Comprehensive difficulty in a game can be defined as the challenge presented by learning the game's system, encompassing fundamental rules, objectives, and parameters. Achieving a well-balanced difficulty level is crucial for a game's success. In the industry, rules and objectives are commonly designed using a Static Game Balance (SGB) system. This involves human designers handcrafting elements such as game mechanics and the core behavior of enemies. In the early stages of game production, particularly during prototyping without access to player data, it becomes challenging to objectively assess the difficulty of these manually designed elements. Limited research has addressed automatic difficulty assessment in the context of SGB, with most studies focusing on executive difficulty, which pertains to a player's motor skills, such as dexterity. However, the industry is in need of more automated software tools to optimize game production. In this paper, we propose a novel method for automatically measuring the comprehensive difficulty of game enemies. Our approach, owing to its generalizability and the standardized way of defining enemies using state machines, lever-ages the properties of graphs (ex. number of states, transitions, cyclomatic complexity, etc.) to establish a standardized model for comprehensive difficulty assessment in games. We present this model along with the results of an initial exploratory experiment, demonstrating the potential of our approach and its feasibility for integration as a plugin in game engines like Unreal Engine 5.
The representation of politics in media influences societal perceptions and attitudes. Video games, as a pervasive form of media, contribute significantly to this phenomenon. In this work, we explore political themes within video games by analyzing politically-themed games on game distribution plat-forms including Steam. We conducted a statistical examination of games with political context to identify patterns and use this as a basis to introduce a first taxonomy to categorize and better understand the interplay between politics and video games. This taxonomy offers a first framework for analyzing political content in games and also sets a foundation for future research in this field
The rapid advancement of generative artificial in-telligence (AI) has led to groundbreaking developments in large language models. As large language models generate textual sequences autoregressively, mitigating latency becomes imper-ative for providing a highly immersive interaction experience within a realtime conversation, for example, providing fast and accurate responses to users' questions. Current efforts focus on accelerating inference processes, yet often at the expense of model architecture alterations, leading to compromised quality. In this paper, we explore latency reduction in the case of speech-based conversational agents. We leverage mathematical functions based on Beam Search to analyze autoregressive textual sequences, enabling a nuanced evaluation of semantic quality during auditory interaction, for example, for use within interactive web podcasts. We implemented our concepts and used the software to evaluate the concepts within (1) an automated evaluation of 1000 question-answer pairs and (2) a user survey. The results show that the semantic quality of autoregressive textual sequences could be assessed successfully by our proposed mathematical terms.
Recently, the importance of having a representation of the users inside Virtual Reality (VR) by using avatars, which replicate the movements of real humans, has grown in several fields of application. Nowadays, developers can use many off-the-shelf devices for visualizations and tracking. Game engines like Unity3D and Unreal allow to easily integrate packages to manage head-mounted displays and other tracking devices. However, it is still difficult to combine different solutions and achieve a full representation of the human body, e.g., combining full-body and hand tracking or switching among different tracking modalities in an easy way. This paper describes IMMERSE, an open-source framework based on Unity3D, the XR Interaction toolkit, and an inverse kinematics solver. IMMERSE allows developers to insert a VR avatar in a virtual environment, animate it through different sources of 6DOF tracking measurements, which are also interchangeable during the simulation, and finally record the movements to animate other avatars or input to motion analysis techniques.
This paper explores the transformation of Mahjong within Japanese internet society. With deep cultural ties to China, Mahjong has seamlessly integrated into Japanese society, adapting captivatingly to the digital era. The paper traces Mahjong's journey in Japan, from its introduction in the early 20th century to the post-World War II era. Despite a temporary setback in 2020 during the COVID-19 pandemic, the paper reveals a noteworthy resurgence, with the Mahjong player population in Japan rebounding to 4.5 million in 2021. Contributing factors include the establishment of the professional Mahjong league (M-League) and increased accessibility through platforms like YouTube and internet TV, transforming Mahjong into a spectator sport. The discussion explores the intersection of Mahjong with the Vtuber industry, showcasing collaborative efforts that positively shape the game's perception. This fusion of cultural elements and technological advancements breaks away from traditional negative stereotypes, emphasizing Mahjong's adaptability and contemporary relevance. In conclusion, the paper offers a comprehensive analysis of Mahjong's journey within Japanese internet society, shedding light on its historical roots, digital transformation, and contemporary resurgence, significantly contributing to understanding the evolving landscape of online games.
In this work, we propose decentralized collaborative learning adopted for detecting deepfake videos in entertainment. Most state-of-the-art works in this field utilize a centralized learning mechanism of Artificial Intelligence (AI), which may pose privacy concerns for participants using actual image datasets, as data needs to be forwarded to a central server. To mitigate this, Federated Learning (FL) has been developed. In this work, the aggregation mechanism is performed in a decentralized manner for enhanced security, as the centralized FL approach is prone to single points of failure. The proposed decentralized collaborative learning is trained by each participant using real and deepfake video datasets. These videos are sampled and trained individually before being forwarded to other participants in the proposed collaborative system. In this work, continual learning mechanisms are employed to reduce the communication overhead required during the aggregation process. A total of 953 videos, each with an average duration of 10 seconds, were utilized for training and evaluation. The results demonstrate that the proposed decentralized collaborative learning approach can achieve satisfactory performance, with an accuracy of 97.30%, while also preserving participant privacy and minimizing communication costs.
Exergames - i.e. games devised to promote exercising, to engage people in physical activities - demonstrated their potential in rehabilitation, especially for facing the challenges due to motor dysfunctions and cognitive impairments that sig-nificantly impact activities of daily living (ADLs). Accordingly, this paper presents an investigation of the potential of Mixed Reality (MR, when digital objects behave like physical ones in a real setting) in exercises for upper limbs, based on the needs of individuals with Multiple Sclerosis (MS), an inflammatory demyelinating disease of the central nervous system. The developed exergame requires the user to manipulate real objects while keeping in balance a virtual object in MR placed on top. The game focuses on repetitive movements towards specific target positions and provides two conditions, one involving the use of a single arm and the other requiring coordination of both arms. The final goal of proposing these two conditions is to assess the feasibility and potential of introducing bimanual tasks (often requested to perform ADLs in daily life) in rehabilitation. The system has been preliminarily tested by people without MS in order to gather data for evaluating user experience. MS specialists have contributed to the game design and the definition of the assessment protocol. Current results show that bimanual tasks are feasible in this MR setting, suggesting further improvements for extensive clinical tests.
WebGL has been the standard API for rendering graphics on the web over the years. A new technology, WebGPU, has been set to release in 2023 and utilizes many of the novel rendering approaches and features common for the native modern graphics APIs, such as Vulkan. Currently, very limited research exists regarding WebGPU's rasterization capabilities. In particular, no research exists about its capabilities when used as a rendering backend in game engines. This paper aims to investigate performance differences between WebGL and WebGPU. It is done in the context of the game engine Godot, and the measured performance is that of the CPU and GPU frame time. The results show that WebGPU performs better than WebGL when used as a rendering backend in Godot, for both the games tests and the synthetic tests. The comparisons clearly show that WebGPU performs faster in mean CPU and GPU frame time.
This study aims to explore the application of game ranking mechanisms in the evaluation of skill-based courses in higher education. As skill-based courses become increasingly important in higher education, effectively assessing student learning outcomes in these courses has emerged as a critical issue. The game ranking mechanism, an evaluation method based on achievements and progress, has been widely used in the gaming industry. This research investigates the potential value and application models of game ranking mechanisms in the evaluation of skill-based courses in universities, proposing an evaluation framework based on this mechanism. The goal is to stimulate students' learning motivation, provide personalized evaluation feedback, and promote self-directed learning and growth. Experiments are conducted on the design and implementation standards of the mechanism and students' acceptance of it. This offers a new perspective and method for evaluating skill-based courses in universities, aiming to enhance the effectiveness of evaluations and boost students' learning motivation.
In the realm of Virtual Reality (VR), the exploration of players' modes of free movement has persistently been a pivotal research focus. This study introduces a novel locomotion approach, specifically employing gaze-directed instantaneous destination selection, aiming to enhance the current methods of movement in VR games and elevate the overall user experience. Through the creation of corresponding game scenarios, we assess the instantaneous movement performance in terms of efficiency, precision, and comfort for both gaze-directed and controller-directed destination selection. Statistical analyses reveal that, in terms of efficiency, gaze-directed instantaneous destination selection outperforms controller-directed movement. However, in the evaluation of comfort, controller-directed movement surpasses gaze-directed instantaneous destination. The findings of this research not only present a viable alternative in movement methodology but also underscore the necessity of striking a balance between efficiency and comfort in VR game design to deliver an enhanced gaming experience.
In recent years, the application of synthetic humans in various fields has attracted considerable attention, leading to extensive exploration of their integration into the Metaverse and virtual production environments. This work presents a semi-automated approach that aims to find a fair trade-off between high-quality outputs and efficient production times. The project focuses on the Rai photo and video archives to find images of target characters for texturing and 3D reconstruction with the goal of reviving Rai's 2D footage and enhance the media experience. A key aspect of this study is to minimize the human intervention, ensuring an efficient, flexible, and scalable creation process. In this work, the improvements have been distributed among different stages of the digital human creation process, starting with the generation of 3D head meshes from 2D images of the reference character and then moving on to the generation, using a Diffusion model, of suitable images for texture development. These assets are then integrated into the Unreal Engine, where a custom widget facilitates posing, rendering, and texturing of Synthetic Humans models. Finally, an in-depth quantitative comparison and subjective tests were carried out between the original character images and the rendered synthetic humans, confirming the validity of the approach.
Infiltration or stealth-based games are a signifi-cant genre in the gaming industry, centered around undetected navigation through levels, avoiding guards, cameras, and other security mechanisms. Level design for such games is a complex challenge, raising questions about guard placement, patrol routes, player viability, level difficulty, and overall player experience. Despite its importance, very few scientific works addressed the challenge in the past. It is why this paper introduces an innovative procedural generation technique for automatically de-signing maps, positioning guards, determining their patrol paths, and evaluating level difficulty, with broad applicability across various game types. Implemented in Unity 3D, our method was validated by generating over five hundred maps, demonstrating its effectiveness and the high quality of the generated levels.
The current availability of low-cost immersive Virtual Reality (VR) systems, opens interesting perspectives for neurorehabilitation of people with Multiple Sclerosis (pwMS). However, it is known that immersive VR can trigger the onset of unpleasant effects such as dizziness, headache, nausea, disorientation and balance alterations. Since MS is associated with similar symptoms, it appears important to clarify the role of disability in exacerbating such possible negative effects. To this aim, we combined instrumental data on postural sway (using Center of Pressure COP data) and perceived discomfort in 56 pwMS and 33 unaffected individuals before and after a 10-minute session of immersive VR consisting in a simulated rollercoaster. The results indicate the presence of temporary impairments of postural control in terms of significantly increased sway area in presence of visual input. Interestingly, when the visual input was removed, a reduction of sway area and COP path length in pwMS with mild-moderate disability and unaffected individuals was observed, thus suggesting the existence of a sort of "balance training" effect. All participants complain about blurred vision and nausea after the VR session. In pwMS, changes in most postural sway parameters originated by the VR session were found moderately correlated with changes in SSQ symptoms scores, particularly those of the disorientation domain. Such findings seem to indicate that a short immersive VR session originates contrasting effects on postural control of pwMS, according to their disability level and to the presence of the visual sensory input. At the same time, participants relevant negative symptoms especially in the disorientation domain. Although immersive VR can be generally considered safe and tolerable, the use in pwMS requires the definition of specific guidelines on aspects like exposure time, posture to assume during use and assessment of possible adverse effects to guarantee efficacy of the treatment, safety and comfort.
The primary goal of mirror therapy is to alleviate symptoms and improve motor function and perception. It involves using a mirror to create a visual illusion that the affected limb is moving regularly and painlessly. Mirror therapy is often used in conjunction with traditional physical and occupational therapy methods and has been studied for post-stroke rehabilitation. However, mirror therapy effectiveness can vary among individuals. Virtual reality mirror therapy (VRMT) is an advanced application of mirror therapy that utilises virtual reality technology to enhance rehabilitation. While traditional mirror therapy uses a physical mirror to create the illusion of movement in the affected limb, VR mirror therapy takes advantage of immersive digital environments to provide a more engaging and customizable experience. This approach is particularly beneficial for stroke rehabilitation. The paper summarises four key design factors: e dosage, motivation, task difficulty and sensory feedback. In addition, it indicates the potential role of mirror neurons in both mirror therapy and VRMT and highlights three areas for future VRMT studies.
In today's era, where environmental concerns are escalating, educating the younger generation about these issues is paramount. The RAISE gamified learning environment introduces an innovative approach to fostering environmental awareness among school students by leveraging the capabilities of a 3D virtual world learning environment. This platform enables students to engage in immersive game scenarios that mirror real-world environmental challenges, facilitating experiential learning. Alternatively, it offers interactive sessions where students can delve into discussions about environmental sustainability, enabling them to understand and reflect on their roles in combating climate change and broader environmental issues. This initiative examines the impact of such a virtual learning environment on students' understanding of environmental issues, their engagement with the topic, and the development of proactive attitudes towards sustainability. Preliminary feedback from the implementation of the alpha version at Arsakeia Schools, involving students aged between 10 to 15 years, indicates a positive reception towards this educational approach. Ultimately, the RAISE gamified learning environment demonstrates significant potential in elevating students' understanding and engagement with environmental issues, as preliminary results from semi-structured interviews reveal insightful feedback on both the educational impact and areas for further enhancement.
Previous research studying music's effect on video games has focused on musical properties, such as tempo, to create particular emotional player experiences. However, music is complex, and selecting music using a particular parameter may not guarantee that the music will be experienced in a particular way (e.g., higher-tempo music will not necessarily make a player feel more rushed, as previous work implies). Through a player study, we demonstrate that music labelled by its emotional content (e.g., peaceful or powerful) could provide a better means for designers to choose music for particular emotional effects. Our results show that powerful (rather than higher tempo) music can significantly increase experienced tension and risk-taking play style compared to peaceful music. We provide game designers and composers with critical new information about how music can be chosen and designed to target play experience and shape player behaviour, suggesting that music's effects in gameplay need to be studied more holistically.
Fuzzy optimization problems play a substantial role in Information Fusion (ISO4: Inf Fusion) and related Multi-Criteria Decision Making (MCDM) problems. These problems tend to be complex because the measures/objectives tend to conflict with each other. Researchers have utilized various metaheuristic approaches for addressing these challenges, such as the obtaining of the Optimal Shapley-Nondominated Solution (OSNS) to contend with the Fuzzy Number (FN) coefficient issue, among others. In this paper, a bepoke Robust Convex Relaxations (RCR)-centric Particle Swarm Optimization (PSO) metaheuristic approach is utilized along with a unique Lower Ambiguity, Higher Uncertainty (LAHU) and Higher Ambiguity, Lower Uncertainty (HALU) Module (LHM) for not only seeking the OSNS, but also an enhanced approximation of nonlinear Spherical FNs (SFNs) via an Optimal Corresponding Generalized Linear “f”-sided SFN form (OCGLfSFN)-based membership function (in accordance with the involved ambiguity and uncertainty). The LHM seeks to ascertain whether an Isomorphic Paradigm (IsoP) has occurred before within the historical data (e.g., the IsoP has already been analyzed, gameplayed, etc.) via a bespoke Isomorphic Comparator Similarity Measure (ICSM) (for FNs/SFNs/[Spherical Fuzzy Sets] SFSs) that leverages OSNS and OCGLfFN.
Traditional teaching methods often struggle to effectively engage students, especially in educational environments with limited resources. Recognizing the potential of gamification to address this challenge, Sapinautas emerges as an educational project aimed at harnessing video games to enhance learning experiences in Honduran public schools, improving learning outcomes and student engagement through immersive and interactive gaming experiences. This article describes the conceptualization, design, and implementation process of the first phase of the Sapinautas platform. The project has been developed under the guidance of their promoters in collaboration with students and professors from Universidad Tecnologica Centroamericana (UNITEC) in Honduras. The development of Sapinautas involved a multi-phase process, beginning with a comprehensive analysis of the educational context and needs. This led to the conceptualization and design of the video game, with students collaborating with educators to create characters, environments, and gameplay mechanics aligned with the National Basic Curriculum. The implementation phase included work in the Unreal Engine, where assets were combined with programming and game logic. At this stage, the capabilities of the engine were leveraged to create an interactive and immersive environment to begin the creative development for the entire project. Sapinautas represents a promising approach to improving education through gamification. In the future, conducting pilot tests of the game and collaborating with education stakeholders will be crucial to scale the project and realize its full potential as a transformative tool for education.
We present a framework to assist therapists and children with autism spectrum disorder in their Applied Behavioral Analysis (ABA) therapy. The framework was designed in collaboration with Spazio Autismo, an autism center in Mantova, Italy. The framework is a first step toward transitioning from the current paper-based to fully digital-supported therapy. We evaluated the framework over four months with 18 children diagnosed with classic autism, ranging from 4 to 7 years old. The framework integrates a mobile app that children and therapists use during the sessions with a backend for managing therapy workflow and monitoring progress. Our preliminary results show that the framework can improve the efficacy of the therapy sessions, reducing non-therapeutic time, increasing patient focus, and quickening the completion of the assigned objectives. It can also support therapists in preparing learning materials, data acquisition, and reporting. Finally, the framework demonstrated improved privacy and security of patients' data while maintaining reliability.
