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Peer assessment is a process where learners evaluate and provide feedback on one another’s performance, which is critical to the student learning process. Earlier research has shown that it can improve student learning outcomes in various settings, including the setting of engineering education, in which collaborative teaching and learning activities are common. Peer assessment activities in computer-supported collaborative learning (CSCL) settings are becoming more and more common. When using digital technologies for performing these activities, much student data (e.g., peer feedback text entries) is generated automatically. These large data sets can be analyzed (through e.g., computational methods) and further used to improve our understanding of how students regulate their learning in CSCL settings in order to improve their conditions for learning by for example, providing in-time feedback. Yet there is currently a need to automatise the coding process of these large volumes of student text data since it is a very time- and resource consuming task. In this regard, the recent development in machine learning could prove beneficial. To understand how we can harness the affordances of machine learning technologies to classify student text data, this thesis examines the application of five models on a data set containing peer feedback from 231 students in the settings of a large technical university course. The models used to evaluate on the dataset are: the traditional models Multi Layer Perceptron (MLP), Decision Tree and the transformers-based models BERT, RoBERTa and DistilBERT. To evaluate each model’s performance, Cohen’s κ, accuracy, and F1-score were used as metrics. Preprocessing of the data was done by removing stopwords; then it was examined whether removing them improved the performance of the models. The results showed that preprocessing on the dataset only made the Decision Tree increase in performance while it decreased on all other models. RoBERTa was the model with the best performance on the dataset on all metrics used. Explainable artificial intelligence (XAI) was used on RoBERTa as it was the best performing model and it was found that the words considered as stopwords made a difference in the prediction. / Kamratbedömning är en process där eleverna utvärderar och ger feedback på varandras prestationer, vilket är avgörande för elevernas inlärningsprocess. Tidigare forskning har visat att den kan förbättra studenternas inlärningsresultat i olika sammanhang, däribland ingenjörsutbildningen, där samarbete vid undervisning och inlärning är vanligt förekommande. I dag blir det allt vanligare med kamratbedömning inom datorstödd inlärning i samarbete (CSCL). När man använder digital teknik för att utföra dessa aktiviteter skapas många studentdata (t.ex. textinlägg om kamratåterkoppling) automatiskt. Dessa stora datamängder kan analyseras (genom t.ex, beräkningsmetoder) och användas vidare för att förbättra våra kunskaper om hur studenterna reglerar sitt lärande i CSCL-miljöer för att förbättra deras förutsättningar för lärande. Men för närvarande finns det ett stort behov av att automatisera kodningen av dessa stora volymer av textdata från studenter. I detta avseende kan den senaste utvecklingen inom maskininlärning vara till nytta. För att förstå hur vi kan nyttja möjligheterna med maskininlärning teknik för att klassificera textdata från studenter, undersöker vi i denna studie hur vi kan använda fem modeller på en datamängd som innehåller feedback från kamrater till 231 studenter. Modeller som används för att utvärdera datasetet är de traditionella modellerna Multi Layer Perceptron (MLP), Decision Tree och de transformer-baserade modellerna BERT, RoBERTa och DistilBERT. För att utvärdera varje modells effektivitet användes Cohen’s κ, noggrannhet och F1-poäng som mått. Förbehandling av data gjordes genom att ta bort stoppord, därefter undersöktes om borttagandet av dem förbättrade modellernas effektivitet. Resultatet visade att förbehandlingen av datasetet endast fick Decision Tree att öka sin prestanda, medan den minskade för alla andra modeller. RoBERTa var den modell som presterade bäst på datasetet för alla mätvärden som användes. Förklarlig artificiell intelligens (XAI) användes på RoBERTa eftersom det var den modell som presterade bäst, och det visade sig att de ord som ansågs vara stoppord hade betydelse för prediktionen.

Classification of sentiment on customer reviews is a real-world application for many companies that offer text analytics and opinion extraction on customer reviews on different domains such as consumer electronics, hotels, restaurants, and car rental agencies. Natural Language Processing’s latest progress has seen the development of many new state-of-the-art approaches for representing the meaning of sentences, phrases, and words in the text using vector space models, so-called embeddings. In this thesis, we evaluated the most current and most popular text representation techniques against traditional methods as a baseline. The evaluation dataset consists of customer reviews from different domains with different lengths used by a text analysis company. Through a train dataset exploration, we evaluated which datasets were the most suitable for this specific task. Furthermore, we explored different techniques that could be used to alter a language model’s decisions without retraining it. Finally, all the methods were evaluated against their time performance and the resource requirements to present an overall experimental assessment that could potentially help the company decide which is the most appropriate technique to replace its system in a production environment. / Klassificeringen av attityd och känsloläge i kundrecensioner är en tillämpning med praktiskt värde för flera företag i marknadsanalysbranschen. Aktuell forskning i språkteknologi har etablerat vektorrum som standardrepresentation för ord, fraser och yttranden, så kallade embeddings. Denna uppsats utvärderar den senaste tidens mest framgångsrika textrepresentationsmodeller jämfört med mer traditionella vektorrum. Utvärdering görs genom att jämföra automatiska analyser med mänskliga bedömningar för kundrecensioner av varierande längd från olika domäner tillhandahållna av ett textanalysföretag. Inom ramen för studien har olika testmängder jämförts och olika sätt att modifera en språkmodells klassficering utan om träning. Alla modeller har också jämförts med avseende på resurs- och tidsåtgång för träning för att hjälpa uppdragsgivaren fatta beslut om vilken teknik som utgör den mest ändamålsenliga utvecklingsvägen för dess driftsatta system.

This thesis explored the 1941 docent-training course for members of the Junior League held at the Metropolitan Museum of Art. The research focused on understanding what place this philanthropic organization held in the American art museum at that time. This course at the Metropolitan Museum of Art was formed as an attempt to teach Junior League members to become trainers of docents and volunteers in their own communities. Additionally, I looked into the background of the museum staff members Francis Henry Taylor and Roberta Murray Fansler Alford Capers and the Junior League member Helen T. Findlay. Utilizing historical research methods, four augments were formed regarding why this docent-training course occurred; (a) the new leadership and structure in the museum facilitating those training, (b) the collaborative work of Helen T. Findlay and Francis Henry Taylor and their passion towards art education for all audiences, (c) the Junior League’s continued commitment to community involvement, and (d) the fundamental need women had for involvement outside the home. The research concludes with a reflection toward the difficulties and hardships that accompany conducting historical research into the women of art education including non-traditional forms of historical documentation. / text

This master thesis deals with the extraction of relationships between named entities in the text. In the theoretical part of the thesis, the issue of natural language representation for machine processing is discussed. Subsequently, two partial tasks of relationship extraction are defined, namely named entities recognition and classification of relationships between them, including a summary of state-of-the-art solutions. In the practical part of the thesis, system for automatic extraction of relationships between named entities from downloaded pages is designed. The classification of relationships between entities is based on the pre-trained transformers. In this thesis, four pre-trained transformers are compared, namely BERT, XLNet, RoBERTa and ALBERT.

There is a growing use of the internet and alongside that, there has been an increase in the use of toxic language towards other people that can be harmful to those that it targets. The usefulness of artificial intelligence has exploded in recent years with the development of natural language processing, especially with the use of transformers. One of the first ones was BERT, and that has spawned many variations including ones that aim to be more lightweight than the original ones. The goal of this project was to train three different kinds of transformer models, RoBERTa, ALBERT, and DistilBERT, and find out which one was best at identifying toxic language online. The models were trained on a handful of existing datasets that had labelled data as abusive, hateful, harassing, and other kinds of toxic language. These datasets were combined to create a dataset that was used to train and test all of the models. When tested against data collected in the datasets, there was very little difference in the overall performance of the models. The biggest difference was how long it took to train them with ALBERT taking approximately 2 hours, RoBERTa, around 1 hour and DistilBERT just over half an hour. To understand how well the models worked in a real-world scenario, the models were evaluated by labelling text as toxic or non-toxic on three different subreddits. Here, a larger difference in performance showed up. DistilBERT labelled significantly fewer instances as toxic compared to the other models. A sample of the classified data was manually annotated, and it showed that the RoBERTa and DistilBERT models still performed similarly to each other. A second evaluation was done on the data from Reddit and a threshold of 80% certainty was required for the classification to be considered toxic. This led to an average of 28% of instances being classified as toxic by RoBERTa, whereas ALBERT and DistilBERT classified an average of 14% and 11% as toxic respectively. When the results from the RoBERTa and DistilBERT models were manually annotated, a significant improvement could be seen in the performance of the models. This led to the conclusion that the DistilBERT model was the most suitable model for training and classifying toxic language of the lightweight models tested in this work.

This diploma thesis aims to explore whether international intervention can be the main cause of suicide terrorism. To determine this causal relation between suicide terrorism and international intervention, it tests Robert Pape's nationalist theory. The research sample of the cases of Afghanistan, Iraq, Pakistan, and Palestine was selected based on the Suicide Terrorism Attack database. The thesis applies the empirical-analytical methodology and the method of multiple case study to confirm or refute the validity of the research hypotheses. The outcome of the thesis is that the main trigger for a suicide terrorist campaign is a significantly stronger adversary, a social climate conducive to self- sacrifice, and an individual sense of hopelessness. The presence of international intervention fulfils all these features, but the theory has its limits - an exclusive focus on foreign intervention and state centrality.

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This thesis investigates Parameter-Efficient Fine-Tuning (PEFT) methods, specifically Low-Rank Adaptation (LoRA) (Hu et al. 2021) and Adapters (Houlsby et al. 2019), using the General Language Understanding Evaluation (GLUE) dataset (Wang et al. 2019). The primary focus is to evaluate the effectiveness and efficiency of these methods in fine-tuning pre-trained language models. Additionally, we introduce a novel application by applying the methodology from Yang et al. 2024 to the adapter module weights. We utilize Laplace approximations over both the LoRA (Yang et al. 2024, Daxberger et al. 2022a) and the newly adapted Adapter weights, assessing the Expected Calibration Error (ECE) and Negative Log-Likelihood (NLL). Furthermore, we discuss practical considerations such as training time, memory usage, and storage space implications of these PEFT techniques. The findings provide valuable insights into the trade-offs and benefits of using LoRA and Adapters for fine-tuning in resource-constrained environments.

Made available in DSpace on 2016-12-08T16:51:57Z (GMT). No. of bitstreams: 1 lara.pdf: 763931 bytes, checksum: ad900beabace23b47c5373832696ac94 (MD5) Previous issue date: 2012-03-30 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This research proposes a study about the intersection between personal life and professional life in the work of the Odin Teatret actresses, Else Marie Laukvik, Iben Nagel Rasmussen, Julia Varley and Roberta Carreri. Were raised during the trajectory of each of the four actresses, moments where interference of personal life in professional life culminated in the creation of new methods of creation and training. One of the axes of this study was to think that the new methods created by the four actresses would allow to locate the space within each group and to identify different ways to practice understanding of the theater. Through analysis of the space established by every actress in the group, discusses gender relations involving these actresses do theater. Purpose of this study was also to bring to public the research of the actresses in an attempt to analyze his work to bring up discussions on the presence of women in theater history / Esta pesquisa propõe um estudo sobre intersecções entre vida pessoal e vida profissional no trabalho das atrizes do Odin Teatret, Else Marie Laukvik, Iben Nagel Rasmussen, Roberta Carreri e Julia Varley. Foram levantados durante a trajetória de cada uma das quatro atrizes, momentos onde interferências da vida pessoal na vida profissional culminaram na criação de novas metodologias de criação e treinamento. Um dos eixos deste estudo foi pensar que as novas metodologias de criadas pelas quatro atrizes possibilitariam localizar o espaço de cada uma delas dentro do grupo bem como identificar diferentes maneiras de compreensão da prática teatral. Através da análise do espaço estabelecido por cada uma das atrizes no grupo, discute relações de gênero que envolvem o fazer teatral destas atrizes. Também foi propósito deste trabalho trazer à público a pesquisa das atrizes do grupo, numa tentativa de analisando seu trabalho trazer à tona discussões sobre a presença das mulheres na história do teatro