The field of AI and machine learning has seen exponential growth in the last decade and even more so in the recent year with the considerable public interest in Large Language models (LLMs) such as chat-GPT. LLMs can be used for several purposes, but one possible application would be fine-tuning a model to perform a particular function in a specific field. The goal is therefore fine-tuning a LLM in the field of psychology using a new method called Reinforcement Learning from Human Feedback to determine if it is a viable method in such cases. The theory behind LLMs and RLHF as well as the ethical perspective on developing a psychological AI is presented. Previous studies on both RLHF and AI in psychology are presented, showing the goal is feasible. Then the method is explained for both training and evaluating the model which is done by comparing a pre-trained model with the fine-tuned one. The study is considered scientifically relevant as RLHF has been used to fine-tune LLMs earlier, but has not been done with the intent to make it more specified in a field. The result did not show any clear difference between the pre-trained and the fine-tuned model therefore, more tests are required. However, with the limitations regarding hardware, time to train, and available data, there is much improvement needed for future studies. An ethical framework applied to a digital psychology assistant is discussed and a suitable introduction to the market and division of responsibilities is proposed. / Området AI och maskininlärning har sett exponentiell tillväxt under det senaste decenniet och ännu mer under det senaste året med det stora allmänintresset för stora språkmodeller som chat-GPT. Stora språkmodeller kan användas till flera saker där en möjlig tillämpning är att finjustera en modell för att fylla en viss funktion inom ett specifikt yrke. Målet med arbetet är därför att finjustera en språkmodell inom området psykologi med hjälp av en ny metod kallad Reinforcement Learning from Human Feedback för att undersöka metodens tillämplighet. Teorin bakom stora språkmodeller och RLHF samt det etiska perspektivet på att utveckla en digital psykologi assistent förklaras. Därefter presenteras tidigare studier om både RLHF och AI inom psykologi som visar att målet är genomförbart. Metoden för att både träna och utvärdera modellen förklaras som görs genom att jämföra den förtränade modellen med den finjusterade. Studien bedöms som vetenskapligt relevant även fast RLHF har använts för att finjustera språkmodeller tidigare, har det inte gjorts med målet att finjustera en språkmodell till ett visst yrke. Resultatet visade inte på någon tydlig skillnad mellan den förtränade och den finjusterade modellen, därför krävs fler tester krävs. Men med de begräsningar som fanns gällande hårdvara, tid att träna och tillgänglig data är det mycket som kan förbättras i framtida studier. Det etiska ramverket applicerat på en digital psykologi assistent diskuteras och en lämplig introduktion till marknaden och ansvarsfördelning föreslås.

Software companies spend a lot of resources on debugging, a process where previous solutions can help in solving current problems. The bug tickets, containing this information, are often time-consuming to read. To minimize the time spent on debugging and to make sure that the knowledge from prior solutions is kept in the company, an evaluation was made to see if summaries could make this process more efficient. Abstractive and extractive summarization models were tested for this task and fine-tuning of the bert-extractive-summarizer was performed. The model-generated summaries were compared in terms of perceived quality, speed, similarity to each other, and summarization length. The average description summary contained part of the description needed and the found solution was either well documented or did not answer the problem at all. The fine-tuned extractive model and the abstractive model BART provided good conditions for generating summaries containing all the information needed. / Vid mjukvaruutveckling går mycket resurser åt till felsökning, en process där tidigare lösningar kan hjälpa till att lösa aktuella problem. Det är ofta tidskrävande att läsa felrapporterna som innehåller denna information. För att minimera tiden som läggs på felsökning och säkerställa att kunskap från tidigare lösningar bevaras inom företaget, utvärderades om sammanfattningar skulle kunna effektivisera detta. Abstrakta och extraherande sammanfattningsmodeller testades för uppgiften och en finjustering av bert-extractive- summarizer gjordes. De genererade sammanfattningarna jämfördes i avseende på upplevd kvalitet, genereringshastighet, likhet mellan varandra och sammanfattningslängd. Den genomsnittliga sammanfattningen innehöll delar av den viktigaste informationen och den föreslagna lösningen var antingen väldokumenterad eller besvarade inte problembeskrivningen alls. Den finjusterade BERT och den abstrakta modellen BART visade goda förutsättningar för att generera sammanfattningar innehållande all den viktigaste informationen.

In this thesis, we present a new continuously variable high frequency standing wave oscillator and demonstrate its use in generating the phase locked clock signal of a digital IC. The ring based standing wave resonant oscillator is implemented with a plurality of wires connected in a mobius configuration, with a cross coupled inverter pair connected across the wires. The oscillation frequency can be modulated by coarse and fine tuning. Coarse modification is achieved by altering the number of wires in the ring that participate in the oscillation, by driving a digital word to a set of passgates which are connected to each wire in the ring. Fine tuning of the oscillation frequency is achieved by varying the body bias voltage of both the PMOS transistors in the cross coupled inverter pair which sustains the oscillations in the resonant ring. We validated our PLL design in a 90nm process technology. 3D parasitic RLCs for our oscillator ring were extracted with skin effect accounted for. Our PLL provides a frequency locking range from 6 GHz to 9 GHz, with a center frequency of 7.5 GHz. The oscillator alone consumes about 25 mW of power, and the complete PLL consumes a power of 28.5 mW. The observed jitter of the PLL is 2.56 percent. These numbers are significant improvements over the prior art in standing wave based PLLs.

Deep learning has been rapidly growing in recent years obtaining excellent results for many computer vision applications, such as image classification and object detection. One aspect for the increased popularity of deep learning is that it mitigates the need for hand-crafted features. This thesis work investigates deep learning as a methodology to solve the problem of autonomous collision avoidance for a small robotic car. To accomplish this, transfer learning is used with the VGG16 deep network pre-trained on ImageNet dataset. A dataset has been collected and then used to fine-tune and validate the network offline. The deep network has been used with the robotic car in a real-time manner. The robotic car sends images to an external computer, which is used for running the network. The predictions from the network is sent back to the robotic car which takes actions based on those predictions. The results show that deep learning has great potential in solving the collision avoidance problem.

When classifying texts using a linear classifier, the texts are commonly represented as feature vectors. Previous methods to represent features as vectors have been unable to capture the context of individual words in the texts, in theory leading to a poor representation of natural language. Bidirectional Encoder Representations from Transformers (BERT), uses a multi-headed self-attention mechanism to create deep bidirectional feature representations, able to model the whole context of all words in a sequence. A BERT model uses a transfer learning approach, where it is pre-trained on a large amount of data and can be further fine-tuned for several down-stream tasks. This thesis uses one multilingual, and two dedicated Swedish BERT models, for the task of classifying Swedish texts as of either easy-to-read or standard complexity in their respective domains. The performance on the text classification task using the different models is then compared both with feature representation methods used in earlier studies, as well as with the other BERT models. The results show that all models performed better on the classification task than the previous methods of feature representation. Furthermore, the dedicated Swedish models show better performance than the multilingual model, with the Swedish model pre-trained on more diverse data outperforming the other.

This thesis aims to evaluate different abstractive text summarization models and techniques for summarizing resumes. It has two main objectives: investigate the models’ performance on resume summarization and assess the suitability of the generated summaries as resume introductions. Although automatic abstractive text summarization has gained traction in various areas, its application in the resume domain has not yet been explored. Resumes present a unique challenge for abstractive summarization due to their diverse style, content, and length. To address these challenges, three state-of-the-art pre-trained text generation models: BART, T5, and ProphetNet, were selected. Additionally, two approaches that can handle longer resumes were investigated. The first approach, named LongBART, modified the BART architecture by incorporating the Longformer’s self-attention into the encoder. The second approach, named HybridBART, used an extractive-then-abstractive summarization strategy. The models were fine-tuned on a dataset of 653 resume-introduction pairs and were evaluated using automatic metrics as well as two types of human evaluations: a survey and expert interviews. None of the models demonstrated superiority across all criteria and evaluation metrics. However, the survey responses indicated that LongBART showed promising results, receiving the highest scores in three out of five criteria. On the other hand, ProphetNet consistently received the lowest scores across all criteria in the survey, and across all automatic metrics. Expert interviews emphasized that the generated summaries cannot be considered correct summaries due to the presence of hallucinated personal attributes. However, there is potential for using the generated texts as resume introductions, given that measures are taken to ensure the hallucinated personal attributes are sufficiently generic. / Denna avhandling utvärderar olika modeller och tekniker för automatisk textsammanfattning för sammanfattning av CV:n. Avhandlingen har två mål: att undersöka modellernas prestanda på sammanfattning av CV:n och bedöma lämpligheten att använda de genererade sammanfattningar som CV-introduktioner. Även om automatisk abstrakt textsummering har fått fotfäste inom olika sammanhang är dess tillämpning inom CV-domänen ännu outforskad. CV:n utgör en unik utmaning för abstrakt textsammanfattning på grund av deras varierande stil, innehåll och längd. För att hantera dessa utmaningar valdes tre av de främsta förtränade modellerna inom textgenerering: BART, T5 och ProphetNet. Dessutom undersöktes två extra metoder som kan hantera längre CV:n. Det första tillvägagångssättet, kallat LongBART, modifierade BART-arkitekturen genom att inkludera självuppmärksamhet från Longformer-arkitekturen i kodaren. Det andra tillvägagångssättet, kallat HybridBART, använde en extraktiv-sen-abstraktiv sammanfattningsstrategi. Modellerna finjusterades med ett dataset med 653 CV-introduktionspar och utvärderades med hjälp av automatiska mått, samt två typer av mänsklig utvärdering: en enkätundersökning och intervjuer med experter. Ingen av modellerna visade överlägsenhet på alla kriterier och utvärderingsmått. Dock indikerade enkätsvaren att LongBART visade lovande resultat, genom att få högst poäng i tre av fem utvärderingskategorier. Å andra sidan fick ProphetNet lägst poäng i samtliga utvärderingskategorier, samt lägst poäng i alla automatiska mätningar. Expertintervjuer framhävde att de genererade sammanfattningarna inte kan anses vara pålitliga som fristående sammanfattningar på grund av förekomsten av hallucinerade personliga egenskaper. Trots detta finns det potential att använda dessa sammanfattningar som introduktioner, under förutsättningen att åtgärder vidtas för att säkerställa att hallucinerade personliga attribut är tillräckligt generiska.

Ces travaux visaient à étudier la possibilité de réduire la production de glycérol chezSaccharomyces cerevisiae, afin d’améliorer le rendement éthanol, tout en préservant les capacités decroissance et de production des levures. La production minimale de glycérol nécessaire à la croissancea été déterminée à l'aide d'un modèle de calcul des flux métaboliques. Des souches présentant uneactivité des enzymes de la voie de production du glycérol modulée, afin de s'approcher au plus près del'activité minimale nécessaire estimée in silico, ont été utilisées.Cette stratégie d’ajustement de l’activité de la voie de synthèse du glycérol a permis, encondition aérobie, de réduire de 88 % le rendement glycérol et d'améliorer le rendement éthanol de4,7 % sans modifier la tolérance des mutants à l'éthanol, mais au détriment de la vitesse spécifique decroissance, légèrement réduite. En condition anaérobie, une diminution de 61 % du rendementglycérol et une amélioration de 7 % du rendement éthanol ont pu être obtenues, mais au détriment dela vitesse spécifique de croissance,qui subit une sévère diminution, et de la tolérance à l'éthanol,qui estréduite.L'analyse fine des résultats, grâce à un modèle métabolique, a permis de mettre en évidence,chez les souches mutantes, un besoin accru en énergie, interprété comme la traduction d'une plusgrande difficulté à gérer le stress du procédé et une réorganisation du métabolisme oxydo-réductif,interprétée comme l'impact de la réduction du glycérol sur les voies de réoxydation du cofacteurNADH dans les cellules.Ces résultats ont permis de valider la pertinence de la stratégie de réajustement des fluxmétaboliques, assistée par modélisation stoechiométrique pour l'amélioration des souches, mais aussid'accroître la compréhension du rôle physiologique du glycérol et son intégration au métabolismecellulaire. / This work aimed to assess the possibility of reducing Saccharomyces cerevisiae's glycerolproduction, in order to improve ethanol yield, without altering the abilities of yeasts to grow andproduce ethanol. Minimum glycerol production required for growth was found, thanks to a metabolicflux calculation model. Strains showing a fine tuned activity in the glycerol synthesis pathway enzymeswere used, to get close to the minimum activity established in silico.This fine tuning strategy lead, in aerobiosis, to a 88 % glycerol yield decrease together with a4.7 % ethanol yield increase, with no reduction of mutants'ethanol tolerance, but there is a slightdecrease of the growth rate. In anaerobiosis, a 61 % glycerol yield decrease, together with a 7 %ethanol yield increase were obtained, but mutant strains suffered of a sharp growth rate reduction anda decrease in their ethanol tolerance.A close analysis of the results, with the help of a metabolic model, highlighted both an increaseof mutants' energy requirements, interpreted as an increased difficulty to cope with osmotic stress,and a reorganisation of their oxydo-reductive metabolism, interpreted as glycerol reduction's impacton the NADH cofactor reoxydation pathway.These results validated the relevance of metabolic fine-tuning, assisted with in silicostoichiometric model for strains improvement and they increased the understanding of the integrationof glycerol in cell metabolism as well as its physiological role.

Publicly available data grows exponentially through web services and technological advancements. To comprehend large data-streams multi-document summarization (MDS) can be used. In this research, the area of multi-document summarization is investigated. Multiple systems for extractive multi-document summarization are implemented using modern techniques, in the form of the pre-trained BERT language model for word embeddings and sentence classification. This is combined with well proven techniques, in the form of the TextRank ranking algorithm, the Waterfall architecture and anti-redundancy filtering. The systems are evaluated on the DUC-2002, 2006 and 2007 datasets using the ROUGE metric. Where the results show that the BM25 sentence representation implemented in the TextRank model using the Waterfall architecture and an anti-redundancy technique outperforms the other implementations, providing competitive results with other state-of-the-art systems. A cohesive model is derived from the leading system and tried in a user study using a real-world application. The user study is conducted using a real-time news detection application with users from the news-domain. The study shows a clear favour for cohesive summaries in the case of extractive multi-document summarization. Where the cohesive summary is preferred in the majority of cases.

This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.

Les modèles de langue pré-entraînés ont montré leur efficacité dans beaucoup de tâches de traitement de la langue naturelle. Ces modèles peuvent capter des régularités générales d'une langue à partir d'un grand ensemble de textes, qui sont utiles dans la plupart des applications en traitement de langue naturelle. Dans ce mémoire, nous étudions les problèmes de dialogue, i.e. générer une réponse à un énoncé de l'utilisateur. Nous exploitons les modèles de langue pré-entraînés pour traiter différents aspects des systèmes de dialogue. Premièrement, les modèles de langue pré-entraînés sont entraînés and utilisés dans les systèmes de dialogue de différentes façons. Il n'est pas clair quelle façon est la plus appropriée. Pour le dialogue orienté-tâche, l’approche de l'état de l'art pour le suivi de l'état de dialogue (Dialogue State Tracking) utilise BERT comme encodeur et empile un autre réseau de neurones récurrent (RNN) sur les sorties de BERT comme décodeur. Dans ce cas, seul l'encodeur peut bénéficier des modèles de langue pré-entraînés. Dans la première partie de ce mémoire, nous proposons une méthode qui utilise un seul modèle BERT pour l'encodeur et le décodeur, permettant ainsi un ajustement de paramètres plus efficace. Notre méthode atteint une performance qui dépasse l'état de l'art. Pour la tâche de génération de réponses dans un chatbot, nous comparons 4 approches communément utilisées. Elles sont basées sur des modèles pré-entraînés et utilisent des objectifs et des mécanismes d'attention différents. En nous appuyant sur des expérimentations, nous observons l'impact de deux types de disparité qui sont largement ignorées dans la littérature: disparité entre pré-entraînement et peaufinage, et disparité entre peaufinage et génération de réponse. Nous montrons que l'impact de ces disparités devient évident quand le volume de données d’entraînement est limité. Afin de remédier à ce problème, nous proposons deux méthodes qui réduisent les disparités, permettant d'améliorer la performance. Deuxièmement, même si les méthodes basées sur des modèles pré-entraînés ont connu de grands succès en dialogue général, nous devons de plus en plus traiter le problème de dialogue conditionné, c'est-à-dire dialogue en relation à une certaine condition (qui peut désigner un personnage, un sujet, etc.). Des chercheurs se sont aussi intéressés aux systèmes de chatbot avec des habiletés de conversation multiples, i.e. chatbot capable de confronter différentes situations de dialogues conditionnés. Ainsi, dans la seconde partie de ce mémoire, nous étudions le problème de génération de dialogue conditionné. D'abord, nous proposons une méthode générale qui exploite non seulement des données de dialogues conditionnées, mais aussi des données non-dialogues (textes) conditionnées. Ces dernières sont beaucoup plus faciles à acquérir en pratique. Ceci nous permet d'atténuer le problème de rareté de données. Ensuite, nous proposons des méthodes qui utilisent le concept d'adaptateur proposé récemment dans la littérature. Un adaptateur permet de renforcer un système de dialogue général en lui donnant une habileté spécifique. Nous montrons que les adaptateurs peuvent encoder des habiletés de dialogue conditionné de façon stricte ou flexible, tout en utilisant seulement 6% plus de paramètres. Ce mémoire contient 4 travaux sur deux grands problèmes de dialogue: l'architecture inhérente du modèle de dialogue basé sur des modèles de langue pré-entraînés, et l'enrichissement d'un système de dialogue général pour avoir des habiletés spécifiques. Ces travaux non seulement nous permettent d'obtenir des performances dépassant de l'état de l'art, mais aussi soulignent l'importance de concevoir l'architecture du modèle pour bien correspondre à la tâche, plutôt que simplement augmenter le volume de données d'entraînement et la puissance de calcul brute. / Pre-trained language models (LMs) have shown to be effective in many NLP tasks. They can capture general language regularities from a large amount of texts, which are useful for most applications related to natural languages. In this thesis, we study the problems of dialogue, i.e. to generate a response to a user's utterance. We exploit pre-trained language models to deal with different aspects of dialogue systems. First, pre-trained language models have been trained and used in different ways in dialogue systems and it is unclear what is the best way to use pre-trained language models in dialogue. For task-oriented dialogue systems, the state-of-the-art framework for Dialogue State Tracking (DST) uses BERT as the encoder and stacks an RNN upon BERT outputs as the decoder. Pre-trained language models are only leveraged for the encoder. In the first part of the thesis, we investigate methods using a single BERT model for both the encoder and the decoder, allowing for more effective parameter updating. Our method achieves new state-of-the-art performance. For the task of response generation in generative chatbot systems, we further compare the 4 commonly used frameworks based on pre-trained LMs, which use different training objectives and attention mechanisms. Through extensive experiments, we observe the impact of two types of discrepancy: pretrain-finetune discrepancy and finetune-generation discrepancy (i.e. differences between pre-training and fine-tuning, and between fine-tuning and generation), which have not been paid attention to. We show that the impact of the discrepancies will surface when limited amount of training data is available. To alleviate the problem, we propose two methods to reduce discrepancies, yielding improved performance. Second, even though pre-training based methods have shown excellent performance in general dialogue generation, we are more and more faced with the problem of conditioned conversation, i.e. conversation in relation with some condition (persona, topic, etc.). Researchers are also interested in multi-skill chatbot systems, namely equipping a chatbot with abilities to confront different conditioned generation tasks. Therefore, in the second part of the thesis, we investigate the problem of conditioned dialogue generation. First, we propose a general method that leverages not only conditioned dialogue data, but also conditioned non-dialogue text data, which are much easier to collect, in order to alleviate the data scarcity issue of conditioned dialogue generation. Second, the concept of Adapter has been recently proposed, which adapts a general dialogue system to enhance some dialogue skill. We investigate the ways to learn a dialogue skill. We show that Adapter has enough capacity to model a dialogue skill for either loosely-conditioned or strictly-conditioned response generation, while using only 6% more parameters. This thesis contains 4 pieces of work relating to the two general problems in dialogue systems: the inherent architecture for dialogue systems based on pre-trained LMs, and enhancement of a general dialogue system for some specific skills. The studies not only propose new approaches that outperform the current state of the art, but also stress the importance of carefully designing the model architecture to fit the task, instead of simply increasing the amount of training data and the raw computation power.