Abstract
Only recently has the software engineering community started conducting research on developing energy efficient software, or green software. This is shadowed when compared to the research already produced in the computer hardware community. While research in green software is rapidly increasing, several recent studies with software engineers show that they still miss techniques, knowledge, and tools to develop greener software. Indeed, all such studies suggest that green software should be part of a modern Computer Science Curriculum. In this paper, we present survey results from both researchers' and educators' perspective on green software education. These surveys confirm the lack of courses and educational material for teaching green software in current higher education. Additionally, we highlight three key pedagogical challenges in bringing green software to computer science curriculum and discussed existing solutions to address these key challenges. We firmly believe that 'green thinking"and the broad adoption of green software in computer science curriculum can greatly benefit our environment, society, and students in an era where software is everywhere and evolves in an unprecedented speed. © 2021 Owner/Author.

Abstract
A key aspect of any hash map design is the problem of dynamically resizing it in order to deal with hash collisions. In this context, elasticity refers to the ability to automatically resize the internal data structures that support the hash map operations in order to meet varying workloads, thus optimizing the overall memory consumption of the hash map. This work extends a previous lock-free hash trie design to support elastic hashing, i.e., expand saturated hash levels and compress unused hash levels, such that, at each point in time, the number of levels in a path matches the current demand as closely as possible. Experimental results show that elasticity effectively improves the search operation and, in doing so, our design becomes very competitive when compared to other state-of-the-art designs implemented in Java.

Abstract
Transactional isolation is a challenge for polystores, as along with the limited capabilities of each datastore, we have to contend with their sheer diversity. However, transactional isolation is increasingly desirable as a variety of datastores are being sought after for roles that go beyond data lakes. Transactional guarantees are also relevant for reliability at scale. In this paper, we propose that transactional isolation in polystores can be achieved by leveraging the query engine, i.e., basing some of the responsibilities of a traditional transactional storage manager (TSM) on the query language itself. This has the key advantage of greatly simplifying design and implementation, as it doesn’t need to be re-invented for each datastore, and should increase performance, by taking advantage of dynamic query optimization where available. We demonstrate the feasibility of the proposal with a simple proof-of-concept and experiment. © 2021, Springer Nature Switzerland AG.

Abstract
Enhancing tourist visits to cultural heritage sites by making use of mobile augmented reality has been a tendency in the last few years, presenting mainly audiovisual experiences. However, these explorations using only visuals and sounds, or narratives, do not allow users to be presented with, for example, a particular smell that can be important to feel engaged or to better understand the history of the site. This article pursues the goal of creating an experience that puts the user in a scene planned to evoke several stimuli with SensiMAR prototype - a Multisensory Augmented Reality system that aims to be used in cultural heritage outdoors. When using SensiMAR, the user will be involved with visual reconstructions, surrounded by the soundscape of ancient times, and is exposed to a particular smell very common that time. Given the novelty of this proposal, ascertaining the usability of such a system was raised as a foremost demand. Thus, in addition to its development and implementation specifications, an experimental study was conducted to evaluate the usability of the system in end-users' perspective. The results obtained from random visitors of an archaeological site were analysed according to their sex, age, previous experience with augmented reality technology, and provided condition - audiovisual condition, and multisensory condition, with visual, audio, and smell stimuli. Results were collected from a total of 67 participants and show that this multisensory prototype achieved good usability results across all groups. No statistically differences were found, demonstrating good usability of the SensiMAR system regardless of their sex, age, previous experience with the technology or provided condition.

Abstract
Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.

Abstract
Epilepsy is a major neurological disorder affecting approximately 1% of the world population, where seizure semiology is an essential tool for clinical evaluation of seizures. This includes qualitative visual inspection of videos from the seizures in epilepsy monitoring units by epileptologists. In order to support this clinical diagnosis process, promising deep learning-based systems were proposed. However, these indicate that video datasets of epileptic seizures are still rare and limited in size. In order to enable the full potential of AI systems for epileptic seizure diagnosis support and research, a novel collaborative development framework is proposed for a scalable DL-assisted clinical research and diagnosis support of epileptic seizures. The designed cloud-based approach integrates our deployed and tested NeuroKinect data acquisition pipeline into an MLOps framework to scale data set extension and analysis to a multi-clinical utilization. The proposed development framework incorporates an MLOps approach, to ensure convenient collaboration between clinicians and data scientists, providing continuous advantages to both user groups. It addresses methods for efficient utilization of HW, SW and human resources. In the future, the system is going to be expanded with several AI-based tools. Such as DL-based automated 3D motion capture (MoCap), 3D movement analysis support, quantitative seizure semiology analysis tools, video-based MOI and seizure classification. © 2021 IEEE