On the challenges and opportunities in visualization for machine learning and knowledge extraction: A research agenda

We describe a selection of challenges at the intersection of machine learning and data visualization and outline a subjective research agenda based on professional and personal experience. The unprecedented increase in the amount, variety and the value of data has been significantly transforming the way that scientific research is carried out and businesses operate. Within data science, which has emerged as a practice to enable this data-intensive innovation by gathering together and advancing the knowledge from fields such as statistics, machine learning, knowledge extraction, data management, and visualization, visualization plays a unique and maybe the ultimate role as an approach to facilitate the human and computer cooperation, and to particularly enable the analysis of diverse and heterogeneous data using complex computational methods where algorithmic results are challenging to interpret and operationalize. Whilst algorithm development is surely at the center of the whole pipeline in disciplines such as Machine Learning and Knowledge Discovery, it is visualization which ultimately makes the results accessible to the end user. Visualization thus can be seen as a mapping from arbitrarily high-dimensional abstract spaces to the lower dimensions and plays a central and critical role in interacting with machine learning algorithms, and particularly in interactive machine learning (iML) with including the human-in-the-loop. The central goal of the CD-MAKE VIS workshop is to spark discussions at this intersection of visualization, machine learning and knowledge discovery and bring together experts from these disciplines. This paper discusses a perspective on the challenges and opportunities in this integration of these discipline and presents a number of directions and strategies for further research

A tree based keyphrase extraction technique for academic literature

Automatic keyphrase extraction techniques aim to extract quality keyphrases to summarize a document at a higher level. Among the existing techniques some of them are domain-specific and require application domain knowledge, some of them are based on higher-order statistical methods and are computationally expensive, and some of them require large train data which are rare for many applications. Overcoming these issues, this thesis proposes a new unsupervised automatic keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, which is domain-independent, employs limited statistical knowledge, and requires no train data. The proposed technique also introduces a new variant of the binary tree, called KeyPhrase Extraction (KePhEx) tree to extract final keyphrases from candidate keyphrases. Depending on the candidate keyphrases the KePhEx tree structure is either expanded or shrunk or maintained. In addition, a measure, called Cohesiveness Index or CI, is derived that denotes the degree of cohesiveness of a given node with respect to the root which is used in extracting final keyphrases from a resultant tree in a flexible manner and is utilized in ranking keyphrases alongside Term Frequency. The effectiveness of the proposed technique is evaluated using an experimental evaluation on a benchmark corpus, called SemEval-2010 with total 244 train and test articles, and compared with other relevant unsupervised techniques by taking the representatives from both statistical (such as Term Frequency-Inverse Document Frequency and YAKE) and graph-based techniques (PositionRank, CollabRank (SingleRank), TopicRank, and MultipartiteRank) into account. Three evaluation metrics, namely precision, recall and F1 score are taken into consideration during the experiments. The obtained results demonstrate the improved performance of the proposed technique over other similar techniques in terms of precision, recall, and F1 scores

Human-Centered Explainable Artificial Intelligence for Anomaly Detection in Quality Inspection: A Collaborative Approach to Bridge the Gap Between Humans and AI

In the quality inspection industry, the use of Artificial Intelligence (AI) continues to advance to produce safer and faster autonomous systems that can perceive, learn, decide, and act independently. As observed by the researcher interacting with the local energy company over a one-year period, these AI systems’ performance is limited by the machine’s current inability to explain its decisions and actions to human users. Especially in energy companies, eXplainable-AI (XAI) is critical to achieve speed, reliability, and trustworthiness with human inspection workers. Placing humans alongside AI will establish a sense of trust that augments the individual’s capabilities at the workplace. To achieve such an XAI system centered around humans, it is necessary to design and develop more explainable AI models. Incorporating these XAI systems centered around human workers in the inspection industry brings a significant shift in conducting visual inspections. Adding this explainability factor to the AI intelligent inspection systems makes the decision-making process more sustainable and trustworthy by bringing a collaborative approach. Currently, there is a lack of trust between the inspection workers and AI, creating uncertainty among inspection workers about the use of the existing AI models. To address this gap, the purpose of this qualitative research study was to explore and understand the need for human-centered XAI systems to detect anomalies in quality inspection in energy industries

Um Método voltado à Recomendação de Itens no Contexto de Marketing de Afiliados

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.O progresso tecnológico tem proporcionado às empresas diversas ferramentas de marketing digital com o intuito de evidenciar a marca e aumentar o faturamento. O marketing de afiliados é uma delas, sendo entendido como um serviço de parceria que proporciona para as empresas uma nova forma de divulgar seus produtos de maneira mais rápida e precisa. Entretanto, a maioria das plataformas de marketing de afiliados sofrem pelo grande volume de afiliados (usuários) cadastrados que não promovem retorno, ou seja, não vendem. Neste sentido, o presente trabalho possui como objetivo o desenvolvimento de um método de recomendação de produtos no cenário de marketing de afiliados visando promover uma maior interconexão entre afiliados e produtos, com impacto no aumento da taxa de conversão, isto é, nas vendas. No intuito de avaliar o método, foram coletadas as interações dos afiliados em uma plataforma real, promovendo suporte para o treinamento e teste do modelo de recomendação baseado em aprendizado profundo do tipo autoencoder. Para avaliar o método proposto são apresentados alguns cenários com o intuito de demonstrar a utilização do sistema de recomendação. Além disso, foram realizados testes para verificar a efetividade das recomendações. Os resultados alcançados para as recomendações de produtos tiveram uma acurácia de 87,37%. Desta forma, conclui-se que o método proposto tem potencial para sugerir produtos aos afiliados que estes tenham interesse para a divulgação, elevando o potencial de faturamento das empresas parceiras da plataforma e o número de afiliados vendedores