Reviewerly/exHarmony huggingface dataset:
Suspicious Crime Identification and Detection Based on Social Media Crime Analysis Using Machine Learning Algorithms

Social networking sites, such as Facebook and Twitter, make it possible for people to communicate with each other. However, some people have exploited the influence of social networking sites for illegal activities, such as intimidating news stories and suspicious posts on local community websites. The main objective of this work is to identify suspicious posting activities on Facebook and predict crime rates among those posts. By using pre-processing steps, the dataset can be cleaned up for identification by eliminating missing values, avoiding duplicates, stemming, removing stop words from posts that may indicate a suspicious post, and encoding the dataset for common format conversion. In order to construct a suspected profile, posts from social media are clustered together. In the next step, relevant features related to crime are selected for prediction using a feature selection strategy. To predict the single crime that relates to the various suspicious activities, classification approaches will be applied.
Recommendation System and Its Techniques in Machine Learning: A Survey

Machine learning-based recommendation systems are formidable tools that target specific customers with tailored product and content recommendations based on their user data and behavioural patterns. Due to the abundance of information available today, it is now challenging to separate out and provide the user with the most pertinent information. Therefore, using recommendation systems to reduce time and costs for both the business and the user is now necessary to address the issue of information overload. Based on a user’s interest and past preferences, these systems might suggest suitable products to them, thus increasing revenue. This paper seeks to clarify the idea of the recommendation systems built upon, the many methods used to create these systems and the necessity of using these systems in the current and future environments.
Knowledge Representation and Information Retrieval from Ontologies

The Ontology is an emerging domain that assists is intelligent decision making and connects various users, thereby facilitating a way of presenting the information on a common platform that could be used by the users for decision making. The Ontology leads to structuring of the unstructured data and retrieval of information from the system. The Ontology allows to share and reuse the data and its related concepts in a homogenized manner so as to provide a single nomenclature to the unstructured data that has been gathered from different sources and paving a way for elucidating the identical data easily. As the times are progressing to the fourth business revolution, absolute use of the artificial intelligence authorized ontologies leads to the decision support in many real-time applications. There are many methodologies by which an Ontology can be designed and the data can be structured and retrieved from the Ontology. This research paper aims to present a survey of the existing literature present in the domain of Ontology that would help in understanding the knowledge representation and information retrieval from the Ontologies created using various techniques.
An Improved Jaya Algorithm (IJAYA) for Optimization

Jaya algorithm is a newly developed algorithm with a very straightforward structure and requires only a few number of control parameters for optimization. However, it needs to be improved in terms of exploration and accuracy of outcomes with a fast convergence speed. This work proposed an improved version “IJAYA”, of the Jaya algorithm by introducing an average number-based equation of best and worst position of vectors in search space. The proposed method approaches worst and best positions to each other and resulting in increase in the search power and exploration index. IJAYA tested on six benchmark problems picked from the literature. Finally, the comparison and result demonstrate the effectiveness of our suggested approach.
Automatic Infographic Builder Using Natural Language Statements

“A picture is worth a thousand words,” visual memories are the strongest memories. Combining graphics with information, Infographics are an effective and eye-catching way to deliver information in a memorable manner. Several kinds of authoring tools have been developed for creating infographics, but casual users either find them difficult to use or do not invest their time in learning to understand the working of these tools. In this paper, an alternative approach for the creation of Infographics from user friendly natural language statements has been proposed. The approach incorporates dynamic layouts, designs, and graphics. The approach takes a natural, proportion-related statement as an input and analyze it using natural language processing algorithms. It dynamically selects graphics and images for infographic creation using a Deep Neural Network model. Taking into consideration, the various visual aspects of Infographic creation, the approach generates a set of Infographics from which the user can select and refine according to their use. The training of the system performed on the MS COCO dataset and the parameter results obtained after testing demonstrates the effectiveness of the system. With an increasing use of visual Infographics in various domains, this system proves to be efficient and time-saving as illustrated by the generated samples and results.
Person Detection Using YOLOv3

In today’s world, person detection in video surveillance is very important. It has many applications like crowd counting, single and multiple object tracking, crowd behavior analysis, anomaly detection, etc. There are different models to detect a person in an image and video. But, the majority of the models focused on many object classes which sometimes lead to poor performance for detecting specific objects. In this paper, a single class of object is considered, i.e., person. Here, we have used transfer learning for generating the person detection system by using YOLOv3. We have generated the content specific customized dataset, and annotated the dataset manually by using Label Tool. The result shows that the proposed model detects and classifies the person with higher accuracy.
Analysis of the Distractions in Youth Due to Social Media and the Effects on Their Concentration Abilities

Distraction is a phenomenon that diverts one’s attention from a desired task. It includes external factors such as addiction toward social media, web surfing, electronic gadgets, texting, and also, the internal factors like laziness, nervousness, lack of concentration, etc. Sometimes, distraction can be useful to divert from a trauma or misery. However, it creates an adverse effect on the person’s mental health by causing anxiety, depression, behavioral changes, loss of interest in humans which affects their career and even leads to death. This study mainly focuses on distractions in youth and its effects on their personal mental health. A quantitative approach was used in collecting and analyzing the data by distributing google forms and verbal response from to the students. The results obtained were analyzed using various machine learning classifiers. Among different classifiers, Logistic Regression obtained the result of 94.68% accuracy and also analyzed on various factors which can cause distraction in youth.
Automated Text Summarization Using Transformers

Text summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information precisely in large text in a short time with little efforts. There are two main strategies involved in text summarization such as abstractive and extractive. In extractive method, the algorithm generates the summary by just picking up the words and line from the corpus. On the other hand in abstractive method, the algorithm generates the summary by rewriting the sentences. The main idea of this paper is to summarize text and know how transformers work in case of text summarization.
Natural Language Processing Meets Deep Learning

Natural language processing (NLP) also known as computational linguistics (CL) is concerned with the question of how to endow computers with the ability of analyzing and generating language.
Research on the Phenomenon of Tone Sandhi of BB in ABB-Pattern Adjectives from the Perspective of Coercion Effects

The tone of BB is one of the concerns of phonetic studies of ABB, and previous studies have noticed that the tone of BB is mostly yinping or becomes yinping, but there is still a lack of research on the variation law and reasons of BB’s tone. By comparing the tones of BB in “Modern Chinese Dictionary (1996 edition)” and “Modern Chinese Dictionary (2015 edition)”, we found that the tone sandhi of BB is related to the part of speech of BB, with a high proportion of non-adjectival BB’s tones changing to yinping, and adjectival BB tending to be pronounced in the original tones. The descriptive meaning also influences the tone sandhi of BB, and BBs with lower descriptivity tend to change to yinping. We suggest that coercion effects are the main reason for BB changing tones, and components whose semantics do not meet the requirements of construction are phonetically coerced.
Development of Mobile Augmented Reality of Series Circuits for Science Learning in Primary School Students

The use of augmented reality (AR) technology has increased the diversity of implementation. With this technology, users can construct an enhanced and augmented reality that can aid in knowledge acquisition by fusing observable, real-world occurrences with animated graphics, written data, or inserted images. Using a mobile augmented reality platform, instructors improve the pedagogical value of their courses while also assisting students in exploring resources in realistic ways. The objective of this research is to develop an augmented reality to encourage students' attitudes and acceptability of science students being taught series circuits via augmented reality. 33 sixth-grade pupils took part in this study in the northeastern region of Thailand. Designing augmented reality was based on a misconception. Prior research revealed common misconceptions among students, including “No light comes on if the switch is off, and parallel connections provide superior lighting than series connections.”. In addition, the architecture of augmented reality focused on a 3D display that showed a flow of electricity and electron and adjusted many parameters to measure voltage and electric current. The evaluation was conducted post-intervention. Students' attitudes and acceptance toward augmented reality were collected and discussed.
On the Emergence of Positive Meaning of Xiāngdāng from the Structure of Xiāngdāng de NP

Xiāngdāng (相当) can modify nominal component more freely and express positive meanings such as ‘very large/good/outstanding/excellent’. The positive meanings of xiāngdāng emerged under the joint action of multiple factors. The inherent quantitative feature of xiāngdāng has accompanied its semantic evolution throughout. Therefore, under the dual effects of the shift of cognitive focus and the semantic constraint of yǒu(有) sentences, the degree implication of xiāngdāng presents high-order prominence so that the degree of high quantity meaning is solidified and became the source power of the positive meaning. At the synchronic level, the close adjacency in syntactic order, the direct relation in semantics and the shift of communicators’ focus further accelerate and solidify the semantic fusion between degree adverb xiāngdāng and the modified attribute concept.
Automatic Patch Generation System for Smart Contract

Smart contract is a computer program running on the blockchain to automatically execute the contract terms without supervision. However, the vulnerability of smart contracts often causes massive property losses to users. SolSaviour is a framework for repairing and recovering vulnerabilities from deployed smart contracts. Thus, we design a set of automatic patch generation system APG within the SolSaviour framework, which solves the security problem of patches applied by SolSaviour and enhances the protection function of SolSaviour for smart contracts. APG consists of two existing frameworks: Slither and EVMPatch. Through comparative analysis, the improved SolSaviour by APG has higher security.
Application of Computer Virtual Reality Technology in College Sports

The development of modern information technology has set off a new wave of virtual reality education in the field of teaching. Virtual simulation training is an important development at present. Throughout the development of teaching form, it has experienced a development path from plane to three-dimensional, single to complex, and simple to deep. However, subject to the influence of various objective factors, the leap forward of experimental teaching to this process is limited, which in turn affects the development of experimental teaching. The teaching effect of experimental teaching application. The main purpose of this paper is to study the application of computer virtual reality technology in sports. The experiment showed that the excellent number of people in the experimental group accounted for 35%, while only 10% in the control group, which is one-third of the virtual teaching experiment.
Integrating 360° Virtual Learning Environment to Support Out-Of-Class Inquiry Activity for Preservice Teachers: A Preliminary Study

Over the last few years, 360° virtual learning environment has been introduced as an educational tool. Educators started embedding the related technology into their classes. In general, this technology is recognized as a suitable tool to illustrate real-context experiences when the reality is hardly accessible, for instance, due to safety issues. However, the pedagogical implementation of these learning media has not been widely explored, especially in the context of higher education. In this study, we aimed to develop the 360° virtual learning environment and design the pedagogical usage to support learners before they are in the context of in-field inquiry activity. The 29 preservice science teachers were recruited in this study. After participating in the 360° virtual learning environment and the out-of-class inquiry activity, the perception questionnaire was administrated. Moreover, they were asked to provide feedback regarding their learning experiences. The quantitative and qualitative analysis, including arithmetic means, standard deviation, and content analysis, is used to analyze the learners' perceptions and self-reflection, respectively. The results and findings of this study are discussed to apply the 360° virtual learning environment to the learning domain.
Design of French Teaching Effect Analysis Model Based on Random Forest Algorithm and Neural Network

In order to improve the quality of French in colleges and universities, a French quality data analysis model is proposed integrating the personal information of teachers, students, and teaching methods. The random forest algorithm is used to measure and describe the feature correlation between teachers and students, the feature correlation between objects and target variables, and the more accurate feature classification method is used to filter and save the optimal feature subset. Through the long and short memory in the self-attention mechanism, the neural network combines the portrait information of teachers and students as well as the French teaching information and carries out an in-depth analysis of the relevant data. Then, a more accurate conclusion is drawn on the correlation between the quality of French teaching and the relevant parameters. The empirical analysis results show that the proposed model effectively analyzes the relevant factors and rankings that affect the quality of French teaching at this stage and obtains more reasonable French teaching data to improve the quality of French teaching.
Construction of Grammar Collocation Library for International Chinese Language Education and Difficulty Standard

Grammar teaching is the focus of International Chinese Language Education, and grammar collocation resources play an essential role in promoting grammar teaching. Based on the three-dimensional grammar description of the formal structure, function, and typical context of the predecessors, this paper uses the BCC corpus to search the sentences of grammar points. This study explores a novel method to accurately acquire practical collocations and sample sentences in large quantities and constructs a collocational library containing 264 grammar points.
Practical Teaching Model as a Tool for Skills Development of Preschool Teachers in China: A Case Study of H University

Practical teaching has been regarded as important content in the teaching of modern colleges and universities.Practical teaching helps students fully transform the theoretical knowledge they learn in class into professional skills.This paper studies the influence of practical teaching on the vocational skills of preschool teachers in the future to help students get better practical results.The study found that cognitive and management assurance significantly impact practical teaching, promoting the transformation of vocational skills.Colleges and universities should guarantee the time for practical teaching, make close contact with the practice teaching base, and strengthen the development of teachers to improve the quality of practical teaching in universities.
Book Review Sentiment Classification in Bangla using Deep Learning and Transformer Model

Sentiment analysis is the approach detecting different underlying emotions from text data. E-commerce business is getting popular all over the world, including in Bangla-speaking regions. E-commerce websites provide different kinds of products and services. Following the growth of e-commerce businesses, there is a gap in research on consumer sentiment analysis. Our study focused on sentiment analysis of book reviews in Bangla. In this paper, we have developed our dataset of 5189 reviews by crawling data through the review sections of rokomari.com, a popular online platform for selling books in Bangladesh. The reviews were manually annotated into positive and negative sentiments by experts. We have investigated the performance of four different deep neural network models; LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM, and three transformer models; m-BERT, Bangla-BERT and XLM-R. We have found that XLM-R outperforms the other DNN models as well as the other transformer models achieving the highest weighted f1 score (88.95%) on test data. The dataset is made publicly available at https://github.com/gcsarker/Bangla-Book-Review-Dataset.
Splicing All Possible Convex and Non-convex Pentagons with Tangram using DFS

The tangram is an ancient Chinese traditional intellectual toy that includes seven puzzles of different sizes and shapes. Those seven fragments can combine thousands of different shapes. However, it is known as an NP-hard problem to solve the tangram puzzle problems due to the unboundedness and multiformity. This paper provides an approach for solving all possibilities to form pentagons using Deep First Search(DFS) algorithm and other Mathematical theories, which reduce the searching counts. Specifically, a heuristic search is considered, in which the border of the pentagon can be limited to a specified area. The key point of the proposed method is to find the contours of the pentagon that meet the conditions and then to fill in the outline with 7 pieces of tangrams using the DFS algorithm. Our basic idea is to separate all 53 pentagons into 2 convex pentagons and 51 nonconvex pentagons, then separate those non-convex pentagons into those whose vertices are contained in the same orthogonal lattice (20 kinds) and whose are not (31 kinds). Our results illustrate that tangram can make at least 53 different contours and there is at least one patchwork approach in each contour using computer programming.
Clustering Research on Learning Behavior of Online Moral Education Course Based on K-Means Algorithm

With the development of Internet study, big data mining for Internet learners is forming a new research direction. Network higher education institutions offer higher education with distance learning for continuous learning. Based on the analysis of the learning diaries of the students in the network of Xi'an Jiaotong University, this study aims to reveal the learning situation of the students in the network of higher education institutions and probe the relationship between the typical Internet study abroad. Based on the statistical analysis of the log data, the aggregation analysis is carried out according to the number of main knowledge points mastered by learners and the realization rate of relevant main knowledge points, and the learners of Internet colleges and universities are classified. The Spearman correlation coefficient is used to analyze the relationship between various learning activities and the achievements of learners. The result provides an important basis for further improvement and a more accurate evaluation of online teaching in the future.
Anticipatory Classifier System With Episode-Based Experience Replay

Deep reinforcement learning with Experience Replay (ER), including Deep Q-Network (DQN), has been used to solve many multi-step learning problems. However, in practice, DQN algorithms need better explainability, which limits their applicability in many scenarios. While we can consider DQN as a black-box model, the Learning Classifier Systems (LCSs), including anticipatory versions, also solve multi-step problems, but their operation is subject to interpretation. It seems promising to combine the properties of these two learning approaches. The paper describes an attempt to design and evaluate modification to the Experience Replay extension of the anticipatory classifier system ACS2. The modification is named Episode-based Experience Replay (EER), and its main premise is to replay entire episodes instead of single experience samples. Promising results affirmed by Bayes estimations are obtained on multi-step problems, albeit limited to deterministic and discrete tasks. The experimental results show that the EER extension significantly improves the ACS2’s learning capabilities.
Pushing ML Predictions Into DBMSs

In the past decade, many approaches have been suggested to execute ML workloads on a DBMS. However, most of them have looked at in-DBMS ML from a training perspective, whereas ML inference has been largely overlooked. We think that this is an important gap to fill for two main reasons: (1) in the near future, every application will be infused with some sort of ML capability; (2) behind every web page, application, and enterprise there is a DBMS, whereby in-DBMS inference is an appealing solution both for efficiency (e.g., less data movement), performance (e.g., cross-optimizations between relational operators and ML) and governance. In this article, we study whether DBMSs are a good fit for prediction serving. We introduce a technique for translating trained ML pipelines containing both featurizers (e.g., one-hot encoding) and models (e.g., linear and tree-based models) into SQL queries, and we compare in-DBMS performance against popular ML frameworks such as Sklearn and ml.net. Our experiments show that, when pushed inside a DBMS, trained ML pipelines can have performance comparable to ML frameworks in several scenarios, while they perform quite poorly on text featurization and over (even simple) neural networks.