Reviewerly/exHarmony huggingface dataset:
Supervised probabilistic latent semantic analysis with applications to controversy analysis of legislative bills

Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text analysis technique that models each word in a document as a sample from a mixture of topics. PLSA is the precursor of probabilistic topic models including Latent Dirichlet Allocation (LDA). PLSA, LDA and their numerous extensions have been successfully applied to many text mining and retrieval tasks. One important extension of LDA is supervised LDA (sLDA), which distinguishes itself from most topic models in that it is supervised. However, to the best of our knowledge, no prior work extends PLSA in a similar manner sLDA extends LDA by jointly modeling the contents and the responses of documents. In this paper, we propose supervised PLSA (sPLSA) which can efficiently infer latent topics and their factorized response values from the contents and the responses of documents. The major challenge lies in estimating a document’s topic distribution which is a constrained probability that is dictated by both the content and the response of the document. To tackle this challenge, we introduce an auxiliary variable to transform the constrained optimization problem to an unconstrained optimization problem. This allows us to derive an efficient Expectation and Maximization (EM) algorithm for parameter estimation. Compared to sLDA, sPLSA converges much faster and requires less hyperparameter tuning, while performing similarly on topic modeling and better in response factorization. This makes sPLSA an appealing choice for latent response analysis such as ranking latent topics by their factorized response values. We apply the proposed sPLSA model to analyze the controversy of bills from the United States Congress. We demonstrate the effectiveness of our model by identifying contentious legislative issues.
Teachers’ Evaluation of the Usability of a Self-Assessment Tool for Mobile Learning Integration in the Classroom

This article explores teachers’ perceptions of a self-assessment tool designed to guide the integration of mobile devices into teaching and learning processes. Using the Educational Design Research (EDR) methodology with a quantitative analysis approach, the study sampled 228 teachers across 60 educational institutions at varying levels of education. Participants used the self-assessment tool to gauge their own competencies and identify areas for improvement. They then completed the “System Usability Scale” (SUS) questionnaire, a reliable metric with a 0.96 reliability score commonly used for evaluating educational tools. The results indicate moderate to high acceptance of the self-assessment tool, with an average SUS score of 70.65. The study also reports a Net Promoter Score (NPS) of 22.4, with approximately 43% of teachers as promoters of the tool. Interestingly, usability scores displayed variability among teachers in the early stages of primary education (6–8 years), ranging from 47.50 to 77.50. However, scores improved in more advanced stages, showing an increase of over 40%. The findings suggest that the tool is generally effective and useful for teachers, providing valuable insights for its wider application.
Decision support system for the classification of Downey cells as a pre-diagnostic tool

Abstract Objectives Epstein–Barr virus (EBV) is a member of the herpes virus that causes infectious mononucleosis (IM). Downey cell is the atypical lymphocyte of IM and can be seen in various conditions. Peripheral blood smear (PBS) microscopic evaluation is used to identify Downey cells. A lack of experienced professionals or professional errors may obstruct early and accurate diagnostics for the microscopic evaluation. The main objective of this study is to create a decision support system by digitizing the PBS samples. A general tool providing an inexpensive and measurable solution is envisioned to analyze the PBS samples in detail to give alerting flags to prevent missing Downey cells in manual analysis. Methods The PBS dataset collected was split into Downey positives and negatives. The negative set consisted of 5 leucocyte subtypes. Mantiscope, a cloud-based slide scanner system, was used to collect images from the physical PBS samples. Clinically and laboratory-confirmed 35 IM patients and 124 healthy PBS slides were selected for this procedure. A number of cell counts were obtained after the application of annotation and augmentation methods, and a partially balanced dataset was created for the artificial intelligence (AI) network training. The verification steps included the calculation of sensitivity, specificity, and Cohen’s kappa metrics from the partitioned testing set that was not used during training. A validation process was also performed over the manually identified PBS samples to measure whether the algorithm noticed the samples or not. Results After testing this setup, we have observed 98 % sensitivity and 99 % specificity for Downey cells. According to the validation procedure of Downey positive and negative samples that were carried out by the physicians, a sensitivity of 57 %, specificity of 100 %, and Cohen’s kappa value of 0 . 5 were observed. Besides, the accuracy was found to be 66 % according to the physicians’ evaluations employing the digital images which were identified by Mantiscope, Conclusions Decision support systems can alert the physician for Downey cells and increase the rate of true diagnosis in PBS evaluation. A higher sensitivity and specificity for the detection of Downey cells would be achieved. However, the variance over the dataset is a constraint for effective diagnosis. As the annotation and AI development process continues to collect more data from patients, the model can be updated for future releases.
Enriching Recommender Systems Results with Data about Sustainability and Ethical Standards of Brands

In recommender systems research, not only user preferences but also the sustainability and ethical standards of the services underlying item fruition should be considered to promote virtuous selection decisions. We analyze user interfaces that guide item comparison taking these evaluation criteria into account and we test a synchronized multi-list aimed at raising users' awareness about items by enabling them to (i) sort items according to different evaluation criteria, and (ii) simultaneously view the overall evaluation of an item and its ranking in each criterion. A user study in the fashion domain has shown that the presentation of data about environmental sustainability and ethical standards induces virtuous selection behavior and that participants are more confident in their selections when using our synchronized multi-list user interface than with single-lists.
An Inquirer-Responder Architecture Using LLMs: Emulating Virtual Hearing Q&amp;A in Education

This paper presents an Inquirer-Responder architecture consisting of multiple LLM instances and applies it to a method for automatically generating feedback comments on student reports by emulating virtual hearing Q&A. This method uses faculty education materials and student reports as inputs for multiple LLM instances. In education, quickly returning personalized feedback to each student is crucial but burdensome for faculty. The implementation of a system that automatically and promptly returns feedback on student submissions can improve student learning efficiency and reduce the faculty workload. Our proposed architecture consists of two LLM instances, referred to as inquirer and responder. The inquirer is an LLM instance that reads the material (e.g., lecture materials) provided by the faculty, poses questions, and evaluates the responses from the responder. The responder is an LLM instance that reads student submissions (e.g., reports) and answers questions posed by the inquirer based on the contents of those submissions. This architecture uses faculty materials and student submissions to emulate a virtual hearing, with evaluation based on the knowledge extracted from both the faculty materials and student submissions. This architecture can be applied to various systems that automatically and quickly check submissions and return feedback. In this study, we established a hypothetical use case for a university class and conducted experiments. As a result, there were some cases in which the evaluation was difficult owing to the performance of the current LLM. However, the proposed architecture provides a new perspective on the design of automatic feedback systems and is a step toward their improvement.
Orthogonal Learning Rafflesia Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Wireless Sensor Networks

The Rafflesia Optimization Algorithm (ROA) is an optimization algorithm that mimics the growth cycle of the Rafflesia. Building upon the ROA, this study introduces a novel heuristic algorithm called Orthogonal Learning Quasi-Affine Transformation Evolutionary Rafflesia Optimization Algorithm (OLQROA). The QUATRE method and the Orthogonal Learning approach are combined in the OLQROA algorithm. Compare OLQROA with ROA algorithm, improved ROA algorithms, and other three mature algorithms using CEC2017 benchmark function. The outcomes of the experiments show that OLQROA works better than the aforementioned algorithms. Additionally, OLQROA is used to apply three-dimensional wireless sensor coverage, producing superior results in comparison to the afore-mentioned algorithms.
An Enhanced Analysis of Blood Cancer Prediction Using ANN Sensor-Based Model

Blood cancer diagnosis is a critical medical procedure, yet difficult and expensive for clinical personnel to perform accurately. Artificial neural networks have been shown to be effective in diagnosing a range of diseases, due to their powerful ability to identify and classify patterns in data. Here, we present a study that employed one such ANN to diagnose blood cancer from data gathered from network sensors. First, a sensor network was placed in an animal model to capture various physiological data, including cardiac and respiratory rates, body temperature, and blood pressure. This data was then sent to an ANN which used a classification system based on the type of cancer for diagnostic analysis. Our results showed that the ANN was able to accurately diagnose a blood cancer with an accuracy of 92.1% and that its accuracy improved with the addition of more data. Our study demonstrates that ANNs can be successfully used to accurately diagnose blood cancer using data from network sensors, which could reduce costs and provide faster results in clinical settings.
Social media users' online behavior with regard to the circulation of hate speech

Online hate speech is ripping Ethiopian society apart and threatening the values of democracy, human dignity, and peaceful coexistence. The current study argues that understanding people's responses to hateful posts helps combat hate speech online. Therefore, this study aims to comprehend the roles social media users play in responding to online hate speech. To this end, 14 ethnic-based hate speech posts each with more than 1,000 comments were collected from the public space of four purposefully selected YouTube news channels and four Facebook accounts, which are considered as hot spots for the circulation of hate speech during data collection period. Then, 100 random comments were collected from each hate speech post using “ exportcomment.com ” which automatically extract comments from social media posts in excel format. After extracting a total of 1,400 random comments, 460 of them were removed because they were found irrelevant and unclear to be coded and analyzed. Then, inductive coding was employed to identify, refine, and name codes and themes that describe the main roles played by social media users in reacting to the hate speeches. The findings showed five major roles social media users play in responding to hatful contents: trolling, pace-making, peace-making, informing, and guarding. The paper discusses the findings and provides recommendations deemed necessary to counter online hate speeches.
Automated Seafloor Massive Sulfide Detection Through Integrated Image Segmentation and Geophysical Data Analysis: Revisiting the TAG Hydrothermal Field

Abstract Accessible seafloor minerals located near mid‐ocean ridges are noticed to mitigate the projected metal demands of the net‐zero energy transition, promoting growing interest in quantifying the global distributions of seafloor massive sulfides (SMS). Mineral potentials are commonly estimated using geophysical and geological data that lastly rely on additional confirmation studies using sparsely available, locally limited, seafloor imagery, grab samples, and coring data. This raises the challenge of linking in situ confirmation data to geophysical data acquired at disparate spatial scales to obtain quantitative mineral predictions. Although multivariate data sets for marine mineral research are incessantly acquired, robust, integrative data analysis requires cumbersome workflows and experienced interpreters. We introduce an automated two‐step machine learning approach that integrates the mound detection through image segmentation with geophysical data. SMS predictors are subsequently clustered into distinct classes to infer marine mineral potentials that help guide future exploration. The automated workflow employs a U‐Net convolutional neural network to identify mound structures in bathymetry data and distinguishes different mound classes through the classification of mound architectures and magnetic signatures. Finally, controlled source electromagnetic data are utilized together with in situ sampling data to reassess predictions of potential SMS volumes. Our study focuses on the Trans‐Atlantic Geotraverse area, which is among the most explored SMS areas worldwide and includes 15 known SMS sites. The automated workflow classifies 14 of the 15 known mounds as exploration targets of either high or medium priority. This reduces the exploration area to less than 7% of the original survey area from 49 to 3.1 km 2 .
Event-Based Data Pipelines in Recommender Systems: The Data Engineering Perspective

Recommender Systems (RS) are information retrieval systems that can be used for serving personalized content to online users. Most industrial recommendation systems utilize a large amount of online data to generate personalized recommendations for users. The quality of the data plays an important role in the performance of the RS. The majority of the RS data is generated from event data that are stored in data lakes through multiple data pipelines. Event-based data pipelines have emerged as a popular approach to handle the massive amount of data generated by modern applications. In this paper, we explore the impact of event-based data pipelines on recommendation systems. We discuss how these pipelines enable efficient data ingestion, real-time processing, and low-latency recommendations.
Augmented Reality

This chapter discusses the possibilities offered by augmented reality (AR) along the customer journey for the development of successful customer experiences. Augmented reality enables the superimposition of virtual elements to the users’ body features and surrounding environments, thus enhancing the reality they are currently inhabiting and the products or services they are experiencing. After defining what augmented reality is, we distinguish between the different applications of AR in mobile, online, and in store contexts. We then discuss and review the literature on how augmented reality is applied in the pre-purchase and purchase stage of the customer journey, specifically highlighting its impact in terms of advertising and retailing. Finally, we draw some conclusions and suggest possibilities for future research.
Evaluation of a snip pruning method for a state-of-the-art face detection model

With rapid development of machine learning and subsequently deep learning, deep neural networks achieved remarkable results in solving various tasks. However, with increasing the accuracy of trained models, new architectures of neural networks present new challenges as they require significant amount of computing power for training and inference. This paper aims to review existing approaches to reducing computational power and training time of the neural network, evaluate and improve one of existing pruning methods for a face detection model. Obtained results show that the presented method can eliminate 69% of parameters while accuracy being declined only by 1.4%, which can be further improved to 0.7% by excluding context network modules from the pruning method.
AI-powered Self-service Technologies

This chapter provides an overview of how artificial intelligence has reshaped the concept of self-service technologies (SSTs). Specifically, this chapter focuses on the latest developments in no-contact technologies enabled by artificial intelligence and employed in store, namely checkout-free and just walk out technologies. These AI-powered self-service solutions substantially simplify purchases in store by removing the need to go through a lengthy payment process with a human employee or a traditional self-service machine. Therefore, they enable a frictionless and seamless purchase process by revolutionising one key component of the purchase stage of the customer journey. As these AI applications are still underexplored in the literature and little is known about their effect on consumer behaviour, we discuss potential avenues that future research could explore to deepen our understanding of these tools.
Domain-Specific Sentiment Analysis of Tweets Using Machine Learning Methods

Most general sentiment analysers degrade quality when tested on Tweets in the broadcast domain. This domain covers both radio and television broadcast. This paper proposes domain-specific data in the broadcast domain. Furthermore, it proposes the use of machine learning methods for the sentiment analysis of tweets in this domain. Data were collected from Twitter using Twitter application programming interfaces. The data were preprocessed, and most special characters and emoticons were not removed, as sentiment analysis involves the use of opinions and emotions which are expressed using emoticons and other characters. The data were automatically labelled using a pre-trained sentiment analyser to enable the use of supervised learning on the data. Two supervised machine learning methods, such as XGBoost and multinomial logistic regression (MLR), are trained and evaluated on the data. The performance of the models was affected by two factors; limited data and the use of a general sentiment analyser to label the data in a specific domain.
A Mathematica Function to Get a List of Random Inequalities and Their Respective Solution Sets

Solving inequalities is a first step and an important achievement for undergraduate students. In fact, it is one of the first subjects studied in most of the classical courses of higher basic mathematics. This paper presents a new function, GenerateInequalitiesList, coded in the Wolfram programming language, which is typical of Mathematica software, to generate a random list of inequalities with their respective solutions. To encode the program, use is mainly made of the native functions RandomInteger, RandomChoice, Reduce, and Surd; as well as the paradigms of functional programming and programming based on rules and patterns (typical of symbolic programming languages). The list of inequalities obtained can be used by teachers and/or students. The former is to save time in preparing exercises and the latter to generate practice material. The problem of the high license price of Mathematica can be overcome by assembling a Raspberry Pi computer and installing the free version of Mathematica for Raspbian.
Deep Learning Model for the Recognition of Its Environment of an Intelligent System

This research project consisted in the design of an Artificial Neural Network with Deep Learning for an intelligent system, they were installed twelve sensors ultrasonic HC-SR04, which ones detected and learned all kinds of obstacles, such as: walls, tables, chairs and others. The methodology used was the concurrent design has five phases: the first the conceptual plan was carried out, the second a kinematic study, the third a dynamic study, the fourth a mechanical project and finally the simulation of the system. Artificial Neural Networks were designed with Deep Learning and trained with the Backpropagation algorithm. The ANN was programmed and recorded in the Arduino Mega 2560 module. All the corresponding simulations were carried out, it was verified that the ultrasonic sensors have sent the signal to the Artificial Neural Network with deep learning and they carried out a learning of their environment, they were also checked the displacement of the mobile robot resulting in the desired performance. In conclusion, the proposed design was achieved and it was simulated with all kinds of events, in addition it was verified that Artificial Neural Networks with deep learning can detect and learn from all the obstacles that are in their environment.
PEMODELAN TOPIK TERKAIT BANJIR PADA TWITTER DENGAN MENGGUNAKAN LATENT DIRICHLET ALLOCATION

In this background discusses the topic of tweet about Flooding on Twitter using the keyword "Flood". Tweet data was taken from June 1, 2021 to June 2, 2021 with the number of tweet data obtained, which was 2000 tweets. The number of tweets related to flooding has not been analyzed so that the topics contained in it are not yet known. Research . Modeling topics related to floods in Indonesia on Twitter social media with the LDA method. Research. This study uses experimental methods with several variables to test hypotheses. Then the data is processed with stages, namely web data extraction, preprocessing, feature extraction, topic modeling using latent dirichlet allocation algorithms, visualization, and analysis. Research. The results of the topic coherence stage were carried out a search for the most optimal topic from the 20 topics that had been determined at the beginning. The results of topic coherence for 20 topics concluded that for topic 10 it has a total topic value of 0.41 and has an ideal topic modeling result and is in accordance with the provisions. Conclusion : Based on the results of the discussion of topic coherence, it can be concluded that the most ideal number of topics is topic 10 because it has the highest value compared to other topics. The advice here is to be able to display or get flood information in Indonesia in real time and accurately.
Precise localization of corneal reflections in eye images using deep learning trained on synthetic data

We present a deep learning method for accurately localizing the center of a single corneal reflection (CR) in an eye image. Unlike previous approaches, we use a convolutional neural network (CNN) that was trained solely using synthetic data. Using only synthetic data has the benefit of completely sidestepping the time-consuming process of manual annotation that is required for supervised training on real eye images. To systematically evaluate the accuracy of our method, we first tested it on images with synthetic CRs placed on different backgrounds and embedded in varying levels of noise. Second, we tested the method on two datasets consisting of high-quality videos captured from real eyes. Our method outperformed state-of-the-art algorithmic methods on real eye images with a 3-41.5% reduction in terms of spatial precision across data sets, and performed on par with state-of-the-art on synthetic images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem, which is one of the important common roadblocks in the development of deep learning models for gaze estimation. Due to the superior CR center localization and ease of application, our method has the potential to improve the accuracy and precision of CR-based eye trackers.