We argue that this assumption is rarely verified in practice, as the recommendation process itself may impact the user's

This paper focuses on the development of an advanced intelligent article scoring system that not only assesses the overall quality of written work but also offers detailed feature-based scoring tailored to various article genres. By integrating the pre-trained BERT model with the large language model Chat-GPT, the system gains a deep understanding of both the content and structure of the text, enabling it to provide a thorough evaluation along with targeted suggestions for improvement. Experimental results demonstrate that this system outperforms traditional scoring methods across multiple public datasets, particularly in feature-based assessments, offering a more accurate reflection of the quality of different article types. Moreover, the system generates personalized feedback to assist users in enhancing their writing skills, underscoring the potential and practical value of automated scoring technologies in educational contexts.

Naija is the Nigerian-Pidgin spoken by approx. 120M speakers in Nigeria and it is a mixed language (e.g., English, Portuguese and Indigenous languages). Although it has mainly been a spoken language until recently, there are currently two written genres (BBC and Wikipedia) in Naija. Through statistical analyses and Machine Translation experiments, we prove that these two genres do not represent each other (i.e., there are linguistic differences in word order and vocabulary) and Generative AI operates only based on Naija written in the BBC genre. In other words, Naija written in Wikipedia genre is not represented in Generative AI.

Entities like person, location, organization are important for literary text analysis. The lack of annotated data hinders the progress of named entity recognition (NER) in literary domain. To promote the research of literary NER, we build the largest multi-genre literary NER corpus containing 263,135 entities in 105,851 sentences from 260 online Chinese novels spanning 13 different genres. Based on the corpus, we investigate characteristics of entities from different genres. We propose several baseline NER models and conduct cross-genre and cross-domain experiments. Experimental results show that genre difference significantly impact NER performance though not as much as domain difference like literary domain and news domain. Compared with NER in news domain, literary NER still needs much improvement and the Out-of-Vocabulary (OOV) problem is more challenging due to the high variety of entities in literary works.

Music is the most popular art form that is performed and listened to by billions of people every day. There are many genres of music such as pop, classical, jazz, folk etc. Each genre has different music instruments, tone, rhythm, beats, flow etc. Digital music and online streaming have become very popular these days due to the increase in the number of users. To create a machine learning model, which classifies music samples into different genres. To classify a music sample or song manually, the person has to listen to the song and select the genre.

There is a head-scratching moment at the beginning of the popular farming simulator video game Stardew Valley, where you wonder, "What now?" Newly installed on your late grandfather's dilapidated farm, you're given no instructions on how to turn the business's fortunes or what to explore in the neighbouring town. This conundrum fills Felix Barrett with glee. As the founder and creative director of British theatre company Punchdrunk, he has spent 19 years turning warehouses into vast worlds that audiences must learn to explore alone. From Woyzeck to Faust, Punchdrunk transforms classic plays into sprawling, interactive experiences. The idea is this: traditional theatre shows are passive affairs where you watch a distant stage from the comfort of a chair - but a Punchdrunk show is active, mysterious, and places you inside a fiction you can touch, smell, and even taste.

A common assumption in recommender systems (RS) is the existence of a best fixed recommendation strategy. Such strategy may be simple and work at the item level (e.g., in multi-armed bandit it is assumed one best fixed arm/item exists) or implement more sophisticated RS (e.g., the objective of A/B testing is to find the best fixed RS and execute it thereafter). We argue that this assumption is rarely verified in practice, as the recommendation process itself may impact the user’s preferences. For instance, a user may get bored by a strategy, while she may gain interest again, if enough time passed since the last time that strategy was used. In this case, a better approach consists in alternating different solutions at the right frequency to fully exploit their potential. In this paper, we first cast the problem as a Markov decision process, where the rewards are a linear function of the recent history of actions, and we show that a policy considering the long-term influence of the recommendations may outperform both fixed-action and contextual greedy policies. We then introduce an extension of the UCRL algorithm ( L IN UCRL ) to effectively balance exploration and exploitation in an unknown environment, and we derive a regret bound that is independent of the number of states. Finally, we empirically validate the model assumptions and the algorithm in a number of realistic scenarios.

Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. However, it's still below the 70% accuracy that humans could achieve in the same task. Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. The method works by training a simple convolutional neural network (CNN) to classify a short segment of the music signal. Then, the genre of a music is determined by splitting it into short segments and then combining CNN's predictions from all short segments. After training, this method achieves human-level (70%) accuracy and the filters learned in the CNN resemble the spectrotemporal receptive field (STRF) in the auditory system.

Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions into more than 100 types. The type set can span various domains including biomedical (e.g., disease, gene), sport (e.g., sports event, sports player), religion and mythology (e.g., religion, god) and entertainment (e.g., movies, music). Most of the existing literature for Entity Recognition (ER) focuses on coarse-grained entity recognition (CgER), i.e., recognition of entities belonging to few types such as person, location and organization. In the past two decades, several manually annotated datasets spanning different genre of texts were created to facilitate the development and evaluation of CgER systems (Nadeau and Sekine 2007). The state-of-the-art CgER systems use supervised statistical learning models trained on manually annotated datasets (Ma and Hovy 2016). In contrast, FgER systems are yet to match the performance level of CgER systems. There are two major challenges associated with failure of FgER systems. First, manually annotating a large-scale multi-genre training data for FgER task is expensive, time-consuming and error-prone. Note that, a human-annotator will have to choose a subset of types from a large set of types and types for the same entity might differ in sentences based on the contextual information. Second, supervised statistical learning models when trained on automatically generated noisy training data fits to noise, impacting the model’s performance. The objective of my thesis is to create a FgER system by exploring an off the beaten path which can eliminate the need for manually annotating large-scale multi-genre training dataset. The path includes: (1) automatically generating a large-scale single-genre training dataset, (2) noise-aware learning models that learn better in noisy datasets, and (3) use of knowledge transfer approaches to adapt FgER system to different genres of text.

Your brain may be the best predictor for whether or not you're a musician, a new study finds. Researchers at Finland's Aarhus University used functional magnetic resonance imaging (fMRI) scans to capture images of the brain activity of 18 musicians and 18 non-musicians while they listened to different genres of music. The images revealed that certain brain areas are better predictors for whether we are musically talented: specifically, the frontal and temporal areas of the brain's right hemisphere. Researchers at Finland's Aarhus University used functional magnetic resonance imaging (fMRI) scans to capture images of the brain activity of 18 musicians and 18 non-musicians while they listened to different genres of music The scientists used six varieties of music during the test, including ones representing timbre and rhythm or tonality. Tonality and pulse are the best predictors of musicianship, the scientists noted.