Reviewerly/exHarmony huggingface dataset:
Legal Nature of Artificial Intelligence Products

With the improvement of artificial intelligence personification technology, its self generated content is more and more up to the level of natural person works, in line with its appearance elements, and has considerable copyright market value. Due to the improvement of artificial intelligence personification technology, there are more and more artificial intelligence products, but there is a lack of legal nature of artificial intelligence products, and the ownership of artificial intelligence products cannot be guaranteed. In order to solve the problem of judicial protection of artificial intelligence products, this paper studies the legal nature of artificial intelligence products through the ownership of the rights of artificial intelligence, the creation of artificial intelligence, and the composition of artificial intelligence products. This paper uses the method of questionnaire to collect data on the legal protection of artificial intelligence products, and the results show that for the generation of artificial intelligence, the legal protection of artificial intelligence products can be obtained The number of people who support the mode of work protection is as high as 48.9%, which shows that it is necessary to protect the artificial intelligence by law.
Topic analysis of French–English tweets in France

Abstract Twitter has become a popular means of communication used in a variety of fields. This widely used platform has an impact on the way people express themselves and is changing language usage worldwide at an unprecedented pace. This article investigates the reasons for the use of French–English language mixing among French users on Twitter using a topic analysis approach. This analysis draws on Gumperz’s theory of conversational switching. In order to collect tweets at a large scale, the data were collected in R using the rtweet package. A total of nine topic categories were identified and analyzed in this article: entertainment, internet/social media, events/community, politics/news, sports, sex/pornography, innovation/technology, fashion/makeup, and business. This study finds that entertainment is the most frequent topic discussed on Twitter. English words, such as trailer, spoil, and live are found in the data. This study enables a deeper understanding of users’ linguistic behavior online, and offers insights into future trends in intercultural and cross-language exchanges.
Similarity Association Pattern Mining in Transaction Databases

Association pattern mining is a method of finding interesting relationships or patterns between item sets present in each of the transactions of the transactional databases. Current researchers in this area are focusing on the data mining task of finding frequent patterns among the item sets based on the interestingness measures like the support and confidence which is called as Frequent pattern mining. Till date, in existing frequent pattern mining algorithms, an itemset is said to be frequent if the support of the itemset satisfies the minimum support input. In this paper, the objective of our algorithm is to find interesting patterns among the item sets based on a Gaussian similarity for an input reference threshold which is first of its kind in the research literature. This study is limited to outlining naïve approach of mining frequent itemsets which requires validating every itemset to verify if the itemset is frequent or not.
Linearly Constrained EKF for Non Linear Estimation Applied to Three-Wheeled Vehicles

The extended Kalman filter (EKF) is one of the most popular estimation techniques that has been largely investigated for state estimation of nonlinear systems. However, performance of the EKF depends on the accurate knowledge of the state space parametric model and is particularly sensitive to different types of mismatches between the assumed state space model and the actual state space model. In this communication we show how linear equality constraints can be incorporated to the EKF in order to mitigate misspecification of the state model, which yields a linearly constrained EKF (LCEKF). As a toy model, we consider the problem of the position estimation of a three-wheeled vehicle. We exemplify through simulations, the capability of the LCEKF to mitigate some state modeling errors.
Sentiment Analysis of Tweets from Airlines in the Gulf Region Using Machine Learning

Twitter is one of the most popular social networking services in the world. Over the past few years' people have been using Twitter messages on a daily basis to express their views and share their feelings. As a result, the size of data is increasing dramatically creating opportunity for researchers to use these tweets as sources for data mining and extract valuable information. Being popular in Saudi Arabia, we believe that twitter messages (tweets) are a good source to capture the sentiment of people. These twitter messages can be divided mostly into two classes: positive or negative. Our goal is to design and implement a sentiment analyzer that will classify real tweets collected by Twitter API into one of the above categories. We used a machine learning based sentiment analysis method and applied several supervised learning algorithms such as Logistics Regression, Naïve Bayes, Support Vector Machine, and Decision Trees. To accomplish this, we preprocess the data-set that we have chosen to train our classifier. We used Bag-of-words and TF-IDF techniques to extract features from the preprocessed tweets. We also used uni-gram, bi-gram, and tri-gram to rank our features to identify best predictive accuracy for the classifier. Among the classification techniques that we used, Logistic regression performs the best in terms of accuracy for the validation and test data while using tri-gram features with stop words and TF-IDF feature extraction technique.
Development of Speech and Gesture Enabled Wheelchair System for People with Cerebral Palsy

People with multiple disabilities, especially with diplegia or quadriplegia are wheelchair bound and they depend on others for their mobility. Conventional wheelchair system that are operated using joystick though have a lot of advantages, may not be a complete alternative for people with disabilities along the arm. A natural source of alternative in these cases would be to use their speech or gesture as commands to control the wheelchair movement. In the current work, to build a speech-enabled command and control (limited vocabulary speech recognition) system, a statistical-model based speaker dependent speech recognition system is trained using hidden Markov models (HMMs) and a deep-learning based robust speech recognition system using DNN-HMM for both English and Tamil. A total of 13 command words each are used in English and Tamil ASR, to control the wheelchair. The trained ASRs are ported on to a Raspberry pi 4 module. A WER of 0.98% and 2.62% are achieved for English and Tamil respectively using DNN-HMMs. Though, gesture may not completely be an alternative to them, few user defined doable gestures by the users may be helpful in calling the wheelchair near them if they are kept quiet far. Towards this end a user-defined hand gesture recognition system is built using accelerometer-gyroscope-based MPU 6050 with ESP32 Bluetooth enabled device. The wheelchair control is enabled by the use of a robotic arm to control the functionalities of the joystick through speech or gesture.
TRANSFORMING THE VALUES OF ACHIEVING THE LIFE SUCCESS OF MILITARY INSTITUTE CADETS

The transformation of the values of achieving the life success of cadets was investigated, their differences in views and preferences in 2014, as well as 2020 were revealed. As a result of the analysis of these empirical studies, it is justified that the combination of basic values "self-confidence," "hard work and perseverance," "patriotism" in modern conditions, on the one hand, is filled with specific content, on the other hand, it seems as the most necessary and positive factor in the implementation by cadets of achieving life success. It has been proved that as a result of military reforms, several trends are being formed to transform the values of achieving the life success of students of a military institute: a group of cadets is stably preserved, for whom "self-confidence" is the main value of achieving life success; the composition of cadets - "workaholics" linking priority values with "hard work, as well as perseverance in the process of achieving the goals" is reduced; a group of cadets is growing - "patriots" completely devoted to the country, ready for self-sacrifice in the name of the good of the Motherland. The interest of young people in gaining a military profession is increasing, the quality of the potential of those entering military institutions is steadily increasing, and the motivational values of achieving their life success are steadily developing.
Multi-interest Aware Recommendation in CrowdIntell Network

In CrowdIntell Network, there are various transactions between different Digital-selfs, and these transactions are affected by the mind of the intelligence subjects. Mining and analyzing the mind of the digital-selfs is conducive to the better decision-making of the Intelligent subjects, and promotes the conclusion of transactions. We focused on how to model the interests of Intelligent subject in a complex environment, and how to use interests to make the recommendation. Considering the relevance and sequential characteristics of the behavior of the Intelligent subject in CrowdIntell Network is of great value for predicting future behavior. Most of the previous work was directly modeling the sequence patterns on interactive history, and then training a DNN model to learn the interest representation. Recent research focuses on interest mining by embedding both sequential and original characteristics of behaviors. However, in a real environment, the interests of users (for the convenience of description, we simplify intelligent subjects to users) are complex and diverse (e.g., a certain user likes documentaries and also likes science fiction movies), and a user may has multiple interests. In order to solve this problem, a Multi-interest Aware Recommendation in CrowdIntell Network is proposed, which involves the embedding layer, two-stage feature extraction layer and full connection layer. Specifically, the model is firstly constructed and trained to learn the user's multi-interest representation by considering the interaction between items in the behavior sequences, and then the convolutional attention mechanism is used to extract local features on the interests “image” as a higher-order user interest representation. Experiments on public datasets show that our method outperforms state-of-the-art sequential recommendation methods.
Research on Informationization Design of English Project Independent Teaching Platform

With the continuous development of the society, China's information technology has also made rapid progress, which has a lot of inspiration for our English teaching. With the continuous development of information technology, we use the information teaching means in our teaching and conduct research on relevant teaching design methods based on the project-based teaching concept. This has incomparable benefits for our information teaching design methods and provides us with new design concepts for English teaching design. In the era of information technology, it is an important task for colleges and universities to deepen the reform of English teaching how to use information technology for teaching design of English courses in project-based teaching. Therefore, this paper will explore the methods of English information-based teaching design based on project-based teaching.
Comparison Between Conventional Methods, Neural Network, and Support Vector Regression in Forecasting Foreign Tourists in Indonesia

Abstract Tourism is one of the important sectors in the growth of Indonesia’s economy. To be able to achieve the target and increase foreign tourist visits to Indonesia it is necessary to plan appropriate promotion and sustainable development that must be in line with the development of foreign tourists to be in target, effective and efficient. In this study forecasting to the level of foreign tourists visiting Indonesia in order to obtain accurate data. Forecasting is done by comparing 3 forecast methods, viz. traditional method, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN) method. In this research, there are 36 monthly visit data from 2017 to 2020 that are used to forecast. The result of this research indicates that the best forecasting is done by the Support Vector Regression (SVR) method with a MAPE value is 2.5614 % whereas, Backpropagation Neural Network (BPNN) has a MAPE value 31.3777%.
A Task Execution Framework Based on Aging Indicator and SARIMI

The long-term running characteristics of spatiotemporal related tasks increase the risk of software aging of the basic supporting software of cloud platform, such as operating system, virtual machine and virtual machine monitor, and reduce the stability of platform services. In addition, software aging, as a process that accumulates over time, does not immediately cause system function failure. Moreover, the existing time series analysis methods do not give enough consideration to the seasonality of load characteristics, which makes it difficult to accurately determine the degree of software aging and the time of software rejuvenation. Therefore, this paper proposes a task execution method integrated with software rejuvenation means. Through the detection and evaluation of software aging, when the virtual machine aging reaches a certain extent, the rejuvenation module will be started to restore the service capacity of the virtual machine in time, so as to improve the task execution efficiency. And through the use of aging indicator and the software aging evaluation method based on Multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA), the current software aging situation and the periodicity of the system load are considered. At the same time, the dynamic trend of software aging is evaluated, and the software is rejuvenated near the optimal rejuvenation time point, which improves the applicability of resource aging evaluation and task execution efficiency in the cloud platform. Experimental results show that our method is effective in the aging of cloud platform software.
A neuro-genetic Hopfield associative memory (HAM) with improved noise immunity

Artificial Neural Network (ANN) is a form of computation inspired by the arrangement and purpose of the brain. Pattern Recognition is one of the demanding areas under Artificial Neural Network and pattern association is one of the major tasks through which pattern recognition is realized. Nowadays researchers are taking interest in the combination of evolutionary algorithms and ANN. In the present work, an attempt is being made to analyze the performance of Hopfield Associative Memory with Monte Carlo adaptation rule and evolutionary searching (Genetic Algorithm) for pattern storage and recall. The training pattern set has been taken a set of five Greek alphabet letters as different objects. The aim is to store Patterns with MC adaptation rule and get the best weight matrices for efficient recalling of any given set patterns. The neural networks architectures have been taken up for storing the given set of inputs, for which conventional Hebbian and Monte Carlo adaptation rule is considered. During the mutation process, various mutation probabilities are supposed to be evaluated. The detailed simulated results thus obtained will be presented with the help of tables and graph.
Standard Deviation and Mode Based Bi-Histogram Equalization Algorithm for Image Enhancement

This paper presents an enhancement method for image enhancement in poor lighting condition. Standard deviation and mode are computed on the image histogram. Histogram clipping process is done to limit the rate of over enhancement by mode-based threshold value. The clipped histogram is divided into two segments founded on a threshold value computed by the standard deviation on the image histogram. Two sub-images of the individual histogram are equalized and combined to form the output image that preserves the entropy and produced better contrast enhancement image. The results in terms of Universal Image Quality Index (UIQI), and Entropy, for images, have been simulated on MATLAB. The outcomes produced by the proposed method have been ompared with the results gotten from existing image enhancement methods. It has been observed from the outcomes that proposed method outperform previous methods.
L'identità rinnovata in Va e non torna e M di Ron Kubati

Migration and identity are the issues around which the social, anthropological and cultural discourse has developed in recent years. The migratory flows that have affected Western countries in recent decades have posed numerous questions on issues such as social cohesion, integration, education, culture. In this perspective, the essay dwells on the relationship that binds the experience of migration to literature, highlighting the role of writing as a place of identity processing. In particular, it analyzes the stories told by the writer Ron Kubati. In Va e non torna and in M, the writer goes in search of the otherwise and redemption, in which the relationship with the past, with his mother tongue, with the future follows a difficult path made of denial and estrangement, of hope, of pain and dream.
Thumbnail Personalization for Users Based on Genre Preferences

Streaming platforms have seen huge growth and an increase in the number of users across different devices. There is a lot of video content on all the platforms and the consumer would not be able to identify their preferred movies just from the title and poster. These platforms use machine learning to personalize the user experience and keep the users interested by recommending movies aligned with their preferences. The next step in personalization is showing likable and preferred thumbnails to increase engagement with the content. Personalizing thumbnails for consumers helps them identify with more ease the content they would enjoy watching. The same movie could have different thumbnails catering to different audiences. Our paper explores methods to select different frames from a video to represent the different genres.
Remote Procedure Call Optimization of Big Data Systems Based on Data Awareness

In recent years, with the development of artificial intelligence technologies, mobile smart terminals, and high-bandwidth wireless internet, the scale of data generated by various data sources has continued to expand. Huge amounts of data contain huge economic value, therefore how to store and process the data efficiently becomes very significant. HDFS (Hadoop Distributed File System) has emerged as a typical representative of data-intensive distributed file systems, and it has features such as high fault tolerance, high throughput, and can be deployed on low-cost hardwares. However, the remote procedure call in HDFS is still not good enough to work better in terms of network throughput and abnormal response. This paper presents an optimization method to improve the performance of HDFS. The proposed method dynamically adjusts the RPC (Remote Procedure Call) configurations between NameNode and DataNodes by sensing the data characters that stored in DataNodes. This method can effectively reduce the processing pressure of the NameNode and reduce the network throughput generated by the information transmission between NameNode and DataNodes. It can also reduce the abnormal response time of the whole system. Finally, the extensive experiments show the effectiveness and efficiency of our proposed method.
Weighted Frequent Itemset Mining Using Weighted Subtrees: WST-WFIM

There is a wide range of association rules mining algorithms concerning the importance of weighted data to extract frequent rules. In some algorithms, each item has only one weight in the data set, while, in others, each item in each segment of the data set has only one specific weight. In this work, a new efficient algorithm named weighted frequent itemset mining using weighted subtrees (WST-WFIM) has been proposed to discover the average weight of frequent rules. This algorithm uses specific trees and some new data structures on the frequent pattern growth (FP-Growth) algorithm to calculate the average weight of discovered rules. It works on the data set that each item in each transaction has a specific weight and keeps them in the dedicated tree. Moreover, we have proposed the shared transactions' concept and WST-WFIM by using it and calculate the average weight for discovered frequent rules. The algorithm capability was evaluated by standard sparse and dense weighted data sets. The results show that, while the algorithm works on weighted transactions, in sparse data sets, relative runtime increases by a decrease in minimum support (MinSup) parameter in comparison to the FP-Growth, and memory usage is approximately the same. The variations of runtime and memory usage with the MinSup parameter are opposite for dense data sets.
Methodology of teaching of design – creative manifestos

This article discusses the main manifestos of designers and theorists of the world, setting new trends in the methodology of education. The materials published in the 70s of the last century, which have not lost their relevance today, as well as modern materials in the field of design theory published in different countries of the world, are studied. The analysis of these sources allowed us to identify two groups of conditions and requirements for the new methodology of teaching design, the need for the formation of modern project thinking. One of them focuses the system on the idea of serving not advertising and industry that produces similar and even dangerous products, but something more valuable, durable and unique. As well as participation in solving important global problems. Another group forms the criteria for the nature of the educational process itself. It puts before us the need to choose methods of continuous, active education, integrate it into everyday life, saturate it with various creative technologies, points out the advantages of working in a team and calls for a constant rethinking of the past experience, both of all mankind and of an individual artist-designer. A methodology based on such requirements is being developed and used in the modern world. All this speaks of changes in the teaching of design, the creation of certain pedagogical conditions that play an important role in the development of design around the world.
The Educational Effect on Course of Scientific Experiment for High School Students

As a problem related to the education of students in primary and secondary education stage, it has been pointed out that they lack interest in science and engineering. From this kind of circumstance, Kanazawa Institute of Technology holds science experiment courses for high school students during summer vacation. As one of the scientific experiment courses, we have developed the experimental course to suppress the shaking of a building model due to earthquake with dynamic damper, and held the contest-style experimental course. As a result, it was suggested that the hands-on scientific experiment course conducted this time on the subject of familiar phenomena is effective for raising students’ awareness of the engineering field.