Reviewerly/exHarmony huggingface dataset:
COLLIE: a broad-coverage ontology and lexicon of verbs in English

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.
A Comparative Study of Some Automatic Arabic Text Diacritization Systems

Arabic diacritization is the task of restoring diacritics or vowels for Arabic texts considering that they are mostly written without them. This task, when automated, shows better results for some natural language processing tasks; hence, it is necessary for the field of Arabic language processing. In this paper, we are going to present a comparative study of some automatic diacritization systems. One uses a variant of the hidden Markov model. The other one is a pipeline, which includes a Long Short-Term Memory deep learning model, a rule-based correction component, and a statistical-based component. Additionally, we are proposing some modifications to those systems. We have trained and tested those systems in the same benchmark dataset based on the same evaluation metrics proposed in previous work. The best system results are 9.42% and 22.82% for the diacritic error rate DER and the word error rate WER, respectively.
Analysis of Influencing Factors of College Students’ Physical Exercise Habits Based on the Continuous Discrete Algorithm

At present, college students give great importance to their physical training, but their physical habits are poor and they cannot exercise regularly. In view of the influencing factors in the habit formation of physical exercise, this paper puts forward a continuous discrete algorithm to provide students with a scientific and reasonable habit formation scheme. First, the data of college students' physical exercise habits are collected, and the data are cleaned and continuously analyzed to initially form a habit formation data set; Then, the information gain and clustering thought in the discrete algorithm are used to judge the data change, and the ordered relationship among the factors is obtained. Finally, in judging the influence degree of each factor, we find out the final influencing factors. MATLAB simulation shows that the continuous discrete algorithm can accurately analyze the problem of physical exercise habit formation and sort the influencing factors, with an accuracy rate of 90% and a calculation time of less than 19 minutes, which is significantly better than the original discrete algorithm.
Research on Interactive Landscape Design based on Virtual Reality Technology

With the constant development of our economy and the improvement of people’s living standards, the requirements for landscape planning are even higher. In the traditional garden landscape has designed into the modern tools to enhance the use of landscape effect. Based on this, a three-dimensional reconstruction of the scene based on the depth image algorithm is proposed, and the scene depth algorithm and the three-dimensional reconstruction method are given. The scene depth is calculated by calibrating the camera. Finally, according to the depth of information, the introduction of the spatial location of each has been pointed. An adaptive 3D reconstruction method based on multi-source information is proposed, which is easy to understand and implement and has high reconstruction efficiency. Experiments show that this method can meet the basic requirements of the scene reconstruction from the ordinary resolution images.
Sentiment Analysis: A General Review and Comparison

The use of natural language processing and opinion mining methods has been utilized throughout the last couple of years through sentiment analysis to detect, obtain, compute, and examine information which could be valuable to users. Organizations that make use of these methods need these methods to evaluate and improve their customer feedback. There are different types of techniques that have been previously implemented. Sentiment analysis tools can be categorized as machine learning techniques and lexicon-based techniques. The machine learning techniques are divided into supervised and unsupervised learning while lexicon-based techniques are split into dictionary-based and corpus-based approaches. The machine learning techniques categorize the polarity in sentiments while the lexicon-based techniques utilize sentiment lexicons. In this work, different machine learning and lexicon-based techniques have been reviewed to discuss their advantages and their limitations when implemented.
Measuring the “backflash” probability of a single-photon detector with a time correlator

We present an experimental setup for the research of photon re-emission (“backflash”) of a single-photon detector depending on the time parameters. The uniqueness of this experimental setup with a time correlator lies in the very low amount of dark and parasitic noise, as well as in the negligibly small contribution of reflected photons in the histograms obtained. Thus, the signal-to-noise ratio increases by at least two orders of magnitude compared to the “classical” optical reflectometry scheme. Histograms of the distribution of “backflash” counts were obtained with a resolution of 100 ps per bin.
A Movement Adjustment Method for DQN-Based Autonomous Aerial Vehicle Mobility: Performance Evaluation of AAV Mobility Control Method in Corner Environment

The Deep Q-Network (DQN) is a deep reinforcement learning method using Convolution Neural Network (CNN) as a function approximation of Q-values in the Q-learning algorithm. The DQN combines the neural fitting Q-iteration and experience replay, shares the hidden layer of the action value function for each action pattern and can stabilize learning even with nonlinear functions such as CNN. However, there are some points where learning is difficult to progress for problems with complex operations and rewards, or problems where it takes a long time to obtain a reward. In this paper, we present a DQN-based Autonomous Aerial Vehicle (AAV) mobility control method and show the performance evaluation results for a corner environment scenario. The performance evaluation results show that the proposed method can reach the destination and can decrease movement fluctuations in a corner environment.
PERBANDINGAN METODE ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MEMPREDIKSI KELULUSAN MAHASISWA

This study was conducted to compare the accuracy of two algorithm methods, namely the C4.5 algorithm and Naïve Bayes algorithm on a number of datasets. The sources of datasets used are student data of the Informatics Engineering Study Program (STMIK Palangkaraya) where each dataset has a different amount of data (instances) and number of attributes. Based on the results of the comparison study of the C4.5 and Naïve Bayes Algorithm for Predicting the On-time Graduation of STMIK Palangkaraya Students, the results obtained from the accuracy of the two algorithms show that the accuracy of the C4.5 Algorithm is 90% better than the Naïve Bayes algorithm, which is only 85%. The C4.5 algorithm also gives values on recall and precision of 92% and 94% better than the Naïve Bayes algorithm with values of 86% and 93%, respectively.
Fake Listing or Truth? Using Pre-trained Deep Learning Model with Data Augmentation to Detect the Imposter

In 2020, after the rising value of cryptocurrency, Graphics Processing Unit (GPUs) became shortage due to many scalper. To combat with this issue, there were some eBay users that trick the scalper bot with fake description or image in the listing. In this articles, we compare baseline machine learning models (Multinomial Naïve Bayes from Tf-idf vector, Logistic Regression, Support vector machine, Gradient Boosting classifier and XGBoost classifier) with deep learning models (Resnet-34 and Resnet-50 for image classifier, BERT and FLAIR-model for text classification) in order to detect these listings. As the data was imbalance, we used data augmentation to enhance the number of fake listing class for both images and text description. All models can achieve accuracy up to 90% except Logistic Regression. XGBoost and BERT are the best accuracy models when using with data augmentation. The accuracy are over 98% for both models.
Algorithms for Path Planning and Scheduling of Automated Guided Vehicles Iteratively Carrying Objects

In this paper, we address algorithms for path planning and scheduling of automated guided vehicles (AGV) iteratively carrying objects. Since the manpower shortage advances, there is a growing need for technology that can transport objects unattended. Indeed, AGVs are generally used in warehouses for transporting objects. Algorithms for path planning and scheduling of AGVs have been investigated so far; however, the purpose of many of them is the path planning and scheduling of essentially one AGV. It is difficult to design an algorithm for moving many AGVs simultaneously without collision because of the intersection of the paths of AGVs. Especially, when small number of AGVs carry objects iteratively for moving many objects in a limited area such as transport at a port, it is difficult to find the optimum path planning and scheduling. We formulate the path planning and scheduling problem of AGVs for this situation as an optimization problem, design a heuristic algorithm, and evaluate the algorithm.
Semantic Network: A Brief Review of its Datasets

Semantic networks are graphical representations, in terms of nodes and edges, of words and phrases from the linguistics vocabulary to form a meaning and relation between them to describe the intended target or object. It is part of the computer science field utilising Natural Language Processing, text mining, psychology, and sociology. The edges may describe the relation between the nodes as weak or strong, or one way or both ways, or if the node is stand alone. Semantic networks use datasets like ConceptNet and WordNet which uses English vocabulary like nouns, verbs, adjectives, adverbs to form common meanings and relations. Sometimes the linguistics vocabulary is not present or not clear e.g., examining a patient electronic medical records or physician’s handwritten notes will reveal several terminologies which are only specific to medical professionals in their field. Similarly, when the intended target is an opinion or a subjective state which may or may not be true and represent the author personal views, biases, or prejudices. Instances like these make it difficult to establish a meaningful relation for data analysis purposes. In that case, need arises to develop a database from the ground up for the semantic network to be established. This paper briefly discusses the major semantic network datasets which are open source and available for semantic analysis.
User’s Emotion Profiling in Web Browsing Behavior

This study presents a method of user's emotion profiling in Web browsing behavior, based on our assumption that a user can be continuously affected by digital information through the Web site access. There are many studies on the user’s emotion model, however, it is still an important issue to quantitatively calculate the amount of emotion change caused by accessing the digital information. This study focuses on designing a user emotion model where a state of user’s emotion changes according to contents that the user is accessing on the Web. When a user’s emotion model is too sensitive, the user’s emotion state is frequently updated, every time the user accesses the Web contents. To avoid this problem, our proposed method calculates the current value of user’s emotion based on the gap between an emotional polarity of the Web contents that a user is accessing and the user’s emotion profiled in advance for the corresponding topic. By this function of gap calculation, the user’s emotion model can be robust for the excessive frequency of user’s emotion changes in the context of user’s emotion profiling.
An overall solution to cloud migration: A case study in environment protection industry

Enterprises are interested in migrating systems to the cloud due to their scalability, flexibility and apparent cheapness. This paper investigates the benefits and significances of cloud migration in the intelligent environmental protection industry. In the case of the Chihu Lake industrial zone, we proposed and implemented an overall solution to solve the pain points, satisfy the demands and reduce the cost effectively. The implementation result indicates that it can noticeably improve the efficiency of environmental management and pollution control. In addition, the software developed by us not only can monitor the dynamic changes of the environment in real time, but also provide overt interfaces service and various portals for different roles. This work provides new wisdom for the application migration in other fields.
Research on Knowledge Extraction Technology for Knowledge Graph Construction

As an efficient and intelligent means of knowledge organization, knowledge graph has developed rapidly in recent years because it can help users to obtain the information they care about quickly and accurately. Knowledge extraction is a key link in the process of knowledge graph construction, which directly affects the construction quality and subsequent application effect of knowledge graph, and is also one of the criteria for evaluating the merits of knowledge graph. Knowledge extraction has become an important research branch in the field of natural language processing. This paper systematically introduces the knowledge extraction technology for knowledge graph construction, analyzes the key technologies of knowledge extraction in each domain for different data types, and summarizes them at last.
Reconstruction of a unitary transformation of an integrated interferometer using coherent light

Integrated optics fastens its place in taken important part of most promising fields of technology including communication and optical modulators, optical neural networks and quantum computing. Once optical integrated chip is fabricated it is essential to characterize its unitary transformation. The unitary provides a complete knowledge about chip operation. Therefore, trustful methods of chip transformation reconstruction are of interest. Here we have performed unitary transformation reconstruction of the femtosecond laser written optical chip with coherent light.
Short-term prediction of distributed PV time-varying penetration rate based on scene clustering

With the increase of the grid-connected scale of distributed photovoltaic power generation, the time-varying output penetration rate will have a real-time impact on the line flow, node voltage, network loss and fault characteristics of the distribution network. The control and management of the distribution network will become more complicated. This paper firstly demonstrates from the perspective of error analysis that the calculation of the real-time penetration rate of photovoltaics after the independent prediction of photovoltaic output and power load will lead to error amplification. Based on this, this paper proposes to use the fuzzy C-means clustering algorithm to first classify the historical data of the hourly penetration rate of photovoltaic output, and then use CNN-LSTM neural network to perform short-term analysis on the time-varying permeability of photovoltaic output prediction. Finally, the validity of the prediction method proposed in this paper is verified by a numerical example. The prediction results can be used to construct the spatiotemporal distribution of photovoltaic output permeability, and can be used by the dispatching department to determine the corresponding operation optimization plan.
Voiceprint recognition check-in system based on deep learning

With the development of modern technology and the rise of artificial intelligence, the application scenarios of identity authentication technology are becoming more and more complex, especially in the current situation of the spread of the new coronavirus, traditional identity authentication technology can no longer meet people’s practical needs, and society urgently needs a security and convenient authentication technology. Voiceprint recognition is a kind of biometric technology, and it is one of the products of comprehensive research on computer technology, acoustics and life sciences. This paper introduces a voiceprint recognition check-in system based on deep learning algorithm. In this design, the audio is converted into Mel frequency cepstral coefficients, and then the convolution network is provided to extract features. Finally, the similarity is calculated to obtain the classification result for voiceprint feature extraction, which is compared with the voice database data to realize voiceprint recognition. The voiceprint recognition check-in system introduced in this paper has a check-in system with an interactive interface. The average recognition rate of the system measured by experiments is higher than 93.3%, which can meet the requirements of practical applications.
Least Mean Square Algorithm Based on Bessel Function and Multiscale Wavelet Transform

The development of adaptive technology is inseparable from the theory of adaptive filtering and its algorithms. The least mean square (LMS) algorithm occupies an important position as one of the most important algorithms in adaptive filtering and is widely used due to its simplicity, ease of implementation, and good convergence in stationary environments. In this paper, for the LMS algorithm, the LMS algorithm based on Bessel function and the multiscale wavelet transform algorithm are studied respectively. On this basis, a new LMS algorithm based on Bessel function and multiscale wavelet transform is proposed, using MATLAB computer simulation software to conduct experiments, the time domain waveform and convergence effect of the algorithm have been significantly improved, verifying the feasibility of the algorithm. The simulation results show that the newly algorithm has faster convergence speed, higher processing speed, strong denoising ability, and its steady state offset is small.
Homotopy-based formations of robot swarms

A problem of transformation of a planar formation of a robot swarm to another formation is considered. Robots are placed in the vertices of a polygon, so the goal is to transform the entire polygon in a continuous way. The proposed solution is based on the topological concept of homotopy. It describes a continuous passage from one closed curve to another one. During the passage the basic structure of the curved is preserved: at each instant it is a closed curve, but not necessarily a polygon, with robots occupying some places on it. In order to provide a constructive solution, it is assumed that the region bounded by the polygon is star-shaped.
Text classification based on machine learning

Under the era of technical surge in recent years, the weight of artificial intelligence in people’s life is increasing over time. This paper will focus on classification. How to classify efficiently and accurately has become the top priority. In this research, we are trying to use four different algorithms SVM, Naive Bayes, Random Forest, and KNN to classify the text of descriptions. In the experiment, we are going to compare the result of each algorithm by prediction accuracy then summarize the various advantages and disadvantages turns out in each algorithm. The result shows that, the best model we made is Naïve Bayes which can hold for the 55% accuracy in predicting the application number by genre.
Prediction of the Number of Online Doctors’ Followers based on Machine Learning

The number of followers of online doctor can reflect the popularity and reputation of a doctor, and the numberof a doctor’s followers is affected by graphic consultation fee, video consultation fee, online consultation evaluation, hospital consultation evaluation, score, consultation volume and reservation volume. To explore the relationship between them, this paper establishes multiple linear regression model, Bayesian ridge regression model, elastic network regression model, support vector machine model, random forest, gradient boosting regression model and back propagation(BP) neural network model by python. The result shows the BP neural network model has the best performance and can be applied to the prediction of doctors’ followers number.
Creating Library Marketing Campaign on New Media: Practice of National Science Library, Chinese Academy of Sciences

With the development of digital techniques and the changes of users&#x0027; behaviors, the tendency of library marketing is changing from conventional ways to digital ways. This paper will introduce a practice of National Science Library, Chinese Academy of Sciences on library marketing campaign on new media. Depending on the platform of the WeChat subscription, we create an official account named &#x201C;Zhongke Zhihui&#x201D; (ZKZH) which means convergence of knowledge in Chinese and take a series of actions. Firstly, we clear our fixed position and target customers, and make specific objectives. Secondly, we describe how we use 4Ps marketing mix as theoretical foundation and implement the marketing campaign from three perspectives-a virtual portal of library, special columns, and sharing information of library events. Thirdly, we monitor process and evaluate its effects to ensure that our campaign achieves objectives and satisfies customers&#x0027; needs well. In addition, we have further thoughts that making good use of new media brings us positive impacts on library marketing and it will continue playing an important role on library business.
Analysis on the recent development of quantum computer and quantum neural network technology

Many countries have invested a large amount of scientific research funds into supporting quantum information technology and proposed many policies to support the three main quantum technology fields: quantum sensing, quantum communication and quantum computing. Many science and technology giants have also provided quantum cloud computing experimental platforms that can provide quantum access online. With the investment of various technologies and resources, quantum information technology has developed rapidly. The combination of quantum computing and neural networks is a research branch that has attracted much attention in the field of quantum computing in recent years. It mainly involves the study of how to exploit the computational acceleration advantage of quantum computing to deal with various problems solved by neural networks in traditional artificial intelligence. In this paper, the recent development of the quantum computer is widely investigated, and a quantum neural network is briefly analyzed. Finally, the performance of the quantum neural network and quantum optimization is discussed, and future research directions are proposed.