title: Prompt-GAN – Customisable hate speech and extremist datasets via radicalised neural language models

authors: Govers, Jarod

subjects: Artificial Intelligence

abstract: Online hate speech and violent extremism knows no borders, no political boundaries, no remorse. Researchers face an uphill battle to collect hate speech data in volumes and topical diversity suitable for training state-of-the art content-moderation systems. Neural language models ushered in a new era of synthetic data generation in use across various businesses, all despite calls for research to protect against unintended toxic output. We present a method for radicalising pre-trained neural language models to identify real online hate speech, as well as present the risks of rouge radicalised AI bots which could undermine our trust in social media. We present Prompt-GAN, a prompt-tuning adversarial approach with three achievements. We demonstrate prompt-tuning’s ability to generate realistic types of hate and non-hate speech which mimics political extremist discourse. Prompt-GAN’s architecture offers a twofold reduction in memory and runtime requirements compared to fine-tuning. Finally, Prompt-GAN improves hate speech classification F1-scores by up to 10.1% and sets a new record in neural language simulation compared to the current state-of-the-art across three benchmark datasets.

text: Prompt-GAN – Customisable hate speech and extremist datasets via radicalised neural language models Online hate speech and violent extremism knows no borders, no political boundaries, no remorse. Researchers face an uphill battle to collect hate speech data in volumes and topical diversity suitable for training state-of-the art content-moderation systems. Neural language models ushered in a new era of synthetic data generation in use across various businesses, all despite calls for research to protect against unintended toxic output. We present a method for radicalising pre-trained neural language models to identify real online hate speech, as well as present the risks of rouge radicalised AI bots which could undermine our trust in social media. We present Prompt-GAN, a prompt-tuning adversarial approach with three achievements. We demonstrate prompt-tuning’s ability to generate realistic types of hate and non-hate speech which mimics political extremist discourse. Prompt-GAN’s architecture offers a twofold reduction in memory and runtime requirements compared to fine-tuning. Finally, Prompt-GAN improves hate speech classification F1-scores by up to 10.1% and sets a new record in neural language simulation compared to the current state-of-the-art across three benchmark datasets.

year: 2022

title: Extremely fast decision tree mining for evolving data streams

authors: Bifet, Albert

subjects: computer science

abstract: Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning.
In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. STREAMDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources.

text: Extremely fast decision tree mining for evolving data streams Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning.
In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. STREAMDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources.

year: 2017

title: What is the difference? What factors contribute to the variation of achievement for Year 7 and 8 Māori students in high decile, full primary schools?

authors: McLocklan, Sue

subjects: Maori student achievement

abstract: The educational disparity between indigenous Māori students and those of the majority continue to be a major issue in New Zealand. Attempting to bridge the gap for Māori students has generated focus on Māori student engagement in the classroom, the actions and understanding of the teacher and the relationship and interactions between the teacher and students in their learning environment. Elements of culturally responsive pedagogy have been identified as positive contributing factors to accelerating academic achievement for secondary Māori mainstream students. 
This small-scale cross-cultural exploratory qualitative research project seeks to identify the factors that contribute to student achievement variation for Year 7 and 8 students in high decile full primary schools and the integration of teacher’s cultural competencies in high quality learning opportunities. Within a kaupapa Māori research process, this study uses empirical data collected from a purposive sample through a semi-structured individual interview and a for self-assessment. Voluntary research participants were twelve students and six teachers in three schools in the Bay of Plenty, Waikato and Wellington regions. This research examines the engagement of student and teacher in pedagogical practice, learning variables, cultural knowledge and practices attributable to positive academic achievement for Māori. 
The findings reveal that when teachers place themselves in an agentic position; adopt a professional commitment and willingness to engage in effective relations, interactions and reciprocal practices to support Māori learners, high levels of relational trust and expectations prevail through mutual respect and interdependence to attain successful educational outcomes.
Further teacher professional learning is essential to enlighten and deepen teachers' understanding of culturally responsive pedagogy to enable Māori learners to reach their potential to participate and contribute in their world of choice.

text: What is the difference? What factors contribute to the variation of achievement for Year 7 and 8 Māori students in high decile, full primary schools? The educational disparity between indigenous Māori students and those of the majority continue to be a major issue in New Zealand. Attempting to bridge the gap for Māori students has generated focus on Māori student engagement in the classroom, the actions and understanding of the teacher and the relationship and interactions between the teacher and students in their learning environment. Elements of culturally responsive pedagogy have been identified as positive contributing factors to accelerating academic achievement for secondary Māori mainstream students. 
This small-scale cross-cultural exploratory qualitative research project seeks to identify the factors that contribute to student achievement variation for Year 7 and 8 students in high decile full primary schools and the integration of teacher’s cultural competencies in high quality learning opportunities. Within a kaupapa Māori research process, this study uses empirical data collected from a purposive sample through a semi-structured individual interview and a for self-assessment. Voluntary research participants were twelve students and six teachers in three schools in the Bay of Plenty, Waikato and Wellington regions. This research examines the engagement of student and teacher in pedagogical practice, learning variables, cultural knowledge and practices attributable to positive academic achievement for Māori. 
The findings reveal that when teachers place themselves in an agentic position; adopt a professional commitment and willingness to engage in effective relations, interactions and reciprocal practices to support Māori learners, high levels of relational trust and expectations prevail through mutual respect and interdependence to attain successful educational outcomes.
Further teacher professional learning is essential to enlighten and deepen teachers' understanding of culturally responsive pedagogy to enable Māori learners to reach their potential to participate and contribute in their world of choice.

year: 2013