation ability.
The Application of Ink Animation Based on Artificial Neural Network Style Transfer

From the practical perspective of ink animation art, this paper aims to study the specific ways of artificial intelligence technology to intervene in the field of ink animation creation. Through the function introduction, model optimization and parameter adjustment of the image style migration algorithm, the creation idea of the animation image style migration for the light dormitory ink effect was explored, and we conducted detailed experiments. The verification results show that the method proposed in this paper can effectively meet the creation needs of ink animation, and provide technical support and thinking innovation for the ink animation creation in the era of artificial intelligence.
Immersive Technology-Based Guidance Module for Induction Motor Diagnosing

One of the main objectives of virtual reality (VR) is to simplify human resource interactions with equipment once a process is in operation, especially in the case of techniques that are related and whose devices communicate with each other. VR allows operators to become familiar with the production environment and the process in a tangible way during training, making them aware of the context where they will be working, both the physical space and the contingencies that may arise. This paper presents an immersive training module for detecting and correcting faults in induction motors. Two groups were considered for testing the effectiveness of the developed system: a control group and an experimental group. The tests showed that with this technology, the teaching-learning process time is optimized by more than 50%. In addition, with a p-value lower than the selected alpha level, it was confirmed that the use of VR, in this case, is more efficient than the use of conventional training methodologies. Finally, the usability of the proposal was evaluated, concluding that, although the system has several areas for improvement, it is beneficial for the industry.
Machine Learning Assisted MPU6050-Based Road Anomaly Detection

Maintaining the quality of roadways is a major challenge.Maintenance work of roads is although necessary but can lead to traffic jams, causing frustration among road users, many a time leads to unwanted consequences. In order to avoid problems related with road anomalies, system has been proposed that can detect road anomalies using a hardware setup. This system that is based on data collected by the hardware setup, automatically detects and classifies road anomalies. Major focus is on the methodology and the process that has been used for the data collection, and discuss some problems and challenges resulting from the above-described process. From a traffic operation perspective, establishing a reliable transportation system could benefit both system planners and the end user. Developing an effective road anomaly detection system will surely improve the viability of autonomous vehicles and will also contribute toward the reduction of road anomalies related accidents. Out of different machine learning methods that has been used to train and test the model, support vector machine (SVM) has shown as shown best accuracy of 95.2% on the self-generated dataset.
Simulation: The Great Enabler?

Autonomous systems (AxS) have successfully been employed in military tasks such as search and rescue, logistics, and reconnaissance. Whether it be on land, at sea, or in the air, diverse and representative training samples are indispensable for operationalising recent advances in artificial intelligence (AI). Applying deep learning to the task of armoured fighting vehicle (AFV) recognition, we examine the role synthetic data are capable of playing in training image classification and object detection models. To this end we implement a modular pipeline for the controlled generation of synthetic samples and their combination with real data with downstream building blocks for data augmentation and adversarial machine learning. This lets us conduct well-structured experiments (e.g., involving varying lighting conditions or adversarial perturbations) and develop hypotheses regarding the most beneficial composition of the training data set and the influence of individual pipeline building blocks on performance or robustness. In order to bridge the simulation-to-reality gap we use data augmentation techniques akin to domain randomisation. In particular, we fuse images with fractal pat-terns, which, in their structural complexity, resemble many forms of military camouflage. Anticipating adversarial attacks on our computer vision systems, we also train a set of more robust models by means of adversarial training, a well-studied defensive measure. Our experiments follow a rigorous evaluation protocol accounting for the multidimensional nature of both performance and robustness. We envision future applications of our thorough approach to training AxS beyond AFV recognition – in all dimensions of modern battlespace.
Traffic Analysis on Videos Using Deep Learning Techniques

With the enormous increase in the number of vehicles that are making use of roadways day by day, traffic congestion is one of the significant issues that are being observed. This problem can be addressed with proper traffic regulation. In this paper, proposing an automated system that will perform traffic analysis from the traffic videos which were captured from static cameras. This traffic analysis will be performed in three stages: vehicle detection, vehicle classification, into ten major categories, followed by vehicle counting under each category. The proposed work adopting the background subtraction method and vehicle classification by using a pre-trained model VGG16 as well as logistic regression (LR). Observations were made across several models ranging from the models that were built from scratch to pre-trained models as well as VGG16 + logistic regression. Experimental results show that the proposed model provides top-1 accuracy of 84.32% and top-5 accuracy of 99.77%.
Information Retrieval Using Data Mining

The expansion of information processing has generated huge quantity of data bank and large data in different domain. Free Conjoin is modify in fact extract organization since it can get any section of matter stored in data collection by more flaccidity and in well-organized manner. Therefore, the fact extract organization can be categorize according to the kinds of data collection. Data mining is faced with some major issues like information retrieval, similarities and differences in spatial data mining techniques, and mining of data clusters. To learn about data mining, a review process involving four stage approaches has been undertaken for 40 research papers which were published in the period of year 2001 to year 2014. After an exhaustive review process, seven key issues were found “Information retrieval, Similarities and differences in spatial data mining techniques, Mining of data clusters, changing in land price, data load balancing, privacy protection, and gap between academia and business tools.” problems have been resolved and explained through the use of appropriate approaches. Various research have found a number of approaches to solve the problem. In this article, the review's conclusions are given in the form of multiple findings. The results covered the algorithms and approaches utilized to tackle a specific study topic, as well as their strengths and drawbacks, as well as the scope for future research in the field.
Automatic Extraction of Metadata Based on Natural Language Processing for Research Documents in Institutional Repositories

The Institutional Repositories allow to organize, preserve, and disseminate scientific and academic production of an institution. Metadata are data about other data, which help identify, recover, and preserve the information of the research documents produced by the Universidad Nacional del Altiplano. The extraction of metadata is a time-consuming process in the treatment of research documents, and there may be a risk of introducing erroneous information. The purpose of this research is to optimize the extraction of metadata on 380 research documents, which is a necessary process for their preservation, Therefore, an algorithm was developed to allow the automatic extraction of metadata using natural language processing (NLP) techniques for Institutional Repositories (E-MeRI), to which the linear algorithmic complexity O(n) was determined, and at the same time, the level of accuracy was determined between 96% and 99% of correct results based on the metrics Precision and Recall. Finally (E-MeRI) proved to be efficient compared to other extractor tools with 50 test documents.
Design of English Teaching Resource Management System Based on Collaborative Recommendation

With the development of modern network and the fast and convenient characteristics based on network, the construction of network education resources has become the focus of information construction in Colleges and universities. However, due to the lack of unified resource description standards and construction standards, most of the resource database construction is still in an isolated and decentralized state, and it is difficult to achieve sharing and reuse. Therefore, by deeply analyzing the metadata standard of educational resources, studying XML and its related technologies, the author has carried out the “design of English teaching resource management system based on collaborative recommendation”, which is a tool for managing English teacher resources. It can be used by teachers, students and school management departments. The main function of the software is to ensure that all resources are in place before the start of the course and to track their status after each course. Detrsc also allows teachers to create different types of reports according to their needs: from a simple list containing names and descriptions to more complex reports, such as activity reports or performance reports.
FCGSM: Fast Conjugate Gradient Sign Method for Adversarial Attack on Image Classification

Deep neural network is sensitive to adversarial samples that crafted by adding imperceptible perturbations to original images, and many methods of generating adversarial samples have emerged. Although existing methods based on gradient direction have good attack performance, some ill-conditioned issues may reduce their performance on occasion. In this paper, we propose a novel attack method based on three-terms conjugate gradient direction, which is effectively for improving this limitation, and its is named as fast conjugate gradient sign method (FCGSM). The proposed method FCGSM can jump from the local maximum during the process of finding the maximum value of loss function, thus generating more adversarial samples than the SOTA methods APGD and ACG. Experiments conducted on two benchmark datasets show that the FCGSM works well in attacking deep neural network-based classification models.
Autonomous Navigation for the Movement of the Robot in the Tube

The paper deals with the issue of autonomous navigation for the movement of a robot in a tube. The solution is to determine the center of a given conic section using three operations in the Geometric Algebra for Conic Sections (GAC). The article describes an engine that renders given conic sections through basic operations in the GAC. At the end of the work, an algorithm is described that calculates the axis of the tube based on the points that are located in the space from the image, where we place the center of the ellipse obtained by the image filter and fitting algorithm.
Study of Various Word Vectors for Sentiment Analysis

With recent advancement in web 2.0, people are expressed their comments through online platforms like social media and even in e-commerce website also. So, text comments are generated more on this medium. Deep learning algorithms are employed to analyze the sentiment accurately by understanding the language. The prediction of stock, predicting public poll, analysis of e-commerce comments, and so on are used this technique. In this work, we have employed and compared the various feature embeddings with simple neural network algorithm which improves the model to utilize words’ relationship. The feature representations are applied on LSTM model. The distributed memory model (DMM) representation achieves excellent result on the customer review dataset.
A Development Model of University Enterprise Joint Laboratory in School of Cryptography

University-enterprise joint cryptographic laboratory is an important measure to promote Hainan University to build a first-class university. We have the following three selection mechanisms to promote it after talking about. It is mainly realized in the following three directions: first, enterprises send people to teach technology, and enterprises can obtain talents in a directional manner. Second, different enterprises bid, guided by the technical problems of enterprises, to promote the growth of students' technical ability, corresponding to the graduation preferential treatment of enterprises. Third, the competition within the school to promote the growth and progress of students' technology. Keywords: First Keyword, Second Keyword, Third Keyword.