With the rise in popularity of online conferencing platforms such as Zoom and Teams, there is an increasing need to understand the dynamics of virtual meetings to enhance productivity. One key aspect that can contribute to productive online meetings is conversational involvement. Personality traits influence the behaviour of participants within a group and therefore, have the potential to influence conversational involvement. This research paper aims to explore the impact of personality traits on conversational involvement in online meetings. To interpret personalities, the HEXACO model has been employed. Through a systematic investigation, this study provides valuable insights into how individual personalities influence engagement levels in a group during virtual meetings. The results of this study shed light on the relationship between personality traits and conversational involvement, thus contributing to the existing body of knowledge surrounding effective online meeting practices.

This research paper focuses on the accuracy and limitations of user values elicited through a textual interface with questions asked in isolation. The primary objective was to conduct a user study using a textual interface that uses questions in isolation to assess the effectiveness and accuracy of this interface type and questioning style. The study involved exploring various scenarios and associated user values, as well as comparing the textual interface with graphical and audio interfaces tested in four related studies. The user study consisted of 15 participants who interacted with the textual interface. Afterwards, they were tasked with judging and adapting their behaviour models while also evaluating the interface's usability. The findings indicate that while the textual interface demonstrates decent usability, participants did not perceive a strong need for the current system and that compared to other interface types, the textual interface does not yield the most accurate results. This research provides insights into the usability and limitations of a textual interface for eliciting user values. It emphasizes the need for further exploration and development of alternative interface types to enhance accuracy and user engagement.

In September 2018, the Smart Teddy project was founded by a group of researchers within the Hague University of Applied Sciences1 in the Netherlands. The Smart Teddy project is a multidisciplinary project aiming to create an interactive system, using a teddy bear as a focus point, which collects the data needed in order to enable seniors with dementia to live independently for a longer period of time. Over the last three years, three prototypes of the Smart Teddy have been developed. The Smart Teddy project was introduced as a final project for students following the BSc program Electrical Engineering at the Delft University of Technology. Starting in April 2021, a team of six students attempted to further develop the Smart Teddy over the course of 11 weeks. This thesis contains the Human Interaction & Integration subdomain of the Smart Teddy thesis project, where Human Interaction refers to the aspects of the Teddy that encourage interaction with the user, and Integration refers to the combination of all subdomains into one fully functioning prototype. In this thesis, the design choices, implementation methods and verification are discussed. The contribution to the prototype regarding Human Interaction & Integration are the addition of a movement system using pneumatics, the implementation of a flexible touch sensor, the ability for the Teddy to produce audio, to communicate wirelessly with the Base Station, and for all components in the Teddy to communicate with the main controller. The final prototype has been implemented using the Raspberry Pi Pico microcontroller, which was mounted on a custom PCB. All controls are provided by the Pico, and uses I2C, SPI, UART, and analog and digital inputs to communicate with the sensors and actuators. These sensors and actuators were implemented using off-the-shelf breakout boards and drivers, to allow for fast design and test iterations. The movement of the Teddy has been implemented using air pumps and molded silicon rubber, and the tail wagging is implemented using the same principle used for soft robotic grippers. The final prototype is fully functional and meets 16 of the 20 requirements - the requirement concerning speech recognition and the noise produced by the pumps have not been met.

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be applied directly to this task. Thus the paper investigates two segmentation methods: a simple context-length window approach and topic segmentation using Latent Dirichlet Allocation (LDA). The context-length window approach's performance is close to the Pointer Generator framework. The topic segmentation gives worse results. Overall gpt-3.5-turbo performs worse with both approaches than state-of-the-art models which use a transformer architecture adapted for long documents.

Behavior support applications aim to provide personalized and flexible support to users in various domains. To achieve this, understanding users' preferences, values, and context is crucial. Creating user models that incorporate users' norms and values has been proposed as a solution to capture the relationship between desired behaviors and values. However, updating and modifying user models at run-time remains a challenge, as users' norms and values may change over time. This study investigates the accuracy of an audio interface designed to elicit values-related information using isolated questions. This involves designing an audio interface and evaluating its effectiveness through participant interactions, where they are presented with four scenarios. It was found that the audio interface performs above average in terms of usability, as indicated by the System Usability Scale score. The accuracy of the user models is evaluated through the Hamming distance and value differences between the base model and the participant-improved model. Most models required a small number of changes, and when changes were made, they were generally minimal. Additionally, feedback collected through open-ended interview questions lays down a basis for further development. The study contributes to the field by demonstrating the efficacy of the audio interface and its potential for updating user models in real-time. Overall, the research findings support the development of more effective and personalized behavior support applications that can adapt to its users.

Meetings are the keystone of a good company. They allow for quick decision making, multiple-perspective problem solving and effective communication. However, most employees and managers have a negative view on the efficiency and quality of their meetings. High quality meetings where every participant feels equally heard and respected is crucial for having positive meeting sentiment within a company. One of the most influential aspects of meetings are speech overlaps. Overlaps range from short utterances such as backchannels, to follow up questions and clarifications, to complete interruptions. In non-competitive cases, the overlapped speaker feels that the other participants are listening and actively engaging with them during the meeting. In competitive cases, the overlapped speaker can feel interrupted and unimportant. Therefore, competitive overlaps often have a negative impact on the course of the discussion and the overlappee's meeting sentiment. In problematic cases, these overlaps should be reduced to a minimum. In order to do this, overlaps must be classified as either competitive or non-competitive. This paper proposes a novel approach to overlap classification, namely that of text-based classification through Large Language Models. Four different prompt designs are used and tested on the two best performing and publicly available models, GPT-3.5-turbo and GPT-4. The results show that the in-context learning approach using the GPT-4 model results in the most accurate classifications. When comparing the results to previous work, it is observed that the text-based GPT-4 model matches carefully engineered neural networks that even adopt a multi-modular approach.

This paper presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases in the text as being either in favor of, against or expressing no opinion on a given thesis. To identify relevant theses, we compare the performance of a latent Dirichlet allocation-based topic model against that of a diverse set of large language models. To classify the stance of a phrase with respect to a thesis, only large language models are used. We find that, while state-of-the-art large language models do not outperform topic modeling-based approaches in extracting semantically relevant content, they are capable of presenting such content in a more concise and grammatically correct manner. We also find that state-of-the-art large language models are not capable of accurately performing stance classification as described above.

The amount of people dealing with dementia is rising globally. The amount of caretakers is, however, not. Therefore, technological aids are needed to support people dealing with dementia and relieve the stress on their caretakers. Current solutions provide tracking of people with dementia. Also, different robots exist that provide people with companionship. However, no solution exists that combines tracking and companionship capabilities. Therefore, the Smart Teddy is introduced. The Smart Teddy can track different indicators that indicate the progress of dementia and simultaneously provide the user with companionship through interaction. The goal of this thesis is to design a data acquisition system that acquires meaningful data that can be used for the development of algorithms that will autonomously determine the progress of dementia. To achieve this, a system with a Teddy and a Base station has been designed. The Teddy has a sound-, a carbon monoxide-, a smoke- and a movement sensor. Also, a real-time module is implemented to be able to assign the current time to the measurement data. Lastly, a GPS and GSM module is implemented to be able to track seniors in case they wander. In the Base station, a mmWave sensor is implemented that tracks the position, velocity, and direction of the persons present in the room. Also, a processor is implemented that gathers and stores the data from the mmWave sensor and the data from the Teddy which is sent via a LoRa connection. In addition, the designed system can store the collected data for more than one week. The collected data can be used by an expert in dementia to extract meaningful information about dementia progress, after that, an expert in digital signal processing is needed to develop algorithms that estimate the quality of life of a senior suffering from dementia.

Adversarial attacks pose a risk to machine learning (ML)-based network intrusion detection systems (NIDS). In this manner, it is of great significance to explore to what degree these methods can be viably utilized by potential adversaries. The majority of adversarial techniques are designed for unconstrained domains such as the image recognition domain, where these methods apply alterations to the pixels in a picture. Therefore, the applicability of these techniques to the NIDS domain is very limited. Related work on adversarial techniques for NIDS generally considers feature-space techniques, which cannot be applied in a practical situation since only the extracted network traffic features are modified and not the actual network traffic. To solve these limitations, a traffic-space approach for creating adversarial examples for evading ML-based NIDS is proposed and assessed with several classification models. The proposed constrained adversarial crafting method is based on the Iterative Fast Gradient Sign Method (IFGSM) and is called the Constrained Iterative Fast Gradient Sign Method (CIFGSM). A constraint set is added as a penalty term to the loss function of the optimization to ensure that the adversarial values remain within the valid space. Additionally, an L2 regularization term is used to minimize the distance between the original and adversarial network traffic samples. The proposed method is evaluated and shown to be an effective way for generating realistic and practical adversarial evasion packets. To achieve this, network packet components and their characteristics are defined as a constraint set which can be used for the optimization task and a custom adversarial loss function is created that encapsulates the different elements of this optimization problem. Furthermore, multiple models are evaluated to test the transferability of this method. Conclusively, the proposed method is evaluated in a realistic scenario, where adversarial packet captures are crafted and examined. Where other state-of-the art works only modify the network traffic features in feature-space or on a connection level only and do not apply their method in a real world scenario, this work modifies the packet captures on a per-packet level which is subsequently used to evaluate flow based classification models.

Negotiation Support Systems (NSSs) can provide help based on the preference setting (domain, issue weights, issue ranking, strategies, etc.) of the users of the systems. However, sometimes the users of the systems might make mistakes in the preference setting. With wrong preferences, the NSSs might provide suggestions that conflict with the users' desires. This thesis focuses on designing a mechanism that could detect the mismatches in issue weights of the users of NSSs and provide recommendations on updating the issue weights with a scoring method. This mechanism also has the ability to provide different recommendations based on the users' self-confidence level. A case study is done with six simulation experiments to test the mechanism. The results show that during the negotiation, the users of the NSS (PN) can have a chance to change the issue weights when mismatches occurred and the NSS (PN) can provide better suggestions after the issue weights are updated. The limitations of the mechanism are concluded at the end together with the future thoughts

Negotiation is not a skill that comes naturally to most people. However, most people could benefit from attaining good negotiation skills. Non-verbal behaviour plays an important role in negotiations. Previous studies have shown a link between mimicry through conversational agents and self-regulation of non-verbal behaviour. The current study aims to raise a person’s self-awareness of their non-verbal behaviour through the medium of a conversational agent in negotiation training. An experiment (N = 64) is conducted where participants performed negotiations with an intermediate feedback round where the participants' non-verbal behaviour was played back on a virtual agent. The agent either mimicked the participants’ non-verbal behaviour or exhibited idle behaviour. It was found that the mimicking agent provided better feedback to the participants. Furthermore, repeated negotiations increase a person's self-awareness. The data indicate that raising a person’s self-awareness in negotiation training by utilising non-verbal mimicry through a conversational agent is feasible.

Involvement in an online setting has become increasingly more important in a rapidly digitizing world. Previous research has suggested that lexical alignment is an effective method for increasing interpersonal involvement. Multi-modal systems exist for analysing involvement, but the lexical alignment modality is not commonly used in these. This study examines the lexical alignment modality as a predictive measure of involvement in online group meetings. By analyzing participation indicators in virtual meetings, the aim is to identify moments of high involvement. Results indicate that lexical alignment does not correlate with moments of high involvement. The findings contribute to the scientific understanding of leveraging the verbal modality for analyzing and organizing online meetings.

The importance of understanding how to maximize involvement in virtual meetings has been greatly increased due to the rapid rise of video conferencing tools during the COVID-19 pandemic. This research builds upon the data collected by the MEMO Corpus, specifically interview footage and annotated data, and will aim to explore the various factors that influence group involvement in virtual meetings. The research will be conducted in three steps; data annotation, data exploration, and data analysis. The goal of this paper is to investigate the influence of factors such as likeability, listening, and audience engagement in how conversational ability is perceived, given that the scales provided by the MEMOCorpus are valid and the three measures are independent, and how conversational ability influences group involvement, by exploring the existence of correlations. The results of the analysis will provide insight into the dynamics of conversational involvement in virtual meetings and improve virtual communication practices.

Humans teach each other by recollecting one's own experiences and sharing them with others. The intention being that the person being taught, does not need to experience those things first-hand to be able to learn from them. A large portion of human learning is in some form derived from this concept. This has inspired this report. Recent developments in Deep Q-Networks applied a so-called replay memory. This replay memory stores experiences in a buffer for it to learn from later. It is this replay memory that is now used as a source of information for other agents. The core principle being attempting to find important entries in the replay memory and sharing those memories with other agents could improve the learning process of an agent. This memory selection process is done by use of prioritised sampling. The desired result being an agent able to learn faster. This is used to determine the importance of memory samples by influencing how likely those memories are to be sampled for teaching one another. Temporal difference is chosen as a metric for importance as it represents a surprise value. This method is then compared to a baseline DQN-agent who does not communicate, a randomly messaging DQN-agent and some other variations. The benchmark tasks describe a simple gridworld with objectives with varying levels of difficulty. The results of the experiments show that, initially, prioritisation results in rapid learning. This is due to the fact that agents are able to converge to identical policies without it having detrimental effects on the return of rewards. This converging, however, has proven to hamstring the learning process as a whole that prioritised messaging agents have. Very similar policies in benchmark tasks which stand to benefit from heterogeneous policies lead to agents not being able to find their personal optimal policy. The non-communicating agent was able to diverge in policies and therefore ultimately solve the problems more efficiently and with a higher return in rewards.

The energy markets are emerging slowly towards a more decentralized form, e.g. technologies are allowing prosumers to trade energy from their solar house rooftops. But as these markets are governed by people and depend on the behavior of people directly, they are susceptible to challenges from the social, economic and cultural norms around the people. One of the example is the cultural discrimination in energy trading noted by some researchers and practitioners in rural India, where people do not prefer to trade energy and goods with different castes, especially lower castes. This creates an unfair market for the poor and lower caste producers, and thus this thesis aimed at modeling such markets through agent based modeling, and introduce different automation mechanisms, namely local mediation, and bid splitting to prevent the effects of discrimination in energy trading. Local mediation allows introduction of a mediator who prevents from direct trade between people and thus prevents any discrimination, bringing in some sort of anonymity. The other mechanism of bid split allows splitting the bids by producer and consumer into small chunks, such that 2 producers in market with 1 and 100 units of energy to trade would look like 101 producers in market trading 101 units of energy. Evaluating the results of these mechanisms on increase of social welfare, efficiency, market access and reduction of inequality has shown that the bid split helps in reducing the discrimination, and so does the introduction of mediation (where the mediator does not discriminate). The former has much more significant effect on the market access, and the validation of the model has helped derive some policy recommendations for implementer in the energy industry and governance field e.g. on the load management for the devices.