Miracle-dz/UniFAR-FTUs huggingface dataset:
Lume: a system for procedural story generation

Procedural storytelling offers immense promise for games to offer more reactive narrative experiences that feel more deeply tailored to players' decisions. To date, interactive narrative systems have tended toward either a large emergent possibility space with less focus on narrative structure, or toward greater structure with smaller possibility spaces. In this paper, we introduce Lume, a system for procedural narrative generation that combines the best of these two approaches through a novel combinatorial scene architecture in which storylet scenes are comprised of parameterized node-trees. We detail how the system works and discuss how it moves toward creating reactive interactive narratives that are both dynamic and coherent.
Comprehensive Study of Continuous Orthogonal Moments—A Systematic Review

Orthogonal moments provide an efficient mathematical framework for computer vision, image analysis, and pattern recognition. They are derived from the polynomials that are relatively perpendicular to each other. Orthogonal moments are more efficient than non-orthogonal moments for image representation with minimum attribute redundancy, robustness to noise, invariance to rotation, translation, and scaling. Orthogonal moments can be both continuous and discrete. Prominent continuous moments are Zernike, Pseudo-Zernike, Legendre, and Gaussian-Hermite. This article provides a comprehensive and comparative review for continuous orthogonal moments along with their applications.
Space, Place and Practice in Computing Education

In this paper, we explore the relationship of space, place and practice in computing education. We first provide an overview of practice theories before selecting a specific theory of practice that consists of three components: materials, competence, and meaning. We then use this framework as a lens to consider a variety of learning spaces that are available to computing students at the University of Kent -- a common room, computer lab, and makerspace -- and how they negotiate them. Specifically, we draw on data collected as part of focus group interviews and discuss the different practices apparent in these spaces. The value of this approach lies in its ability to expose otherwise invisible student behaviours.
Jamming with children: an experience report

Although there is an increase in Science, Technology, Engineering, and Mathematics (STEM) career opportunities in most developed economies, the lack of qualified graduates to take these opportunities is a serious concern. Increasing participation in STEM education and experiences has the potential to address this issue. Furthermore, through increasing diversity of STEM qualified graduated not only has the potential to increase participation in STEM careers, but also assist in increasing product and service variety. In this submission the author presents an experience report of a game development summer camp and game jam for girls aged 9-11 years-old to help them form positive perceptions about computer science (CS) and STEM.
Unsupervised Approaches for Textual Semantic Annotation, A Survey

Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.
Affordances of brainstorming toolkits and their use in game jams

Brainstorming is an important part of creative design, related to exploring the associativity of different ideas and the combination of their parts. Previous work has investigated social interactions, productivity, techniques, and quality of brainstorming activities. The paper contributes to this body of work by studying the design affordances of brainstorming toolkits, and the relationship between idea associations and brainstorming. We examined 21 brainstorming toolkits designed for creative brainstorming within the context of game design, which led us to four tiers of design affordances that specify the supportive qualities of toolkits for brainstorming. To gain further insights into the use of tools, we surveyed game jams participants about their brainstorming activities and the use of tools at Global Game Jam 2017. We found a large number of participants using traditional stationery to aid brainstorming and a common usage of mind mapping and rearranging post-it notes. These findings inform our discussion of how idea creation is leveraged by a hybrid use of traditional and digital tools.
Automated Reuse of Model Transformations through Typing Requirements Models

Model transformations are key elements of model-driven engineering, where they are used to automate the manipulation of models. However, they are typed with respect to concrete source and target meta-models, making their reuse for other (even similar) meta-models challenging. To improve this situation, we propose capturing the typing requirements for reusing a transformation with other meta-models by the notion of a typing requirements model (TRM). A TRM describes the prerequisites that a model transformation imposes on the source and target meta-models to obtain a correct typing. The key observation is that any meta-model pair that satisfies the TRM is a valid reuse context for the transformation at hand. A TRM is made of two domain requirement models (DRMs) describing the requirements for the source and target meta-models, and a compatibility model expressing dependencies between them. We define a notion of refinement between DRMs and see meta-models as a special case of DRM. We provide a catalogue of valid refinements and describe how to automatically extract a TRM from an ATL transformation. The approach is supported by our tool TOTEM. We report on two experiments—based on transformations developed by third parties and meta-model mutation techniques—validating the correctness and completeness of our TRM extraction procedure and confirming the power of TRMs to encode variability and support flexible reuse.
Towards an End-to-End Human-Centric Data Cleaning Framework

Data Cleaning refers to the process of detecting and fixing errors in the data. Human involvement is instrumental at several stages of this process such as providing rules or validating computed repairs. There is a plethora of data cleaning algorithms addressing a wide range of data errors (e.g., detecting duplicates, violations of integrity constraints, and missing values). Many of these algorithms involve a human in the loop, however, this latter is usually coupled to the underlying cleaning algorithms. In a real data cleaning pipeline, several data cleaning operations are performed using different tools. A high-level reasoning on these tools, when combined to repair the data, has the potential to unlock useful use cases to involve humans in the cleaning process. Additionally, we believe there is an opportunity to benefit from recent advances in active learning methods to minimize the effort humans have to spend to verify data items produced by tools or humans. There is currently no end-to-end data cleaning framework that systematically involves humans in the cleaning pipeline regardless of the underlying cleaning algorithms. In this paper, we present opportunities that this framework could offer, and highlight key challenges that need to be addressed to realize this vision. We present a design vision and discuss scenarios that motivate the need for this framework to judiciously assist humans in the cleaning process.
Wormingo: a 'true gamification' approach to anaphoric annotation

In this paper we present Wormingo, 1 a new Game-with-a-Purpose for anaphoric annotation. It introduces the motivation-annotation paradigm which uses linguistic puzzles and other widely known gamification techniques and word game mechanics to motivate players to carry out anaphoric annotation tasks. In a preliminary experiment, the game was tested on 270 players recruited through the Reddit platform, achieving promising results.
Spatial Structure Preserving Feature Pyramid Network for Semantic Image Segmentation

Recently, progress on semantic image segmentation is substantial, benefiting from the rapid development of Convolutional Neural Networks. Semantic image segmentation approaches proposed lately have been mostly based on Fully convolutional Networks (FCNs). However, these FCN-based methods use large receptive fields and too many pooling layers to depict the discriminative semantic information of the images. Specifically, on one hand, convolutional kernel with large receptive field smooth the detailed edges, since too much contexture information is used to depict the “center pixel. ” However, the pooling layer increases the receptive field through zooming out the latest feature maps, which loses many detailed information of the image, especially in the deeper layers of the network. These operations often cause low spatial resolution inside deep layers, which leads to spatially fragmented prediction. To address this problem, we exploit the inherent multi-scale and pyramidal hierarchy of deep convolutional networks to extract the feature maps with different resolutions and take full advantages of these feature maps via a gradually stacked fusing way. Specifically, for two adjacent convolutional layers, we upsample the features from deeper layer with stride of 2 and then stack them on the features from shallower layer. Then, a convolutional layer with kernels of 1× 1 is followed to fuse these stacked features. The fused feature preserves the spatial structure information of the image; meanwhile, it owns strong discriminative capability for pixel classification. Additionally, to further preserve the spatial structure information and regional connectivity of the predicted category label map, we propose a novel loss term for the network. In detail, two graph model-based spatial affinity matrixes are proposed, which are used to depict the pixel-level relationships in the input image and predicted category label map respectively, and then their cosine distance is backward propagated to the network. The proposed architecture, called spatial structure preserving feature pyramid network, significantly improves the spatial resolution of the predicted category label map for semantic image segmentation. The proposed method achieves state-of-the-art results on three public and challenging datasets for semantic image segmentation.
Building and sustaining large, long-term online communities: family business and gamifying the game

This work examines how leaders of guilds, a type of community found in many online multiplayer games, build and sustain large online groups of 400 to 500 people, considerably larger than previous work has suggested is possible. Using a sequential mixed-method design that focused on interviewing guild leaders of large guilds in the game The Elder Scrolls Online, several items were realized. Guild leaders found community management tools and methods unremarkable, reflecting the importance of their ease of use. Leaders need the ability to exclude unwanted persons and perhaps to include wanted members. Non-redundant channels of communication are needed, these included ephemeral voice for immediacy, and text and photos for a longer-lasting record of communication. Keeping members active increases engagement, but members, as human beings, only have so much capacity for strong ties: many in large groups will be loosely tied to others in the group.
Software Development Lifecycle for Energy Efficiency

Motivation: In modern it systems, the increasing demand for computational power is tightly coupled with ever higher energy consumption. Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level. Nevertheless, the software itself provides numerous opportunities for improving energy efficiency. Goal: Given that energy efficiency for it systems is a rising concern, we investigate existing work in the area of energy-aware software development and identify open research challenges. Our goal is to reveal limitations, features, and tradeoffs regarding energy-performance for software development and provide insights on existing approaches, tools, and techniques for energy-efficient programming. Method: We analyze and categorize research work mostly extracted from top-tier conferences and journals concerning energy efficiency across the software development lifecycle phases. Results: Our analysis shows that related work in this area has focused mainly on the implementation and verification phases of the software development lifecycle. Existing work shows that the use of parallel and approximate programming, source code analyzers, efficient data structures, coding practices, and specific programming languages can significantly increase energy efficiency. Moreover, the utilization of energy monitoring tools and benchmarks can provide insights for the software practitioners and raise energy-awareness during the development phase.
Roguelike ancestry network visualisation: insights from the roguelike community

Idea networks are inherently illustrative of the domain knowledge they seek to capture. They provide pathways to retrace known paths, and to explore the unknown. In this study, we conceptualised an idea network of 639 roguelike games, using data collected from RogueBasin and other sources, and conducted a user study for designer insight. Study participants interacted with three idea network visualisations that support exploration of design influence, accompanied by a brief historical context of roguelike games, and our design process of visualisations. The study uncovered 6 classes of insights from game developers: thinking in networks, insights on data consistency, accessibility and usability, visualisation preferences, exploring related games, and data contributions from the community. The study is limited by the data sources available about design influences, acknowledging that idea networks are only a partial representation of the full picture, which cannot be known. However, this paper shows that even a partial idea network can be useful for exploring the ancestry of roguelikes and their design influences. It also showed how idea networks can facilitate knowledge sharing through online conversations and new lines of inquiry.
Understanding the Photo-shooting Patterns of Sightseeing

In this paper, we investigate whether photographers' preference in shooting patterns could be a reliable index to represent the sightseeing value of a travel spot. To this end, we collected 30k geotagged images from 30 scenic spots over 4 continents. First, an effective framework based on convolution neural network (CNN) is proposed to establish the photographer's preferred shooting patterns (wide- or narrow-angle) inferred from a single photo. Subsequently, our statistical results reveal that the ratio of photo-shooting patterns at a spot strongly correlates with its sightseeing value. The results suggest that photographers prefer to take wide-angle photos at the spot with high value, which also accords to the notion in positive psychology - "positive emotions broaden the scope of attention". In addition, we mapped the spatial distribution of photographers' shooting patterns to find out common regions of interests within a spot and discuss the potential applications that could arise from our study.
An Improved Clustering Algorithm based on Single-pass

Topic Detection and Tracking is a popular topic clustering method in the big data age, which aims at automatic recognition of new topics and continuous tracking of known topics in news information flow. Traditional Topic Detection and Tracking mainly studies short text. With the rapid development of digital devices and communication techniques, the news is going to be longer and richer. So nowadays traditional Topic Detection and Tracking is faced with three problems, first, long news text usually contains multiple topics, so traditional clustering algorithm cannot accurately identify them. Second, traditional clustering mostly uses multi-dimensional computation based on word bag, but the time-consuming of this multi-dimensional computation increases exponentially with the increase of the length and number of articles. Third, long-text news contains more information. How to show the continuity and relevance of long-text news in a better way is very important and meaningful. Therefore, an improved clustering algorithm based on single-pass is presented in this paper, which can solve the above problems primly. Experiments show that, compared with K-means clustering algorithm, agglomerative hierarchical clustering algorithm, Density-Based Spatial Clustering of Applications with Noise and hierarchical clustering on the constructed concept graph, the accuracy of this algorithm is improved by about 20% to 30%, the recall rate is increased by 10% to 20%, and the algorithm time is reduced by more than 40%. With the increase of the number of articles, the time-consuming curve of the improved single-pass clustering algorithm approximates a linear function. For each additional article, the time required for the algorithm is only 0.1-0.5 times that of other algorithms. Besides, by adding timelines and extracting topics in the theme during presentation, the algorithm can effectively mine the continuity and relevance information of news topics and track the changes of news topics.
Set of Usability Heuristics for Quality Assessment of Mobile Applications on Smartphones

The innovations proposed by the cell phone market have grown steadily in recent years, along with the increasing complexity of the hardware, operating systems, and applications available in this market. These changes bring new challenges related to usability that need to be considered during the development process of these applications since the new forms of user-application interactions increasingly require adapting the behavior of smartphone users. In this situation, usability is an important issue that depends on factors such as the Users, their characteristics and abilities, the Task which the users intend to achieve and also the application usage Context. This work presents a systematic literature review with the objective of identifying the heuristics and usability metrics used in the literature and/or industry. Based on the review results, this work presents another contribution with a proposal of a set of usability heuristics focused in mobile applications on smartphones, considering the User, Task and Context as usability factors and Cognitive Load as an important attribute of usability. The components of this set are detailed in a model intended to be used in empirical validations allowing to dynamically incorporate improvements to the proposal.
DeltaTouch: a 3D Haptic Display for Delivering Multimodal Tactile Stimuli at the Palm

DeltaTouch is a novel wearable haptic display with inverted Delta structure for providing multimodal tactile stimuli at any point of the palm of Virtual Reality (VR) user or operator of remote robot. It is capable of generating 3D force vector at the contact point and presenting multimodal tactile sensation of weight, slippage, encounter, and texture. The BallFeel, BalanceIt, and AnimalFeel applications have been developed to demonstrate the capabilities of the proposed technology. In the first user study, we evaluated the perception of tactile cues delivered by the haptic display on a participants’ palm. The second experiment estimated the distinction in the perception of different forces generating by DeltaTouch on the human palm in a virtual weight sorting task. DeltaTouch can potentially bring a new level of immersion of the user in VR and improve the quality of teleoperation of the robot equipped with tactile and force sensors.
Command Acknowledge through Tactile Feedback Improves the Usability of an EMG-based Interface for the Frontalis Muscle

This work presents a study on the effectiveness of tactile feedback for the acknowledgement of a correct command detection in an EMG-based interface for the frontalis muscle. EMG interfaces are increasingly used in assistive robotics to control robots exploiting the repeatability and robustness of the electromyographic signal. However, in many application a feedback about the correct detection of an input is often missed and the user has to wait for the device motion in order to understand if his/her will has been correctly detected by the system. We demonstrate with a user study involving fifteen subjects, that a simple vibrotactile feedback can reduce the muscular effort and the time needed to execute a sequence of action commanded by an EMG device. As a case study, an EMG interface for the frontalis muscle has been used, however proposed results could be extended to EMG interfaces designed for other muscles, e.g., for prosthesis or exoskeleton control.
Micro-Doppler Extraction of Bicycle Pedaling Movements Using Automotive Radar

The specific detection and classification of vulnerable road users in the closer vehicle environment is the foundation for safe autonomous driving of the future. In addition, the capability of predictive intention recognition is of fundamental interest in achieving the goal of “Vision Zero”. High-resolution radars in the short-range enable the detection of cyclist-identifying micro-Doppler distributions. This paper presents a method for the extraction of micro-range-Doppler pedaling motions of cyclists in highly relevant movement scenarios. An adaptive ellipse fitting algorithm separates the high-resolution micro-Doppler components of the wheels from the pedaling motions and enables their robust extraction for the potential use of anticipatory safety functions. The proposed procedure is applied to real radar measurements of a cyclist. The results show that the time-dependent motion behavior of the pedals and contributing parts of the legs can be obtained from the radar data.