[
    {
        "Name": "advanced_cpmv",
        "Title": "Advanced Detection of Copy-Move Forgery: Using Generative and Transformer-based Models for Distinguishing Source and Target Regions in Medical and Digital Imaging",
        "Experiment": "Utilize a CNN-Transformer Generative Adversarial Network (GAN) that combines CNN's local feature extraction with the transformer's global context recognition. This setup will allow the model to generate masks that accurately distinguish source and target regions in forgery, aiming for higher precision in both localization and detection accuracy for digital and medical images.",
        "Interestingness": 7,
        "Feasibility": 4,
        "Novelty": 3,
        "novel": false
    },
    {
        "Name": "deep_cpmv",
        "Title": "Frontiers in Copy-Move Forgery Detection: A Comprehensive Survey of Deep Learning Innovations for Digital and Medical Image Integrity",
        "Experiment": "Conduct an extensive literature review to compile and synthesize recent advancements, particularly in GANs, CNN-Transformer models, and feature fusion techniques for copy-move forgery detection. This survey aims to highlight the evolution from traditional SVM-based techniques to the latest deep learning-driven approaches that improve accuracy and robustness in diverse imaging contexts.",
        "Interestingness": 7,
        "Feasibility": 4,
        "Novelty": 3,
        "novel": false
    },
    {
        "Name": "automatedessayscoring",
        "Title": "Automated Essay Scoring Using Transformer-based Language Models: A Feasibility Study",
        "Experiment": [
            "1. Collect or create a dataset of essays with corresponding human-assigned scores.",
            "2. Preprocess the essays to fit the character-level format required by the model.",
            "3. Modify the training script to include a new loss function that incorporates the essay scores during training.",
            "4. Train the model on the essay dataset.",
            "5. Evaluate the model's ability to generate essays and predict scores by comparing generated essays and scores with human-assigned scores.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for AES."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 6,
        "novel": false
    },
    {
        "Name": "codegenerationandcompletion",
        "Title": "Code Generation and Completion Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Preprocess an existing codebase to fit the character-level format required by the model. This can include Python, JavaScript, or any other programming language.",
            "2. Modify the training script to include a new dataset of code snippets and ensure the model is trained on this dataset.",
            "3. Implement a function to evaluate the model's ability to generate complete code snippets from partial inputs (code completion).",
            "4. Evaluate the model's performance by comparing generated code snippets with human-written code in terms of syntax correctness and functionality.",
            "5. Analyze the results to determine the feasibility and accuracy of the model for code generation and completion tasks.",
            "6. Explore the potential of the model for simple code transformations, such as refactoring or error correction."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": true
    },
    {
        "Name": "sentimentanalysiswithcharleveltransformer",
        "Title": "Sentiment Analysis Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect or create a dataset of text samples with corresponding sentiment labels (positive, negative, neutral).",
            "2. Preprocess the text samples to fit the character-level format required by the model. This includes tokenizing the text into characters and converting them into numerical IDs.",
            "3. Modify the training script to include a new loss function that incorporates the sentiment labels during training. Specifically, use a cross-entropy loss function for classification.",
            "4. Train the model on the sentiment analysis dataset. Use a validation set to monitor the performance and avoid overfitting.",
            "5. Evaluate the model's ability to predict sentiment by comparing the predicted labels with the true labels. Use metrics such as accuracy, precision, recall, and F1-score to assess performance.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for sentiment analysis. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": false
    },
    {
        "Name": "charlevelsummarization",
        "Title": "Character-Level Text Summarization Using Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect or create a small, well-annotated dataset of text documents with corresponding human-generated summaries. Each document should be paired with a short, concise summary.",
            "2. Preprocess the text documents and summaries to fit the character-level format required by the model. This includes tokenizing the text into characters and converting them into numerical IDs.",
            "3. Modify the training script to include a new loss function that incorporates the summary generation task. Specifically, use a sequence-to-sequence (seq2seq) setup where the model is trained to generate summaries from the input documents.",
            "4. Train the model on the summarization dataset. Use a validation set to monitor the performance and avoid overfitting. Implement early stopping based on validation loss.",
            "5. Evaluate the model's ability to generate summaries by comparing the generated summaries with the human-generated summaries. Use metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) to assess performance.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for text summarization. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": true
    },
    {
        "Name": "codeoptimizationusingcharleveltransformer",
        "Title": "Code Optimization Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect or create a dataset of code snippets and their optimized versions. Focus on Python code and include pairs of unoptimized and optimized code snippets that demonstrate performance improvements.",
            "2. Preprocess the code snippets to fit the character-level format required by the model. This includes tokenizing the code into characters and converting them into numerical IDs.",
            "3. Modify the training script to include a new loss function that incorporates the optimization task. Use a sequence-to-sequence (seq2seq) setup where the model is trained to generate optimized code snippets from unoptimized inputs.",
            "4. Train the model on the code optimization dataset. Use a validation set to monitor the performance and avoid overfitting. Implement early stopping based on validation loss.",
            "5. Evaluate the model's ability to generate optimized code by comparing the generated code snippets with the optimized versions in the dataset. Use metrics such as syntax correctness, performance improvements (e.g., execution time, memory usage), and readability to assess the quality of the optimized code.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for code optimization. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": true
    },
    {
        "Name": "styleanddomaingeneration",
        "Title": "Style and Domain-Specific Text Generation Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect or create a dataset of text samples with a specific style or domain. For example, legal documents, medical reports, or dialogues from a specific author.",
            "2. Preprocess the text samples to fit the character-level format required by the model. This includes tokenizing the text into characters and converting them into numerical IDs.",
            "3. Modify the training script to include the new dataset. Ensure the model is trained to capture the specific style or domain characteristics of the text.",
            "4. Implement a function to evaluate the model's ability to generate text that adheres to the specified style or domain. This can be done by comparing generated text with human-written text in terms of style consistency, domain accuracy, and coherence.",
            "5. Evaluate the model's performance by analyzing the generated text for style consistency, domain accuracy, and coherence. Use metrics such as perplexity, human evaluation, and domain-specific accuracy metrics to assess performance.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for style and domain-specific text generation. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": true
    },
    {
        "Name": "codecommentgeneration",
        "Title": "Code Comment Generation Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect a dataset of code snippets with corresponding human-written comments. Focus on a specific programming language, such as Python, and ensure the comments are relevant and descriptive.",
            "2. Preprocess the code snippets and comments to fit the character-level format required by the model. Tokenize the text into characters and convert them into numerical IDs.",
            "3. Modify the training script to include a new loss function for the comment generation task. Use a sequence-to-sequence (seq2seq) setup where the model is trained to generate comments from code snippets.",
            "4. Train the model on the code comment generation dataset. Use a validation set to monitor performance and avoid overfitting. Implement early stopping based on validation loss.",
            "5. Evaluate the model's ability to generate comments by comparing the generated comments with human-written comments. Use metrics such as BLEU score, ROUGE, and human evaluation to assess the quality of the generated comments.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for code comment generation. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": false
    },
    {
        "Name": "sqlquerygeneration",
        "Title": "SQL Query Generation Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect a diverse dataset of SQL queries from open-source projects or databases. Ensure the dataset covers a wide range of SQL query structures and operations to promote generalization.",
            "2. Preprocess the SQL queries to fit the character-level format required by the model. Tokenize the queries into characters and convert them into numerical IDs.",
            "3. Modify the training script to include the new dataset of SQL queries. Ensure the model is trained to generate syntactically correct and semantically meaningful queries.",
            "4. Train the model on the SQL query dataset. Use a validation set to monitor performance and avoid overfitting. Implement early stopping based on validation loss.",
            "5. Evaluate the model's ability to generate SQL queries by comparing the generated queries with human-written queries. Use metrics such as syntax correctness, query execution on a database, and human evaluation to assess the quality of the generated queries.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for SQL query generation. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": true
    },
    {
        "Name": "codedocumentationgeneration",
        "Title": "Code Documentation Generation Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect a dataset of code snippets and their corresponding high-quality documentation. Focus on a specific programming language, such as Python, to ensure consistency.",
            "2. Preprocess the code snippets and documentation to fit the character-level format required by the model. Tokenize the text into characters and convert them into numerical IDs.",
            "3. Modify the training script to include a new loss function for the documentation generation task. Use a sequence-to-sequence (seq2seq) setup where the model is trained to generate documentation from code snippets.",
            "4. Train the model on the code documentation dataset. Use a validation set to monitor performance and avoid overfitting. Implement early stopping based on validation loss.",
            "5. Evaluate the model's ability to generate documentation by comparing the generated documentation with human-written documentation. Use metrics such as BLEU score, ROUGE, and human evaluation to assess the quality of the generated documentation. Specifically, for human evaluation, ask developers to rate the generated documentation based on clarity, accuracy, and usefulness.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for code documentation generation. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 7,
        "Novelty": 7,
        "novel": true
    },
    {
        "Name": "structuredtextgeneration",
        "Title": "Structured Text Generation Using Character-Level Transformers: A Feasibility Study",
        "Experiment": [
            "1. Collect a dataset of structured text files, such as JSON, XML, or YAML. Ensure the dataset covers a variety of structures and complexities to promote generalization.",
            "2. Preprocess the structured text files to fit the character-level format required by the model. This includes tokenizing the text into characters and converting them into numerical IDs.",
            "3. Modify the training script to include the new dataset of structured text files. Ensure the model is trained to generate syntactically correct and semantically meaningful structured text.",
            "4. Train the model on the structured text dataset. Use a validation set to monitor performance and avoid overfitting. Implement early stopping based on validation loss.",
            "5. Evaluate the model's ability to generate structured text by comparing the generated files with human-written files. Use metrics such as syntax correctness, structure validity, and human evaluation to assess the quality of the generated files.",
            "6. Analyze the results to determine the feasibility and accuracy of the model for structured text generation. Identify any patterns or insights that can be drawn from the model's performance.",
            "7. Compare the performance of the character-level model with a word-level model on the same task to highlight any advantages or disadvantages. Use the same evaluation metrics for a fair comparison."
        ],
        "Interestingness": 8,
        "Feasibility": 6,
        "Novelty": 8,
        "novel": true
    }
]