Reviewerly/exHarmony huggingface dataset:
Review Paper: Social Media Sentiment Analysis Using Twitter Dataset

The expansion of social media platforms, particularly Twitter, has transformed them into vast databases of user-generated content expressing a wide range of thoughts and emotions. This review delves into the field of conceptual analysis applied to Twitter datasets, focusing on machine learning. This article provides an overview of methods, techniques, and operational models for sentiment analysis using machine learning in the specific context of Twitter. Introducing the evolution of machine learning applied to the Twitter dataset, starting by exploring the main concepts of sentiment analysis. A variety of supervised and unsupervised learning methods are reviewed, from traditional methods to recent developments including deep learning models. This article discusses the key features and issues associated with this process, considering abbreviations, nuances of user-generated messages, and quality of Twitter content. This research includes a discussion of structure, lexical theory, and the role of pre-learning word embeddings in improving the performance of learning models. Focus on transforming the structure into specific ideas and connecting the data of the points to achieve higher classification. Practical uses of sentiment analysis, including but not limited to understanding public sentiment on Twitter, tracking product sentiment, and finding trends, are a good fit. The review also addresses ethical considerations and issues related to bias in the perspective of analyzing patterns studied in Twitter data. The article concludes by presenting the current state, open challenges and future directions in the Twitter space using machine learning. Combining insights from existing literature, this review provides useful material for researchers, practitioners, and enthusiasts exploring the complexities of adapting to the Twitter environment and analyzing sentiment in power. The widespread reach of social media platforms like Twitter gives them the advantage of rich, original user-generated content that reflects different perspectives and opinions. This in-depth review article aims to explore the dynamic areas of sentiment analysis applied to Twitter datasets, with particular emphasis on the application of different types of machine learning. The narrative turns the complexity of sentiment analysis on its head by delving into the nuances of Twitter's dynamic content and the evolution of machine learning. This article begins with a detailed review of the fundamentals of sentiment analysis and examines the evolution of machine learning techniques adapted to the Twitter dataset. Panoramic research covers many methods, from classical supervised and unsupervised models to deep learning models. The article will take a closer look at the specific challenges encountered in analysing short texts, user-generated messages, and modifying messages on Twitter.
The role of persuasive leadership in reducing job alienation for the opinions of a sample of employees of the Faculty of Administration and Economics / Al-Mustansiriya University

Job alienation is one of the challenges facing the contemporary working environment, which negatively affects individuals and organizations. The role of persuasive leadership here comes to play a vital role in reducing these effects and enhancing employee engagement and commitment. This research deals with the role of persuasive leadership in reducing functional alienation of the opinions of a sample of administrators of the Faculty of Administration and Economics at Al-Mustansiriya University. The methodology was to use a questionnaire to collect data from (34) employees. The results showed that the level of availability of the dimensions of persuasive leadership was moderate from the point of view of employees, as the dimensions of persuasive leadership had a direct impact on reducing job alienation.
Perception of Human Characteristics in an Ai Chatbot Acting as On-the-Job Coach

With the increasingly widespread use of large language models (LLMs) in various applications, understanding the way such models are perceived by their human interlocutors has become more relevant. We explored the possibility of an AI workplace motivational coach using our chatbot HARRi, and whether such a coach would be perceived to have anthropomorphic characteristics such as emotions and empathy. Participants were told to converse with the bot as if they were logging in to work on time or late and were primed by instructions to believe they were either speaking to a chatbot or a human manager. Following the conversation with the chatbot, participants were asked to complete survey questions derived from the Individual Differences in Anthropomorphism Questionnaire (Waytz, Cacioppo, & Epley, 2010). Results indicated that with the exception of the human prime being rated as slightly more friendly, the bot and human prime made little difference in ratings of anthropomorphism. The implications of these findings are discussed.
Cross-Modal Alternating Learning with Task-Aware Representations for Continual Learning

Continual learning is a research field of artificial neural networks to simulate human lifelong learning ability. Although a surge of investigations has achieved considerable performance, most rely only on image modality for incremental image recognition tasks. In this paper, we propose a novel yet effective framework coined cross-modal Alternating Learning with Task-Aware representations (ALTA) to make good use of visual and linguistic modal information and achieve more effective continual learning. To do so, ALTA presents a cross-modal joint learning mechanism that leverages simultaneous learning of image and text representations to provide more effective supervision. And it mitigates forgetting by endowing task-aware representations with continual learning capability. Concurrently, considering the dilemma of stability and plasticity, ALTA proposes a cross-modal alternating learning strategy that alternately learns the task-aware cross-modal representations to match the image-text pairs between tasks better, further enhancing the ability of continual learning. We conduct extensive experiments under various popular image classification benchmarks to demonstrate that our approach achieves state-of-the-art performance. At the same time, systematic ablation studies and visualization analyses validate the effectiveness and rationality of our method. Our code will be available upon publication.
Penumbra2.0

Penumbra2.0 is an AI immersive art installation exploring the visualisation of an unpredictable extreme wildfire scenario using a pyro-aesthetic, an aesthetic based on the perceptual qualities of this fire type. Currently seen in Canada, these wildfires are extreme in their speed and scale. They are unpredictable as their behaviour moves across terrains in unforeseen ways, unlike the linear and predictable paths of bushfires. Using geo-located data, Penumbra2.0 recreates an actual wildfire in the Vosges mountains, France, 2020. It has been collaboratively developed by art, AI and fire researchers.
Named entity recognition of chemical experiment operations based on BERT

Named Entity Recognition (NER) for chemical experiment operations can not only extract key information and automatically generate operation instructions in the field of automated synthesis but also facilitate chemical experiment personnel in analyzing literature data more efficiently. In this paper, we propose a NER model that combines multiple layers of BiLSTM and IDCNN in parallel, based on the Bert pre-trained model. By adjusting the number of BiLSTM and IDCNN modules at each layer, we can extract more contextual information and local feature for different datasets, and subsequently generate entity labels using a Conditional Random Field (CRF) layer. The experimental results indicate that the model achieves an F1 score of 0.9174 in the constructed dataset, surpassing existing algorithms.
Optimization of Python sorting algorithm for CIFAR-10 image classification dataset

In the burgeoning era of deep learning and computer vision, image classification has become paramount across diverse domains. The CIFAR-10 dataset, emblematic in image classification, garners widespread attention from scholars. Yet, the imperative of data preprocessing, especially sorting, cannot be understated, especially for expansive datasets like CIFAR-10. This paper sheds light on a novel optimization technique addressing the snags encountered by traditional Python sorting algorithms with CIFAR-10. Rigorous experimental validations underscore remarkable enhancements in sorting efficacy and precision, paving the way for robust preprocessing in ensuing image classification tasks.
Handwritten formula data enhancement based on Edmonds' algorithm

This paper describes a tree-based decomposition and subexpression exchange method for generating new strategically valid handwritten mathematical expressions (HMEs). This approach uses the Edmonds algorithm to extract a Minimum Spanning Tree (MST) from a directed view and groups input strokes to segment symbols and label spatial relationships between symbols and symbol pairs. By training the HME recognition model and the mathematical Language Model (LM), we aim to improve the recognition performance and generalization ability of existing state-of-the-art methods from both data enhancement and model design perspectives. By adding HMEs of different styles, complexity and difficulty levels in the training dataset in order to increase the adaptability of the system to various forms of expressions.
Vector-circulant matrices and codes over rings

Circulant matrices are of interest on their own as well as for their relevance to several areas of mathematics. In this note vector-circulant matrices over commutative rings are introduced and related results are given. Vector-circulant based additive codes are presented over a particular finite ring.
1 Semantic web technologies: architecture, application, trends, and challenges

Semantic web (SW) and cloud innovation frameworks have been basic parts in making and sending applications in different fields. In spite of the fact that they are independent, they can be joined in different approaches to make arrangements, which has as of late been examined top to bottom. The SW has drawn in a different, however more significantly, local area of analysts, establishments, and organizations conviction that one day the SW will gigantically affect our lives as the current web has. Consequently, there is a great deal of work that has been done around here. This chapter gives an outline of the SW and what has been done so far in the SW field. Then, at that point, it features the current significant difficulties in this field. The association of this chapter is as follows: the initial segment of the chapter examines the SW key components and the spaces that increment the development of the SW. In the second part, we attempt to outline the spaces where SW innovations assume an imperative part. In the third part, we accentuate those areas that go connected at the hip with SW innovations.
Fuzzy Power, Converter for Coin Sorting System Based on Weight, Edge

Nowadays, intelligent robot technology is extensively used in every aspect of human existence and production. To achieve automatic sorting, future development must concentrate on employing intelligent robots instead of humans. Coin sorting robots are an important research object in the field of sorting robots. This paper focuses on a machine vision (MV) sorting especially difficult application, in which the `part' is an Indian coin. The lack of distinguishing characteristics between denominations and the variation in features within a single denomination makes the application difficult in part. Even though coin recognition algorithms have been published in the literature, most applications are evaluated offline using the coins' weights. In the end, it was determined that none of these three methods achieved the desired 95% accuracy at 1000 coins per minute.
Design Information System Sales of Nuts and Bolts at PT. Catur Naga Steelindo

The company is expected to be able to compete with other companies that market the same product and be able to manage the company well. Companies that are just developing need to develop service regulations to sustain companies that can compete in quality and quality. Because looking at a sale will be good if a business or concrete steps taken can reach the target and can be seen from how to move a product, whether in the form of goods or services, from producers to consumers as the target can be said that sales are one of the activities in the process of fulfilling the needs of sellers and buyers, both in cash and on credit, which companies can carry out to maintain their business so that they develop and to get the desired profit or profit. The primary purpose of sales is to bring profit or profit from the products or goods produced by the producers with good management. Conducting sales is an activity aimed at finding buyers, influencing and giving buyers so that purchases can adapt their needs to the products offered and enter into agreements offered and enter into agreements regarding prices that benefit both parties. In general, sellers or entrepreneurs aim to get a sure profit and maintain or even try to increase it for a long time. This goal can be realized if sales can be carried out as planned
Identifying Predictors for Final Diagnosis of Ischemic Events in an Emergency Department Observation Unit (4985)

Sunday, April 26April 14, 2020Free AccessIdentifying Predictors for Final Diagnosis of Ischemic Events in an Emergency Department Observation Unit (4985)Arooshi Kumar, Cen Zhang, Ava Liberman, Koto Ishida, Jose Torres, and Sara RostanskiAuthors Info & AffiliationsApril 14, 2020 issue94 (15_supplement)https://doi.org/10.1212/WNL.94.15_supplement.4985 Letters to the Editor
Robustness Aspects of Optimized Centroids

Abstract Centroids are often used for object localization tasks, supervised segmentation in medical image analysis, or classification in other specific tasks. This paper starts by contributing to the theory of centroids by evaluating the effect of modified illumination on the weighted correlation coefficient. Further, robustness of various centroid-based tools is investigated in experiments related to mouth localization in non-standardized facial images or classification of high-dimensional data in a matched pairs design. The most robust results are obtained if the sparse centroid-based method for supervised learning is accompanied with an intrinsic variable selection. Robustness, sparsity, and energy-efficient computation turn out not to contradict the requirement on the optimal performance of the centroids.
Penalized Model-Based Functional Clustering: A Regularization Approach via Shrinkage Methods

Abstract With the advance of modern technology, and with data being recorded continuously, functional data analysis has gained a lot of popularity in recent years. Working in a mixture model-based framework, we develop a flexible functional clustering technique achieving dimensionality reduction schemes through a L 1 penalization. The proposed procedure results in an integrated modelling approach where shrinkage techniques are applied to enable sparse solutions in both the means and the covariance matrices of the mixture components, while preserving the underlying clustering structure. This leads to an entirely data-driven methodology suitable for simultaneous dimensionality reduction and clustering. Preliminary experimental results, both from simulation and real data, show that the proposed methodology is worth considering within the framework of functional clustering.
Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations

Abstract Nowadays, grocery lists make part of shopping habits of many customers. With the popularity of e-commerce and plethora of products and promotions available on online stores, it can become increasingly difficult for customers to identify. products that both satisfy their needs and represent the best deals overall. In this paper, we present a grocery recommender system based on the use of traditional machine learning methods aiming at assisting customers with creation of their grocery lists on the MyGroceryTour platform which displays weekly grocery deals in Canada. Our recommender system relies on the individual user purchase histories, as well as the available products’ and stores’ features, to constitute intelligent weekly grocery lists. The use of clustering prior to supervised machine learning methods allowed us to identify customers profiles and reduce the choice of potential products of interest for each customer, thus improving the prediction results. The highest average F-score of 0.499 for the considered dataset of 826 Canadian customers was obtained using the Random Forest prediction model which was compared to the Decision Tree, Gradient Boosting Tree, XGBoost, Logistic Regression, Catboost, Support Vector Machine and Naive Bayes models in our study.
On-premise File Server Vs Cloud Storage with Incident Management: A Comparative Study

Many organizations are already shifting their infrastructure and applications to the cloud. Cloud technology is accessing and availing products and services over the internet. The maintenance of cloud technology is often managed by a cloud service provider. A comparison has been made between an existing technology, on-premise file servers, and virtualized file servers in the form of cloud storage to determine the advantages and disadvantages of each filesharing system’s performance. An architectural framework and a simulation of the new cloud storage architecture have been conducted to serve as file storage. Finally, several users that are currently employing the existing on-premise file server technology have participated in user acceptance testing to try the cloud-based storage as a replacement for the file server. The test outcomes proved that end-users were able to execute their regular duties using cloud storage and that they favored it over their current file storage
Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Background An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. Objective This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. Methods We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models’ performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. Results Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting–based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. Conclusions Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.
Maximally Efficient Biphoton Generation by Single Photon Decay in Nonlinear Quantum Photonic Circuits

We develop a general nonperturbative formalism and propose a specific scheme for maximally efficient generation of biphoton states by parametric decay of single photons. We show that the well-known critical coupling concept of integrated optics can be generalized to the nonlinear coupling of quantized photon modes to describe the nonperturbative optimal regime of a single-photon nonlinearity and establish a fundamental upper limit on the nonlinear generation efficiency of quantum-correlated photons, which approaches unity for low enough absorption losses.
Evaluation of distance learning in ukrainian higher media education: Perspectives and impact of stakeholders

The research focuses on the utilization of computer-oriented educational environments for distance learning, aiming to assess the current landscape, highlighting both the positives and negatives, as well as successful examples and future prospects in higher education. Methodologically, the study involved an analysis of various sources, including statistical and narrative information, and conducted surveys among key stakeholder categories to evaluate their perceptions, effectiveness, and the most productive formats of distance learning. The findings challenge the notion that distance education is only suitable for extreme conditions. Key educational process participants, especially in fields like "Journalism," view well-organized distance education as comparable to traditional and extramural learning methods. It enhances the computer technology skills of both teachers and students, proves to be the most inclusive educational format, lessens financial burdens, broadens professional development opportunities, aids in the internationalization of education, and serves as a marketing tool for attracting prospective students. However, the study also reveals an inconsistency between student and faculty expectations regarding remote communication formats and the practical ability of specialists to meet these demands.
DERIVATIONAL SUFFIXES FOUND IN SELECTED ARTICLE IN TEMPO ENGLISH MAGAZINE

This research aims to find out the type of derivational suffixes and the morphological process of derivational suffixes which found in selected article of law in Tempo English Magazine. Descriptive qualitative and quantitative method were used to identify the type and the morphological process of derivational suffixes. There are 59 data were found in three articles of law in Tempo English Magazine which were: derivational suffixes Noun to Adjective 5 data (8,43%) such as: -ic and –an. Derivational suffixes Verb to Noun 31 data (52,54%) such as: -ance, -ation, -er, -ion. Derivational suffixes Adjective to Adverb 16 data (27,12%) such as: -ly. Derivational suffixes Noun to Verb 2 data (3,39%) wich were: -ize, -ate. Derivational suffixes Adjective to Noun 2 data (3,39%) which were: -ity. And derivational suffixes Verb to Adjective 3 data (5,08%) like: -ity. The data that has the most occurrences is derivational suffixes Verb to Noun, while the data that has the fewest occurrence are derivational suffixes Noun to Verb and Adjective to Noun.&#x0D; Keywords: Derivational suffixes; morphology; Tempo English Magazine