ICPRAM 2021 (10th International Conference on Pattern Recognition Applications and Methods) received 97 paper submissions from 30 countries. To evaluate each submission, a double‐blind paper review was performed by the Program Committee. After a stringent selection process, 21 papers were published and presented as full papers, i.e. completed work (12 pages/25' oral presentation), 53 papers were accepted as short papers (28 as oral presentation and 25 as poster presentation). ICPRAM's program included three invited talks delivered by internationally distinguished speakers, namely: The papers were organized in thirteen parallel sessions ranging from areas such as Machine Learning Methods; Deep Learning and Neural Networks; Classification and Clustering; Natural Language Processing; Theory and Methods; Methods and Applications; and Image and Video Analysis and Understanding. The organizing committee included the ICPRAM Conference Chair: Ana Fred, Instituto de Telecomunicações and University of Lisbon, Portugal; and the Program Co‐Chairs: Maria De Marsico, Sapienza Università di Roma, Italy; and Gabriella Sanniti di Baja, Italian National Research Council CNR, Italy.

Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time con-suming. In this paper, we propose a system that leverages customer/system interaction feedback signals to automate learning without any manual annotation. Users of these systems tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results.

Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through an immense amount of online and offline content, aiming to discover what their competitors are doing in the marketplace to understand what type of threat they pose to their business' financial well-being. Currently, this process is time and labor-intensive, slow and costly. This paper presents Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment in the global technology company, IBM. Clarity has been running for more than a year and is used by over 4,500 people to perform over 200 competitive analyses involving over 1000 products.

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks.

Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through an immense amount of online and offline content, aiming to discover what their competitors are doing in the marketplace to understand what type of threat they pose to their business' financial well-being. Currently, this process is time and labor-intensive, slow and costly. This paper presents Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment in the global technology company, IBM. Clarity has been running for more than a year and is used by over 4,500 people to perform over 200 competitive analyses involving over 1000 products.

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. During that time, these symposia provided a fertile ground for numerous collaborations and pioneered many discussions revolving around HRI, XAI for HRI, service robots, interactive learning, and more. This unique intersection of expertise, along with the rising interest in robots in mixed human-robot environments, calls for an informed discussion about the future of AI-HRI as a united research community. As such, this year's symposium had no single theme and AI-HRI submissions were encouraged from across disciplines and research interests. Moreover, with the rising interest in AR and VR as part of an AI-HRI system, along with the difficulties in running physical experiments during the pandemic, this year we specifically encouraged researchers to submit works that do not include a physical robot in their evaluation, but promote HRI research in general.

The Association for the Advancement of Artificial Intelligence's 2021 International Conference on Artificial Intelligence and Interactive Digital Entertainment was held October 11-15, 2021. There were three workshops in the program: Experimental AI in Games, Programming Languages in Entertainment, and Strategy Games. This report contains summaries of some, but not all symposia. The 2021 Experimental AI in Games Workshop helped to encourage experimentation and discovery in game AI research and game development. This year saw fourteen presentations exploring established subjects such as level and narrative generation, to theoretical and practitioner work.

This year the 34th edition of the IEA/AIE (International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems), abbreviated as IEA/AIE 2021, was held in Kula Lumpur, https://ieaaie2021.wordpress.com/ The IEA/AIE conference is a longstanding conference, held every year since 1988, which focuses on artificial intelligence and its applications. Over many years, the IEA/AIE conference has been held worldwide in more than twenty different countries. The IEA/AIE 2021 conference is sponsored by the International Society of Applied Intelligence (ISAI) in cooperation with Springer, University Teknologi Malaysia, the i-SOMET incorporated Association, Association for the Advancement of Artificial Intelligence (AAAI) / Assoc. This year, 145 papers were submitted to the conference.

The 18th International Conference for Artificial Intelligence and Law (ICAIL 2021) was organized at the University of São Paulo School of Law, Brazil. ICAIL is a biannual conference organized under the auspices of the International Association for Artificial Intelligence and Law (iaail.org) For the first time, the ICAIL conference was organized entirely online, due to the overall Covid-19 pandemic situation. Despite these unusual circumstances, the conference came out as a considerable success, attracting almost 1400 registered participants, the highest number ever. The conference talks were streamed publicly on the YouTube channel and the discussions and networking were enabled on the platforms accessible for the registered participants.

Research interest in Conversational AI has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more "human" tasks that involve creativity and variation, evaluation strategies need to mature with them. Conversational AI, or ConvAI as it has been abbreviated, is a sub-field of artificial intelligence (AI) where the goal is to build an autonomous agent that is capable of maintaining natural discourse with a human over some interface such as text or speech. The purpose may be to help humans perform tasks as a virtual/digital assistant, provide a natural language interface to another system as in information retrieval or navigation systems, or simply to converse like one would with an open domain chatbot.