Reviewerly/exHarmony huggingface dataset:
Modulation Classification of Jamming Signal Based on Separation and Spectrum Feature

For the problem of classification for jamming signal in the normal communication channel, a new modulation identification method is proposed. With the help of JADE algorithm, the jamming signal is separated from the normal communication signal. Four new features is extracted from the jamming signal power spectrum and used for modulation identification. The method is robust in the presence of carrier offsets. For 2ASK, 2FSK, 4FSK, BPSK and QPSK, the algorithm is simulated under several received conditions. Simulation results show the validity of the proposed method for jamming signals with different spectrum.
An Exploratory Study of Project Activity Changepoints in Open Source Software Evolution

To explore the prevalence of abrupt changes (changepoints) in open source project activity, we assembled a dataset of 8,919 projects from the World of Code. Projects were selected based on age, number of commits, and number of authors. Using the nonparametric PELT algorithm, we identified changepoints in project activity time series, finding that more than 90% of projects had between one and six changepoints. Increases and decreases in project activity occurred with roughly equal frequency. While most changes are relatively small, on the order of a few authors or few dozen commits per month, there were long tails of much larger project activity changes. In future work, we plan to focus on larger changes to search for common open source lifecycle patterns as well as common responses to external events.
Application Research of Surface Controller Based on STM Series Well Logging

Surface controller is a very important part of logging system, Mainly complete logging signal processing and transmission, Logging signal processing mainly refers to decoding and processing the uploaded encoded logging signal, Signal transmission refers to uploading original logging data to the software of the supreme computer and receiving and issuing commands through CAN bus and Ethernet, A surface controller is used to monitor downhole production conditions in real time. In this paper, the surface controller is divided into two modules of logging signal decoding and transmission to illustrate the working principle of the controller. The whole system takes STM series embedded chip as the core to build peripheral circuit, The decoding module takes STM103 as the core processing unit, plus the signal conditioning circuit, The transmission module takes STM429 as the core processing unit, Peripheral circuits are mainly memory chips, CAN buses, Ethernet circuit and so on, Complete the signal packaging and upload to the top computer for data processing.
Studying a Face Search Method Based On the Idea of Sparse Data Representation by Generating Random Points

The article discusses a method for extracting features from digital images, based on the idea of a sparse representation of data by generating random points on images with a face.The article shows the connection of this method with the stochastic Monte Carlo algorithm. Experiments results on the selection of system parameters to increase recognition accuracy are reflected in the article.The conducted research can be used in the development of real recognition systems based on brightness values pixel values as features.
Survey: Research on Blockchain Consensus Mechanism in IoT Security

The Internet of Things (IoT) technology, as an important part of a new generation of information technology, is beginning to be applied in more and more fields. However, due to the lack of basic security technology, the Internet of Things is facing a severe information security situation. Blockchain technology, as a new solution that can realize decentralized collaboration in an untrusted network, has a natural fit with IoT, which is inherently distributed. The performance of the blockchain largely depends on the consensus mechanism, but the current research rarely discusses IoT security from the perspective of the consensus mechanism. In this article, we comprehensively analyzed the impact of the consensus mechanism on IoT security. First, we briefly explained the blockchain technology. Next, we analyzed and discussed typical consensus mechanisms. Then, we proposed to classify current IoT security issues from three perspectives: data security, communication security, and application security. Next, we discussed the consensus mechanism of single-chain and multi-chain in IoT. Finally, we analyzed the problems of blockchain consensus in IoT security. In response to the security challenges of IoT, it is proposed to look forward to the optimization of consensus from three perspectives: the optimization of blockchain node evaluation, the optimization of blockchain architecture, and the optimization of blockchain storage. We hope to contribute to the future development and innovation of blockchain in IoT.
Taking into account the individual characteristics of students in teaching mathematics

In this article, the goal of education is not the achievement of certain knowledge, skills and abilities for students, but the provision of his individual development, attention to his individuality as a unique, unique education; not the education of the performer, but the formation of a creative personality. In this regard, it becomes necessary to take into account the individual characteristics of students in teaching, in particular, to take into account cognitive styles that reflect differences between people in the nature of perception and processing of information. One of the first steps in this direction was the strengthening of the role of differentiation in education.
Weightless Swarm Algorithm as Transformation Technique for Improving the Performance of Machine Learning Algorithms

Nowadays machine learning algorithms are applied to various fields but still there is a requirement for improving the accuracy of machine learning algorithms for certain applications. This paper focuses on using Weightless Swarm Algorithm as transformation technique for enhancing the accurateness of machine learning techniques in SONAR dataset classification. Three different machine learning techniques namely Random Forest, Stochastic Gradient Descent, and Decision Tree are examined as classifier for categorizing the SONAR data as either mineral or normal rock. Notably, Mathews correlation Coefficient of Random forest classifier is 75.27% and this is increased to 81.81% through the usage of Weightless Swarm Algorithm as transformation technique.
Joint Transfer Strategy for Cross-Domain Human Activity Recognition

Human activity recognition based on wearable sensors has been considered as an important work in the increasingly developed artificial intelligence and Internet of Things. Since acquiring enough activity labels is often expensive and time-consuming, an intuitive way is to leverage existing data from one sensor (source domain) to recognize target data from another sensor (target domain). Unfortunately, the data from different domains may have a large discrepancy. Existing approaches typically consider reducing the domain discrepancy to transfer knowledge. However, these approaches do not take full advantage of data structure, which is important for activity recognition. In this paper, we propose an effective method, named Joint Transfer Strategy (JTS). The method has high accuracy and robustness for cross-domain human activity recognition. Specifically, JTS first obtains pseudo labels for the target domain via a majority voting technique. Then, it leverages a joint transfer strategy to map the source and target domain data into a shared domain-invariant subspace and preserve the data structure information. Finally, an adaptive classifier is learned to label target data. Comprehensive experiments on three large public activity recognition datasets demonstrate that JTS outperforms other state-of-the-art methods in terms of classification accuracy.
Research on Cultivating New Teachers’ Literacy Based on Artificial Intelligence

With the development of science and technology, artificial intelligence began to integrate into all aspects of people's lives, and began to have an important impact on the lives of residents. The main characteristics of artificial intelligence include from artificial knowledge expression to big data-driven knowledge learning technology, from classified processing multimedia data to cross-media learning, from the pursuit of intelligent machines to high-level man-machine, brain-computer interaction and fusion, from focusing on individual intelligence to intelligent groups based on the Internet and big data, so teachers need to improve their professional literacy and core literacy through role remodeling in the era of artificial intelligence, and at the same time learn more professional knowledge and solid skills based on the nature and characteristics of education. In specific teaching and classrooms, the application of artificial intelligence should be gradually popularized. In the flexible human-computer interaction, the power of artificial intelligence technology to promote the growth of human life is maximized. It can provide students with a better and more suitable teaching path for individualized development. This article aims to explore the research on teacher literacy cultivation in the era of artificial intelligence as follows.
Cross-Domain Rating Prediction for Market Development

Most of the review-based prediction tasks are designed for an in-domain setting, in which a review score, sentiment, or sales in a domain is predicted by reviews in the same domain. In this paper, we address the rating prediction problem in a cross-domain setting, i.e. rating prediction of an item in a domain where no review has been given for the item yet, based on reviews provided in a different domain. For example, we predict the rating of a US product in the UK market, which has never been sold in the UK, in order to decide whether to start to sell the item in the UK. For effective cross-domain rating prediction, we propose a multitask learning model that predicts both the review rating and review contents simultaneously, and found that it was effective in two ecommerce datasets from Amazon and Yahoo. Furthermore, our experimental results revealed categories and cases in which the cross-domain rating prediction was successful.
Face Recognition using Self Organizing Map Based on Multi-Level Thresholding and Features Extraction

Abstract Facial recognition has played a very important role in security systems in recent decades. Various studies have been conducted to find a good method for this recognition to be effective. The facial recognition process in this study uses the self-organizing maps (SOM) method which is based on Multi-Level Thresholding segmentation and extraction of image features. The obtained face image will be segmented using Multi-Level Thresholding and the face will be generated without involving the background. The image that has been segmented will be calculated the values of texture feature extraction using GLCM and color feature extraction using HSV. The resulting image will be carried out by a training and testing process for facial recognition using the self-organizing map (SOM) method. The test results in the proposed study showed good results with an introduction rate of 78.44%. This percentage is higher than face recognition without a segmentation process with a recognition rate of 65.78%. This shows that the proposed method has better performance for the face recognition process without involving the segmentation process.
Application of Diagram Image Classification Based on PCBMER

Abstract Nowadays, in order to express data more clearly and accurately, data visualization has become a wave of the times. In this context, due to the different types and quantities of data stored in computer systems, different forms of data visualization can be seen in web pages, demonstration slides and papers. However, in many cases, we can’t directly obtain the underlying data, so the classification of chart images becomes very important. Only with the classification technology of chart images can we understand it more intuitively and deal with it more concretely, such as generating new charts according to the extracted data. Using depth learning technology to classify chart images is now a very popular way. In the field of image classification, convolution neural network (CNN) is the most widely used technology in depth learning, not only because of its high accuracy, but also because it encapsulates the process of feature extraction. At the same time, using the software architecture of PCBMER framework to design application programs can maintain high stability of the system. From the experimental results, this new technology is feasible, and the architecture can be further optimized to make the results more accurate.
Students’ perspectives on 3D-vision

Abstract The perception of our surroundings is determined by vision to a large extent. Human vision is not only able to generate a three-dimensional impression of three-dimensional objects, but also of two-dimensional objects. Nowadays, 3D-technologies even provide additional opportunities to trigger visual 3D-perception. 3D-vision provides a context for teaching and learning several topics in physics. In order to develop a learning environment for high school students, following the framework of educational reconstruction, students’ perspectives must be considered. As we could not find any documented students’ conceptions on 3D-vision, an exploratory study was conducted. Main aim of this study was to provide a broad overview on students’ perspectives on 3D-vision. Therefore, the questionnaire mainly consisted of open questions. The questionnaire was administered to Austrian secondary level students of two schools and was finally filled in completely by 215 students. Data was analysed by the means of qualitative content analysis and an inductive category system was developed. The results show that students mainly connect 3D-vision with 3D-technologies in cinemas. However, differences between junior and senior high school students were detected. While junior high school students mainly associate 3D-vision with cinemas, senior high school students additionally mention the physiological process of 3D-vision as well as 3D-technology.
Obstacle Avoidance using BVC for a Quadrotor

D. Zhou et al. proposed an algorithm based on the buffered Voronoi cells (BVC) for quadrotors to reach each given target position without mutual collision. However, in the method, it is assumed that all quadrotor positions are known and the control inputs for all quadrotors are calculated in a single computer. In this paper, we describe the method to compute the Voronoi diagram using only the obstacle information around a quadrotor in an unknown environment and to reach a given target position. The usability of this method is confirmed by simulation.
Attention-Based Encoder-Decoder Network For Single Image Dehazing

Single image dehazing is a challenging problem in computer vision due to it is highly ill-posed. Although recent research has made great progress, the dehazed images produced by existing models still have residual haze and lose too much detail information. To solve the above problems, we propose an end-to-end Attention-based Encoder-Decoder Network (AEDNet) which is capable to effectively remove haze while preserving image details well. AEDNet employs a novel channel shuffle attention mechanism to adaptively adjust the weight of each channel-wise feature. This attention mechanism is integrated in residual block which is the core feature extraction module of encoder-decoder. Extensive experiments on synthetic datasets and real-hazy images demonstrate that our AEDNet achieves better performance compared with previous state-of-the-art methods.
The eigenstrain-based method for the incremental hole-drilling technique

Using the incremental hole-drilling technique and eigenstrain concept, a new method is introduced to calculate the calibration coefficients of laminated composites. Based on the Saint-Venant principle, strain responses on the surface of the specimen rapidly become nonsensitive to the stresses in the depth of the sample. To overcome these limitations, the eigenstrain-based (ESB) method was used to estimate the constant eigenstrain field instead of the variable stress field. To evaluate the method, the procedure for the numerical calculation of calibration coefficients is explained. Then, simulations of macroscopic thermal stresses of [04/904]s and [08/908]T glass/epoxy composite specimens under a specified curing cycle are performed and the incremental hole-drilling is simulated. By applying the ESB method, the eigenstrains and consequently residual (thermal) stresses are estimated. In the last step, incremental hole-drilling experiments are performed and the calculated stresses by ESB and CLT are compared. The new proposed analytical-experimental method does not have the limitations of the CLT in the estimation of residual stresses such as the requirement of knowing cure and manufacturing conditions, thermal coefficients of material, and stress-free temperature. The proposed method, by having the mechanical properties of the unidirectional ply, can present a reasonable estimation of the residual stresses.
LegalBERT-th: Development of Legal Q&amp;A Dataset and Automatic Question Tagging

Tagging questions according to their topics is useful for internet forum management. In this paper, we use the Bidirectional Encoder Representations from Transformers (BERT) model to categorize posts from Thai legal internet forums. First, We construct our new legal Q&A dataset by scraping the internet, cleaning the data, and annotating the data. Second, We perform transfer learning to let our model learn about the legal language model in general and then fine-tune the model for the law topic classification task. As a result, we have developed a legal Q&A dataset of 12,695 question/answer pairs and a law topic classification model based on BERT with 92% accuracy. Finally, we build a prototype legal internet forum which equipped with the automatic tagging function, law topic classification, to provide a concrete example of how to apply the model in the real situation.
System Modeling by Ultra High Frequency Sigmoid and Sine Artificial Higher Order Neural Networks

New open box and nonlinear system model of Ultra High Frequency Sigmoid and Sine Artificial Higher Order Neural Network (UGS-HONN) is presented in this paper. A new learning algorithm for UGS-HONN is also developed from this study. A time series data modeling system, UGS-HONN Simulator, is built based on the UGS-HONN system models too. Test results show that average error of UGS-HONN system models are closing to zero (10-6). The average errors of Polynomial Higher Order Neural Network (PHONN), Trigonometric Higher Order Neural Network (THONN), and Sigmoid polynomial Higher Order Neural Network (SPHONN) models are from 2.8128% to 4.9076%. It means that UGS-HONN system models are 2.8128% to 4.9076% better than PHONN, THONN and SPHONN models.
Demo Paper: Atomic Bonded Cross-chain Debt

Atomic Bonded Cross-chain Debt (ABCD) is the first non-custodial smart-contract-independent cross-chain atomic bond. Theoretical aspects of ABCD have been presented in the International Conference on Blockchain Technology and Applications (ICBTA) and won the best presentation award. It is the first time a demo of Atomic Bonded Cross-chain Debt is presented.
Deep Learning Chatbot with Feedback

We have proposed a Chatbot using seq 2seq model with attention, for an open domain dialog system. Improved our chatbot by giving feedback to those responses which are incorrect during the conversation. This feedback is like a reward for our chatbot which is trained in an online fashion. Unlike traditional approaches which are trained from beginning with new sets of data and after then, it is deployed. In quantitative terms, we achieved the bleu scores of 0.31 compared to the state-of-the-art whereas in qualitative terms we have improved the responses during online feedback learning for incorrect responses.
An Android-based COVID Case Monitoring Application Using a Geographic Information System to Educate the Public to be More Aware of COVID-19 Spreads

Abstract The purpose of this study is to monitor the spread of Covid-19 cases. The monitoring process takes advantage of geolocation to detect the level of the spread of Covid in an area. Geographical information system utilization is used for mapping areas that have Covid cases. To notify users of the use of geolocation sensors, it is used to notify system users within a certain radius that the area entered is in an active zone for the spread of Covid-19. To carry out the tracing and tracking process, each Covid-19 patient will be reported to the system by recording their telephone number. Therefore, the patient’s movement will be detected whether he complies with the protocol by isolating independently or violating the protocol by exiting the quarantine area. Geolocation sensors and telephone number recording will involve a smartphone device so that the system built will be mobile-based. The software development method used the waterfall method, which consists of requirements, design, implementation, verification, and maintenance. This study is a product design prototype application for monitoring mobile phone-based covid cases using a geographic information system approach.
A Constructive Logic with Classical Proofs and Refutations

We study a conservative extension of classical propositional logic distinguishing between four modes of statement: a proposition may be affirmed or denied, and it may be strong or classical. Proofs of strong propositions must be constructive in some sense, whereas proofs of classical propositions proceed by contradiction. The system, in natural deduction style, is shown to be sound and complete with respect to a Kripke semantics. We develop the system from the perspective of the propositions-as-types correspondence by deriving a term assignment system with confluent reduction. The proof of strong normalization relies on a translation to System F with Mendler-style recursion.
Towards the smart use of embedding and instance features for property matching

Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes) . However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings.Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME.