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  Data Trustworthiness in mobile Crowd Sensing With ML

  Vol. 2 No. 08 (2025)

  Abstract

  The project, "Data Trustworthiness in Mobile Crowd Sensing ML," aims to address the critical issue of ensuring the reliability and authenticity of data collected through mobile crowd sensing applications. In the rapidly evolving landscape of sensor-equipped smartphones and ubiquitous connectivity, leveraging the collective intelligence of a crowd for data acquisition has become increasingly popular. However, the inherent challenges of ensuring the trustworthiness of data gathered from diverse sources pose significant obstacles.

  This project focuses on developing robust mechanisms and algorithms to validate and authenticate data in the context of mobile crowd sensing. The research encompasses the design and implementation of stringent data collection protocols, authentication measures, and quality control mechanisms to filter out inaccurate or fraudulent data points. The goal is to enhance the overall reliability of information collected from various contributors.

  In addition to technical aspects, the project emphasizes the importance of creating a transparent and collaborative environment. Privacy-preserving techniques and clear communication regarding data usage policies are integral components to foster trust among contributors. By addressing these aspects, the project aims to establish a framework that ensures the anonymity and privacy of participants while building a foundation of trust in the mobile crowd sensing ecosystem.

  Ultimately, the outcomes of this project are expected to contribute significantly to the advancement of reliable data collection practices in mobile crowd sensing applications, fostering innovation in areas such as environmental monitoring, urban planning, and healthcare.

   

   

  Index Terms

  Mobile Crowd Sensing, Data Trustworthiness, Data Authentication, Data Validation, Reliability, Authenticity, Sensor-equipped Smartphones, Ubiquitous Connectivity, Collective Intelligence, Data Collection Protocols, Quality Control Mechanisms, Fraudulent Data, Privacy-preserving Techniques, Data Usage Policies, Transparency, Collaboration, Privacy, Anonymity, Environmental Monitoring, Urban Planning, Healthcare.

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  Zero-Shot Multilingual Sentiment Analysis Using Transformer-Based Models

  Vol. 2 No. 02 (2025)

  Abstract

  This project aims to explore the feasibility and effectiveness of zero-shot multilingual sentiment analysis using transformer-based models. Traditional sentiment analysis techniques often rely on language-specific models trained on large corpora of labeled data, making them impractical for analyzing sentiments across multiple languages. In contrast, transformer models, such as BERT and GPT, have shown promising results in natural language understanding tasks by leveraging large-scale pre-training and fine-tuning on specific tasks. This project proposes to extend the capabilities of transformer models to perform sentiment analysis across various languages without requiring language-specific training data. The project will involve pre-training a transformer model on multilingual text data and fine-tuning it on sentiment analysis tasks using transfer learning techniques. The effectiveness of the proposed approach will be evaluated on standard benchmark datasets in multiple languages, measuring the accuracy and robustness of sentiment predictions. The outcomes of this project have the potential to significantly enhance the applicability of sentiment analysis tools in multilingual settings, catering to diverse linguistic communities and enabling broader cross-cultural sentiment analysis applications.

  Index terms

  Zero-shot sentiment analysis, Multilingual sentiment analysis ,Transformer-based models ,BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), Natural language understanding, Transfer learning, Pre-training, Fine-tuning, Sentiment analysis tasks, Benchmark datasets, Accuracy measurement, Robustness assessment, Cross-cultural sentiment analysis, Linguistic diversity, Applicability enhancement, Language-specific models, Large corpora, Feasibility study, Effectiveness evaluation.