Multimodal Group Activity Dataset for Classroom Engagement Level Prediction

We collected a new dataset that includes approximately eight hours of audiovisual recordings of a group of students and their self-evaluation scores for classroom engagement. The dataset and data analysis scripts are available on our open-source repository. We developed baseline face-based and group-activity-based image and video recognition models. Our image models yield 45-85% test accuracy with face-area inputs on person-based classification task. Our video models achieved up to 71% test accuracy on group-level prediction using group activity video inputs. In this technical report, we shared the details of our end-to-end human-centered engagement analysis pipeline from data collection to model development

Analyzing Sanctioned Suicide: a case study on pro-choice suicide sites

According to the World Health Organization, close to 700’000 people take their own lives every year. Suicide has always been a socially important topic, so much so that free hotlines, help bots and automatic banners are displayed and easily accessible to people that search related keywords on the web. In the last year, it has come to light the existence of Sanctioned Suicide, a pro-choice forum discussing suicide, where users can both look for help with their recovery or research and asks questions about methods and how to acquire them. These types of sites have yet to be extensively researched in the literature. Their analysis could allow us to better understand what are the topics discussed and how these communities act, very useful knowledge for suicide prevention and help of suicidal individuals. In this thesis, we use Sanctioned Suicide as a case study and investigate how it is organized, what knowledge can be found and how users communicate in this environment. We have collected data for a total of 53K threads, 700K comments and 16K users. We use this dataset to analyze user trends, extract the topics of conversation in the forum and uncover hidden relations. Our analyses show that 30% of the topics found in Sanctioned Suicide discussions deal with suicide methods. We also discover that Covid has been a distress factor for users, especially during the first lockdown, highlighting a strong connection between talks of suicide and Covid.According to the World Health Organization, close to 700’000 people take their own lives every year. Suicide has always been a socially important topic, so much so that free hotlines, help bots and automatic banners are displayed and easily accessible to people that search related keywords on the web. In the last year, it has come to light the existence of Sanctioned Suicide, a pro-choice forum discussing suicide, where users can both look for help with their recovery or research and asks questions about methods and how to acquire them. These types of sites have yet to be extensively researched in the literature. Their analysis could allow us to better understand what are the topics discussed and how these communities act, very useful knowledge for suicide prevention and help of suicidal individuals. In this thesis, we use Sanctioned Suicide as a case study and investigate how it is organized, what knowledge can be found and how users communicate in this environment. We have collected data for a total of 53K threads, 700K comments and 16K users. We use this dataset to analyze user trends, extract the topics of conversation in the forum and uncover hidden relations. Our analyses show that 30% of the topics found in Sanctioned Suicide discussions deal with suicide methods. We also discover that Covid has been a distress factor for users, especially during the first lockdown, highlighting a strong connection between talks of suicide and Covid

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem

Reservoir SMILES: Towards SensoriMotor Interaction of Language and Embodiment of Symbols with Reservoir Architectures

Language involves several hierarchical levels of abstraction. Most models focus on a particular level of abstraction making them unable to model bottom-up and top-down processes. Moreover, we do not know how the brain grounds symbols to perceptions and how these symbols emerge throughout development. Experimental evidence suggests that perception and action shape one-another (e.g. motor areas activated during speech perception) but the precise mechanisms involved in this action-perception shaping at various levels of abstraction are still largely unknown. My previous and current work include the modelling of language comprehension, language acquisition with a robotic perspective, sensorimotor models and extended models of Reservoir Computing to model working memory and hierarchical processing. I propose to create a new generation of neural-based computational models of language processing and production; to use biologically plausible learning mechanisms relying on recurrent neural networks; create novel sensorimotor mechanisms to account for action-perception shaping; build hierarchical models from sensorimotor to sentence level; embody such models in robots

The Role of Explainable AI in the Research Field of AI Ethics

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems that can explain their decisions. Explainable AI (XAI) refers to AI systems that are interpretable or understandable to humans. The research fields of AI ethics and XAI lack a common framework and conceptualization. There is no clarity of the field’s depth and versatility. A systematic approach to understanding the corpus is needed. A systematic review offers an opportunity to detect research gaps and focus points. This paper presents the results of a systematic mapping study (SMS) of the research field of the Ethics of AI. The focus is on understanding the role of XAI and how the topic has been studied empirically. An SMS is a tool for performing a repeatable and continuable literature search. This paper contributes to the research field with a Systematic Map that visualizes what, how, when, and why XAI has been studied empirically in the field of AI ethics. The mapping reveals research gaps in the area. Empirical contributions are drawn from the analysis. The contributions are reflected on in regards to theoretical and practical implications. As the scope of the SMS is a broader research area of AI ethics the collected dataset opens possibilities to continue the mapping process in other directions.© 2023 Association for Computing Machinery.fi=vertaisarvioitu|en=peerReviewed

Adult Public Library Patrons\u27 Perceptions of an Academic Library E-Learning Resource

Many Americans lack the skills required to use public access computers and the Internet at public libraries (PLs). Staff members of a PL in the Midwestern United States provide basic computer training to support patrons\u27 Internet and public access computer use. However, adult patrons who are beyond the basic skills level and those with sensory-disabilities are underserved. The purpose of this qualitative single-case study was to understand how an academic library\u27s information literacy e-resource affected the PL\u27s adult patrons\u27 learning based on the perceptions of adult patrons at a PL. Kling\u27s social informatics served as the study\u27s conceptual framework and the research questions centered on how academic library\u27s e-resource affected the participants\u27 learning. Purposive homogeneous sampling was used to identify 10 participants over the age of 18 who were patrons at the target site. Data were collected using observations, semi structured interviews, and document review. The data were analyzed using coding and structural analysis. Themes supporting the findings of an academic e-resource affecting the participants\u27 learning included standards-based e-resource sharing across library types, digital exclusion, digital inclusion, change, and innovation. A white paper was developed including a summary of the findings and the recommendation that library leaders adopt the academic library\u27s e-resource system to improve access and to support individuals who have sensory disabilities as well as patrons beyond the basic skills level at the study site. The implications for social change include enhanced e-services and the potential expansion of the patron base to include underserved stakeholders within the urban PL community