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Multifaceted Disparities Associated with Translator Earnings: A Quantitative Study of Upwork Profiles

Journal of Social Computing 2024年第1期5卷 88-100页

作者： Tyler Horan Data Analytics and Computational Social Science Program University of Massachusetts AmherstAmherstMA 01003USA

This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation *** a dataset comprising 45000 translator profiles,the study focusses on delineating disparities correlated with demographic variables such as age,gender,home country wealth,and language *** study uses a random forest regression model to delineate the complex interaction between gender,the economic standing of a translator’s domicile country,age,and linguistic proficiency,as they relate to *** findings substantiate and,in many ways,extend existing qualitative and anecdotal evidence that has shaped the discourse in this *** rigorous empirical framework employed here can be replicated or adapted to study other sectors within the gig economy,thus contributing to a more comprehensive understanding of labour dynamics in the digital age.

关键词： translation online employment gig economy intercultural communication

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Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media 23

Leveraging LLMs for Integrated Sentiment and Topic Analysis ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sibitenda, Harriet Hu, Ruofan Rundensteiner, Elke Diattara, Awa Traore, Assitan Ba, Cheikh University of Gaston Berger Computer Science Department Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester United States Data Science Program Worcester Polytechnic Institute Worcester United States Actroll Data Science Department France

ISBN: (纸本)9798350374889

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with 'Poverty' achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions. © 2024 IEEE.

关键词： Tweets

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Hyperbolic Metric Learning Embedding for User Behavior Sequence

Hyperbolic Metric Learning Embedding for User Behavior Seque...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Lin, Tse-Yu National Taiwan University Data Science Degree Program Taipei Taiwan

ISBN: (纸本)9798350324136

The sequential behavior history is an important data in the field of consumer electronics, A common way is to model all browser history as a graph, thus, each sequence of user behavior can be view as a walk on the certain graph. Further, we could model individual user's historical behavior as a tree sub-graph. Recently, tree-structure data is widely studied, and various hyperbolic metric learning methods are proposed to find a nice representation into a hyperbolic space for the usage of downstream tasks. In this work, we propose a framework to deal with sequential behavior history defined by tags of website pages, then apply hyperbolic metric learning models to obtain an appropriate representation each user. © 2024 IEEE.

关键词： hierarchical representations Learning hyperbolic metric learning user behavior

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Utilization of K-Means Clustering to Examine Library User Segmentation's Impact on Student Graduation Rates 2

Utilization of K-Means Clustering to Examine Library User Se...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Ritonga, Ihdi Syahputra Candra, Ade Budiman, Mohammad Andri Universitas Sumatera Utara Data Science and Artificial Intelligence Program Medan Indonesia

ISBN: (纸本)9798350351613

This research aims to optimize library services through user segmentation analysis and to determine the correlation between user activities and student graduation levels using the K-Means clustering algorithm. By analyzing this relationship, the study seeks to enhance both library service effectiveness and student academic success. The methodology involves several stages: data collection, preprocessing (including data merging and cleaning), and data modeling. The K-Means clustering method is specifically used to segment library users based on various factors and to identify patterns that correlate with student graduation levels. The dataset comprises circulation data and student graduation data from the UIN Padangsidimpuan Academic Information System (SIAKAD), including variables such as study duration, Grade Point Average (GPA), and loan levels. The analysis resulted in the identification of three distinct clusters: Cluster 0, Cluster 1, and Cluster 2. These clusters are characterized by different lengths of study, GPA, and library loan levels, providing insights into the preferences and needs of library users. The findings suggest that tailored library services, informed by these clusters, can potentially enhance student engagement and academic performance. For example, libraries could develop specialized discussion services related to student research, thereby improving thesis quality and research outcomes. Additionally, optimizing digital-based library services can further support student success. © 2024 IEEE.

关键词： Students

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Automated Adversarial-Attack Removal with SafetyNet Using ADGIT 10

Automated Adversarial-Attack Removal with SafetyNet Using AD...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Halim, Steven Anandi, Arvio Devin, Vyos Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step. © 2024 IEEE.

关键词： Adversarial machine learning

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Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity 7

Cosmetics Recommendation System Based on Ingredients Using C...

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7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Alisha Firdaus, Sekar Dwi Nurmuthia, Laksmi Imtiyaz, Raina Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365351

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare. © 2024 IEEE.

关键词： Skin care products

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Some granular structures for weighted digraphs

Some granular structures for weighted digraphs

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lin, Tse-Yu National Taiwan University Data Science Degree Program Taiwan

ISBN: (纸本)9798350324174

Graphs are non-Euclidean data structures used to model interaction relationships between abstract objects. When additional structures, such as graph orientation, weighted vertices, or weight functions on edges, are imposed on the graph, additional graph properties can be applied to discover hidden patterns within the structure of the graph. In this paper, we discuss possible granular structures for graphs, with a focus on granular structure for edge-weighted digraphs derived from pairwise comparison problems. These structures have been applied in various fields such as recommender systems, graph databases, and social network analysis. © 2023 IEEE.

关键词： Graph databases

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Uncovering Sentiments Expressed on GPT in Academic Papers 17

Uncovering Sentiments Expressed on GPT in Academic Papers

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Hong, Alicia Nwoji, Stanley C Murungi, Joyce K. Tatikonda, Pavan K. Kaluvakolli, Anandhakumar Harrisburg University of Science and Technology Data Science Program HarrisburgPA United States

ISBN: (纸本)9798350381320

The growing hype surrounding GPT models has generated both fear and excitement, with concerns about job replacement and admiration for its capabilities. This study investigated the prevailing sentiments within the academic field. The paper aimed to conduct a more comprehensive and objective analysis focusing on academia. We conducted sentiment analysis on a corpus of both peer-reviewed and preprinted article abstracts published between January 2022 and March 2023 to determine the early prevailing sentiments toward GPT models. We collected and processed 400+academic papers on GPT models, extracting the abstracts and keywords to gain insights into authors' perspectives. The study focused on identifying these articles' positive, negative, and neutral sentiments. The study considered various approaches, including RoBERTa, and traditional Machine Learning models such as Naïve Bayes, Random Forest, and Support Vector Machine, to analyze the collected data and compare performance results. The results demonstrated that the predominant sentiments expressed in scholarly paper abstracts toward GPT models are neutral (60.2% of the sample), instead of polarized. This observation holds even when the confidence score of the model output is limited to $\gt0.5$. The significance of this study lies in its novelty, as limited articles have examined these sentiments. Understanding the various sentiments expressed in scholarly discourse on GPT models can contribute to further research on the ethical implications of generative AI. © 2023 IEEE.

关键词： Sentiment analysis

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical Engineering and Computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias 9th

Variational quantum neural network with optimized ansatz for...

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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Dominic, Nicholas Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases. © 2024 The Authors.

关键词： Quantum computers

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