Abstract
Technological evolution impacts the research and development of new solutions, as well as consumers’ expectations and behaviors. With the advent of the new millennium, collaboration systems and technologies were introduced to support ordinary cooperative work and inter-dependent, socially and culturally mediated practices as integral units of everyday life settings. Nevertheless, existing classification systems are limited in scope to analyze technological developments and capture the intellectual structure of a field, understood as an abstraction of the collective knowledge of its researchers and their socially mediated activities. Ten years after the introduction of Mittleman et al.’s taxonomy, we build upon earlier work and adopt this classification scheme to provide a descriptive, taxonomy-based analysis of four distinct venues focused on collaborative computing research: ACM CSCW, ACM GROUP, ECSCW, and CRIWG. The proposal consists of achieving evidence on technical attributes and impacts towards characterizing the evolution of socio-technical systems via (and for) taxonomic modeling. This study can also constitute an important step towards the emergence of new, potentially more valid and robust evaluation studies combining Grounded Theory with alternative methods and techniques. © Springer Nature Switzerland AG 2018.

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The increasing amount of scholarly literature and the diversity of dissemination channels are challenging several fields and research communities. A continuous interplay between researchers and citizen scientists creates a vast set of possibilities to integrate hybrid, crowd-machine interaction features into crowd science projects for improving knowledge acquisition from large volumes of scientific data. This paper presents SciCrowd, an experimental crowd-powered system under development “from the ground up” to support data-driven research. The system combines automatic data indexing and crowd-based processing of data for detecting topic evolution by fostering a knowledge base of concepts, methods, and results categorized according to the particular needs of each field. We describe the prototype and discuss its main implications as a mixed-initiative approach for leveraging the analysis of academic literature. © Springer Nature Switzerland AG 2018.

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Complex optimization scheduling problems frequently arise in the manufacturing and transport industries, where the goal is to find a schedule that minimizes the total amount of time (or cost) required to complete all the tasks. Since it is a critical factor in many industries, it has been, historically, a target of the scientific community. Mathematically, these problems are modelled with Job Shop scheduling approaches. Benchmark results to solve them are achieved with evolutionary algorithms. However, they still present some limitations, mostly related to execution times and the difficulty to generalize to other problems. Deep Reinforcement Learning is poised to revolutionise the field of artificial intelligence. Chosen as one of the MIT breakthrough technologies, recent developments suggest that it is a technology of unlimited potential which shall play a crucial role in achieving artificial general intelligence. This paper puts forward a state-of-the-art review on Job Shop Scheduling, Evolutionary Algorithms and Deep Reinforcement Learning. It also proposes a novel architecture capable of solving Job Shop Scheduling optimization problems using Deep Reinforcement Learning. © 2020, Springer Nature Switzerland AG.

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