Reviewerly/exHarmony huggingface dataset:
Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT

ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques.
Facial Emotion Detection Using Deep Learning: A Survey

The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin’s 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain.
Towards Workflow Allocation using Dragonfly Algorithm in Cloud Computing Environment

The cloud computing platform provides clients with on-demand access to computational resources on pay you go basis for workflow execution to ensure the quality of service (QoS) parameters like cost and execution time, energy, and resource utilization. This paper proposes a metaheuristic algorithm, Dragonfly algorithm (DA) to minimize the makespan of workflow tasks from cloud users submitted for execution. DA utilizes static and dynamic swarming behaviors to explore and exploit in navigation, search the foods, and avoid enemies. It has various phases: separation, alignment, cohesion, attraction, and distraction. The workflow allocator is implemented in MATLAB for the performance analysis. The experimental findings demonstrate that the Dragonfly algorithm (DA) significantly performs better than its peer in terms of fitness value (makespan) and convergence rate under study.
Image Captioning using Deep Learning Techniques

The automatic generation of image descriptions is leading the field of computer vision and natural language processing-based research. Image captioning is a key task that calls for a semantic understanding of the images and the capacity to create descriptions with right structure. Image captioning is a complex problem as it often demands accessing data that might not be visible in each scene. It will require logical thinking to evaluate or have in-depth knowledge about the object present in an image. In this study, we developed a multilayer Convolutional Neural Network to produce words that describe the images, and we used Long Short-Term Memory to accurately construct relevant sentences out of the words that are produced. To generate an accurate description, the Convolutional Neural Network (CNN) model first compares the targeted image against a huge dataset of training samples. In this study, we have used the Flickr 8k dataset. We have used the Bilingual Evaluation Understudy (BLEU) metric to determine how well our model is generating captions for the images. It evaluates the generated text that has been translated from one language to a different language to evaluate the effectiveness of the machine translation system. In this study, we have also used two pre-trained models (VGG16, and XceptionV3) for comparative study.
Sentiment Analysis of Russo-Ukrainian War using Twitter text Corpus

In this research paper, the focus is predominantly set on the sentiment analysis of a Twitter tweet dataset generated by python script related to the Russia-Ukraine war. The data was analyzed and studied using the RoBERTa model, a deep learning model based on transformers. We classify tweets under this dataset into three parallels: Negative, Positive and Neutral based on the sentiment scores assigned by the model. The findings suggest that the majority of users on Twitter hold negative views towards the conflict, which elucidates the importance of considering the impact of social media on public perception of current events. The trend also follows when we bifurcate the tweets into their respective months. This research paper contributes to the ever-growing field of sentiment analysis and has dwelled deep into the process of how the dataset was generated and how RoBERTa classifies a highly engaged topic consisting of a plethora of sentiments.
Binary Artificial Hummingbird Algorithm: A Binary Version of Artificial Hummingbird Algorithm for Optimization Problems

The Artificial hummingbird algorithm which is given by Mirjalili in 2022 is a swarm-based meta-heuristic technique. This technique shows better results than many classic techniques when compared and tested by the Wilcoxon test the AHA shows better results and has found applications in different real-life problems like in the energy sector. In this work, the binary version of the code for various optimization algorithms provided by various researchers serves as inspiration for developing the binary version of the artificial humming algorithm to solve discrete optimization problems. The binary AHA is evaluated on the benchmark functions and the results are compared with the original AHA algorithm at different dimensions.
Sentiment Analysis on Educational Tweets: A Case of National Education Policy 2020

Due to COVID-19 pandemic lockdowns, the transition from traditional class-room-based approaches, there has been rise in online education. There is a growing need to adopt the best global academic and innovative practices and implement the National Education Policy-2020 (NEP) in Indian education. This study uses a dataset, NEP_Eduset, created by gathering tweets about education. An attempt has been made in this study to examine the tweets by preprocessing, generating labels or sentiments using standard tools and libraries in Python language, applying and comparing various machine learning (ML) algorithms. ML approaches are powerful and used in various applications ranging from sentiment analysis, text analysis, natural language processing (NLP), image processing, object detection. ML methods are widely used in sentiment analysis tasks and text annotations. This work uses Text-Blob, Valence Aware Dictionary for Sentiment Reasoning (VADER), and a Customized method, Senti_NEP to analyze the sentiment score of tweets’ text. Senti_NEP method is shown is produce better results for various experiments conducted for the dataset, NEP_Eduset. Various supervised ML models have been applied for text classification of user sentiment. Word2Vec feature extraction technique has been applied to build and evaluate the models. Performance metrics such as precision, accuracy, F1 score and recall have been used to evaluate the ML models. The results reveal that the support vector machine and random forest classifiers achieve higher accuracy with Word2Vec. The performance results have been compared with VADER, TextBlob and Senti_NEP. It has been found that the Senti_NEP method produces better results.
Mischievous URL Prediction Through Supervised Machine Learning Algorithms

Mischievous URL (Uniform Resource Locator) is a usual ordinary common and one of the critical cybersecurity threats. They are often used in phishing attacks, where attackers trick users into clicking on a link that leads to a fake website designed to steal sensitive information such as login credentials or credit card numbers. Machine learning techniques can be effective in detecting and preventing these types of attacks. By analyzing patterns in the URLs, machine learning models can identify malicious URLs with a high degree of accuracy. To enhance and boost the generalization of mischievous URLs, certain machine learning mastering and acquiring methods and techniques are being investigated and explored with growing attraction and interest in current new, latest, and upcoming years. This paper represents an up-to-date review of all significant algorithms employed for detecting mischievous URLs,out of which Random forest have achieved highest accuracy. Moreover, various issues in formulating different algorithms for detecting mischievous URL problems are discussed.
A Conceptual Comparison between Density-based Clustering algorithms

Data and information have become an integral part of our lives today. The world revolves around data to this abundance of data, and data analytics has become very important. A popular method to gather some useful information from data is clustering. Density-based clustering is a subset of Cluster analysis that groups the components according to the spatial density of the objects. The real-world data available or collected is usually mixed in nature, i.e., is comprised of numerical and categorical data types. The standard algorithms are generally designed to handle only a single data type. Many density-based clustering algorithms are implemented on benchmark heterogeneous datasets and compared in this study. The performance of these algorithms is then evaluated using some benchmark evaluation metrics.
Trusted Charity System Based on Blockchain Access Control

At present, there are widespread problems in traditional centralized charity systems such as unclear information about the flow of funds, centralized data easily tampered with, and data security not guaranteed, so public charity systems need a trusted and decentralized solution. Blockchain has the property of self-trust because of its tamper-proof and traceable nature. It enables mutual trust among stakeholders without the need for third-party proof. The adoption of blockchain technology can solve the trust problem in charity systems and improve the transparency and trust of charitable organizations. To this end, this paper proposes a trusted charity system based on blockchain access control by combining the traceable and tamper-evident characteristics of blockchain with the charity field.
The Action Research of Intelligent Oral Evaluation Interaction and Standard Reading Teaching on Accurate Diagnosis and Improvement of Oral English in Junior Middle School

The change of the new environment for educational development puts forward more stringent requirements for the teaching of oral English. Although English learners in the information age are surrounded by massive learning resources, their oral ability has not been rapidly improved. This is due to the shackles of traditional examination-oriented ideas on the one hand, and the lack of effective and continuous language output by most learners on the other hand. This paper comprehensively uses interviews, questionnaires and tests to carry out a one-month action study on 40 junior high school students, and tries to explore the effectiveness of the interactive and standard reading of oral English assessment based on the platform of speaking freely. The results show that: (1) The students in the class who use the platform of free speech intelligence to assist learning have significantly improved both their reading performance and the ability of situational dialogue; (2) There is no significant difference in performance improvement between men and women; (3) There is a significant negative correlation between the improvement of English performance and the pre-test English performance, so it can be seen that the Changyan Smart Class is more effective for students with lower oral English proficiency. Finally, this paper uses linear regression model to fit the improvement of English level after using smart classroom, and the fitting accuracy reaches 86.7%.
Model Selection and Evaluation for Learning Analytics via Interpretable Machine Learning

Learning analytics is one of the most crucial tasks in understanding the relationship between learners' learning behaviors and academic performance in MOOCs. Machine learning methods that enable us to learn the pattern of complex data make it more effective to conduct learning analytics. However, among a variety of models, only some of them can provide us with sufficient interpretability to understand the modeling result and propose pedagogically instructive suggestions for improving learners' learning strategies. By demonstrating the performance and interpretability of 17 machine learning models, this study would select the CART, which could balance the model performance and interpretability well, as the optimal model for learning analytics in MOOCs. Through the further study of the CART, we concluded that the social interaction behaviors and prompt completion of learning tasks could most significantly help with improving learners' academic performance.
Named Entity Recognition in Fire Control Texts Based on BERT

In order to extract named entities from fire control texts more accurately, a named entity corpus based on fire control related texts is constructed. Aiming at the traditional word vector or word vector can not better represent the context semantics and the disadvantages of recurrent neural network in using GPU parallel computing ability, a named entity recognition model of fire control related texts based on BERT is proposed. The word granularity vector matrix is obtained by using the BERT language model, and the use the IDCNN to abstract features from text. The text dependency on a long-distance of sentences is obtained through MA layer. At last, the global best sequence is extracted through CRF. By analyzing the final experimental findings, it is known that the accuracy, recall and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> value increased 2.78%, 1.59% and 1.33% respectively.
Image Synthesis Using GANs and Diffusion Models

Image synthesis is the creation of new images using existing ones as a reference or source. This can be done using various techniques, such as image manipulation, computer graphics, and deep learning. In Deep Learning, image synthesis can be achieved using Generative Adversarial Networks (GANs), Diffusion Models (DMs), Variational Autoencoders (VAEs), etc. GANs and Diffusion Models are becoming a hot topic in generative models and image synthesis. In this paper, we put forth the recent research on GANs and Diffusion Models for image synthesis. GANs and Diffusion Models have successfully generated high-quality images in various domains, such as faces, animals, landscapes, etc. We then discuss these models’ advantages, disadvantages, and unique features. We get familiar with the general architecture of DMs and GANs for image synthesis and how they can generate realistic-looking images with very high caption similarity. We also touch on Inception Score (IS) and Frechet Inception Distance (FID) and how they are used for ‘ various image synthesis methods as evaluation criteria. We briefly explain why IS and FID should not be the only performance criteria a model should adopt and why human evaluation is also essential for the generated image. This review aims to provide insights into the research on deep learning techniques such as GANs and Diffusion Models.
P2P Traffic Identification under Dual Protocol Stack Based on the C4.5 Decision Tree Algorithm

P2P (peer-to-peer) technology is widely used in the internet, so the research on identifying P2P traffic has always been a hot topic. However, in the past, the identification of P2P traffic was only limited to pure IPv4 networks. In the global trend of promoting the deployment of IPv6, universities are in the stage of transition to an IPv6 network environment, where IPv4 and IPv6 will coexist for a long time. Currently, there is a lack of research on the identification of P2P traffic in this complex dual stack environment. To address the limitations of traditional recognition methods that cannot detect encrypted traffic and manually determine popular feature thresholds, this article adopts the C4.5 decision tree algorithm in machine learning, selecting the proportion of upstream and downstream traffic, the degree of discreteness of packet size, and the length of flow connection as sample features, collecting data traffic in a real university dual stack network environment to identify P2P traffic under both IPv4 and IPv6 links. The experimental results show that this method can effectively identify P2P traffic under IPv4 and IPv6, with an average accuracy of over 95%.
TALCLIP: CLIP for Temporal Action Localization

For regulators of online live streaming and short video platforms, the problem of producing and disseminating soft pornographic videos is becoming serious. For online cultural market supervision, it is a challenging and high-value task to detect and locate soft pornographic temporal regions in videos of live streaming and short video platforms. We present a dataset about Video Frame-Danmaku Dataset containing soft pornographic information based on the Bilibili Danmaku Website. On the task of temporal action localization for soft pornographic video clips, we use the multi-modal model (CLIP) to extract video features and text features. Meanwhile we use the prompt mechanism to convert the danmaku which is splicing similar danmaku in the same release time into labels. Finally, we use a boundary-matching network to get the start and end positions containing soft pornographic information on Bilibili Video-Danmaku Dataset and containing specified action on ActivityNet-1.3.
Metaverse Empowering Practice Teaching of Computer Specialty

Metaverse empowers practice teaching. Exploring new educational scenes and models implies a new learning revolution and educational revolution. Different from the traditional education mode based on textbooks, teachers and classrooms, the metaverse will bring a new mode of space-time learning.
Named Entity Recognition Based on Boundary Enhanced for Chinese Electronic Medical Records

With the continuous popularization of medical informatization, the number of medical documents, medical guides, electronic medical records and other medical texts shows a trend of rapid growth and huge scale. Medical texts contain great social value. However, most medical texts exist in an unstructured form and cannot be directly applied to products. Therefore, how to fully explore the potential valuable information has become a hot research direction in the field of natural language processing. Named entity recognition is the basic task of natural language processing, and also the key task of medical data mining and analysis. It also lays the foundation for downstream tasks such as medical term relationship extraction and medical term standardization. Named entity recognition aims to find some key phrases in a large number of texts, such as people's names, place names, diseases and drugs. At present, named entity recognition has made great achievements in the open field, but there are still many problems in the research of named entity recognition in the medical field, such as the strong professionalism of entities, the long length of some types of entities, and the existence of complex entities mixed in Chinese and English. Aiming at the problem that the length of named entities in existing medical electronic medical records is longer than that in other fields, resulting in low extraction accuracy, a named entity recognition algorithm based on boundary enhancement is proposed. The model adopts the method of multi-task learning, which can effectively alleviate the boundary problem caused by the length of medical terms. We have tried various models on our medical electronic medical record data set. Experimental results show that this method is simple and effective in medical named entity recognition.
Breast Cancer Classification Using Threshold Segmentation and Voting Classification Method

The complex issue before professionals is to compute the mammography in these days, and Computer Aided Diagnosis (CAD) systems are required for this task. The mammography becomes a robust tool for diagnosing the breast cancer. Subsequent to attain the mammograms, the major task is to analyze the images for determining the presence of cancer. However, detecting breast cancer is quite challenging task in machine learning and image processing. Moreover, the morphology of the calcifications plays a significant role in classifying the breast images as benign or cancerous. Various stages are executed for detecting breast cancer. This work enforces Otsu’s segmentation along GLCM algorithm to extract the features. The earlier work has implemented Random Forest (RF) to classify the cancer. This work suggests a Voting classification algorithm to classify the breast cancer accurately. In this algorithm, logistic regression, naive bayes and random forest models are integrated. According to the simulation results, the suggested algorithm has better accuracy, precision and recall than existing methods. The proposed model is compared with the random forest for classifying the breast cancer. The proposed model shows the significant improvements in the results in terms of accuracy, precision and recall.
Research on Improved General Property Graphs Query Method Based on TinkerPop

With the development of artificial intelligence, knowledge graphs, which are an important part of many artificial intelligence technologies, have also begun to receive more and more attention. The current mainstream data models of knowledge graphs mainly include RDF graphs and property graphs. Although property graphs have multiple implementations, they lack a standard query language. This paper proposes an improved general query method for property graphs based on the general graph framework TinkerPop, which can use the standard query language SPARQL of RDF graph to query property graphs. This method improves the original module of TinkerPop and realizes ASK query and path query functions. At the end of the article, this paper verifies the effectiveness of the proposed method by conducting experimental comparisons on real-world air route graph data.