Machine Learning
Resource usage fuzzing samples and related data. Contains samples from Pythoin, random data, GPT-3.5, GPT-4, Gemini-1.0, Claude Instant, and Claude Opus. These samples are generated for 50 Python functions. Also included are resource measures for CPU time, instruction count, function calls, peak RAM usage, final RAM allocated, and coverage. These values were collected on an isolated system and account for interference from other processes.
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The limited availability of Guitar notes datasets hinders the training of any artificial intelligence model in this field. TaptoTab dataset aims to fill this gap by providing a collection of notes recordings. This dataset is collected as part of an honours project at the Faculty of Computer and Information Sciences, Ain Shams University. The dataset is composed of audio data that has been self-collected, focusing on capturing a comprehensive range of guitar notes. The dataset consists of recordings of guitar notes played on each of the six strings, covering up to the 12th fret.
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The data in this dataset is the experimental data related to the article named Privacy-preserving approach to edge federated learning based on blockchain and fully homomorphic encryption , which contains data such as running time comparison, communication spend comparison, encryption and decryption time comparison, accuracy comparison, etc.
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Brain-Computer Interface (BCI) technology facilitates a direct connection between the brain and external devices by interpreting neural signals. It is critical to have datasets that contain patient's native languages while developing BCI-based solutions for neurological disorders. However, present BCI research lacks appropriate language-specific datasets, particularly for languages such as Telugu, which is spoken by more than 90 million people in India.
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This study investigates the integration of artificial intelligence (AI) to enhance endpoint management solutions. The research explores AI's impact on security, efficiency, and compliance within enterprise environments (R1). Through case studies and empirical analysis, the paper highlights the benefits and challenges of such integrations, offering insights into future developments.
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The "Multilabel Extremism Classification Tweets Dataset" dataset contains user comments annotated with labels including toxic, severe toxic, obscene, threat, insult, and identity hate. Designed for multi-label classification, this dataset is valuable for researchers focused on detecting online extremism and toxicity across multiple languages. It enables the development of NLP models for content moderation, hate speech detection, and extremism identification.
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The "Multi-Label Extremism and Jihadism Classification Tweets Dataset" dataset is a multilingual resource designed for multi-label classification of online extremism and toxic behavior, including extremism and jihadism. Each comment is annotated with labels indicating the presence of various extremism traits: toxic, severe toxic, obscenity, threats, insults, identity hate, and jihadi content.
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The Protection of Children from Sexual Offences (POCSO) Act was an important legislation that was enacted in India in 2012. It aims to safeguard children from sexual exploitation through various enforcement and legal redressal mechanisms. This dataset has been scraped from eCourts India Services using Python script which uses Selenium. We have mined apex and high courts’ judgements, which mentioned the POCSO Act and its respective sections. We have chronologically scraped POCSO judgements from 2012 to 2020 in the corpus.
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Measuring and assessing intelligence level in children and adolescents is crucial for monitoring their developmental progress, identifying intellectual disabilities, and implementing early interventions. To date, there is no digital and simplified tool specifically designed to evaluate whether intelligence is normal or abnormal in these age stages.
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