Abstract
To study how emotions affect people in expressive writing, scientists require tools to aid them in their research. The researchers at M-BW use an Experiment Management System, called HandSpy to store and analyze the hand-written productions of participants. The input is stored as digital ink and then displayed on a web-based interface. To assist the project, HandSpy integrates with new sources of information to help researchers visualize the link between psychophysiological data and written input. The newly acquired data is synchronized with the existing burst-pause interval model and represented on the user interface of the platform together with the already existing information.

Abstract
A learning path is the implementation of a curriculum design. It consists of a set of learning activities that help users achieve particular learning goals. Personalizing these paths became a significant task due to differences in users' limitations, backgrounds, goals, etc. Since the last decade, researchers have proposed a variety of learning path personalization methods using different techniques and approaches. In this paper, we present an overview of the methods that are applied to personalize learning paths as well as their advantages and disadvantages. The main parameters for personalizing learning paths are also described. In addition, we present approaches that are used to evaluate path personalization methods. Finally, we highlight the most significant challenges of these methods, which need to be tackled in order to enhance the quality of the personalization.

Abstract
Loss of motivation is one of the most prominent concerns in programming education as it negatively impacts time dedicated to practice, which is crucial for novice programmers. Of the distinct techniques introduced in the literature to engage students, gamification, is likely the most widely explored and fruitful. Game elements that intrinsically motivate students, such as graphical feedback and game-thinking, reveal more reliable long-term positive effects, but those involve significant development effort. This paper proposes a game-based assessment environment for programming challenges, built on top of a specialized framework, in which students develop a program to control the player, henceforth called Software Agent (SA). During the coding phase, students can resort to the graphical feedback demonstrating how the game unfolds to improve their programs and complete the proposed tasks. This environment also promotes competition through competitive evaluation and tournaments among SAs, optionally organized at the end by the teacher. Moreover, the validation of the effectiveness of Asura in increasing undergraduate students' motivation and, consequently, the practice of programming is reported.

Abstract
We present RAMBLE(1), a framework for georeferenced content-sharing in environments that have limited infrastructural communications, as is the case for rescue operations in the aftermath of natural disasters. RAMBLE makes use of mobile edge-clouds, networks formed by mobile devices in close proximity, and lightweight cloudlets that serve a small geographical area. Using an Android app, users ramble whilst generating geo-referenced content (e.g., text messages, sensor readings, photos, or videos), and disseminate that content opportunistically to nearby devices, cloudlets, or even cloud servers, as allowed by intermittent wireless connections. Each RAMBLE-enabled device can both produce information; consume information for which it expresses interest to neighboors, and; serve as an opportunistic cache for other devices. We describe the architecture of the framework and a case-study application scenario we designed to evaluate its behavior and performance. The results obtained reinforce our view that kits of RAMBLE-enabled mobile devices and modest cloudlets can constitute lightweight and flexible untethered intelligence gathering platforms for first responders in the aftermath of natural disasters, paving the way for the deployment of humanitary assistance and technical staff at large.

Abstract
Lock-free implementation techniques are known to improve the overall throughput of concurrent data structures. A hash map is an important data structure used to organize information that must be accessed frequently. A key role of a hash map is the ability to balance workloads by dynamically adjusting its internal data structures in order to provide the fastest possible access to the information. This work extends a previous lock-free hash map design to also support lock-free compression. The main goal is to significantly reduce the depth of the internal hash levels within the hash map, in order to minimize cache misses and increase the overall throughput. To materialize our design, we redesigned the existent search, insert, remove and expand operations in order to maintain the lock-freedom property of the whole design. Experimental results show that lock-free compression effectively improves the search operation and, in doing so, it outperforms the previous design, which was already quite competitive when compared against the concurrent hash map design supported by Intel. © Springer Nature Switzerland AG 2020.

Abstract
This work presents some approaches to overcome current Reinforcement Learning limits. We implement a simple virtual environment and some state-of-the-art Reinforcement Learning algorithms for testing and producing a baseline for comparison. Then we implement a Relational Reinforcement Learning algorithm that shows superior performance to the baseline but requires introducing human knowledge. We also propose that Model-based Reinforcement Learning can help us overcome some of the barriers. For better World models, we explore Inductive Logic Programming methods, such as First-Order Inductive Learner, and develop an improved version of it, more adequate to Reinforcement Learning environments. Finally we develop a novel Neural Network architecture, the Inductive Logic Neural Network, to fill the gaps of the previous implementations, that shows great promise. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.