Search results for "ComputingMilieux_PERSONALCOMPUTING"
showing 10 items of 256 documents
Towards a Game-Design Framework for Evidence-Based Clinical Procedure Libraries
2019
Serious games have been and currently are used for multiple purposes other than just entertainment, such as education, healthcare or emergency management. This research presents game-design elements based on specific functional and professional requirements among which usability plays a key role. The aim of the research is to steer the direction towards a game-design framework for evidence-based clinical procedure libraries (eCPL). For context analysis and game-element presentation, a "serious application" example is shown to illustrate the improvement through game-design elements. A context analysis was required to select suitable game-design elements to target system usability and solve s…
Determinants of Chairman Compensation
2011
This study examines determinants of chairman compensation in a supervisory board setting and, specifically, the relationship between chairman and CEO compensation. Using a sample of publicly listed firms in Sweden, the study indicates that chairman compensation – despite its fixed nature – is reflective of firm performance via a positive relationship to CEO compensation. As CEO compensation is set before chairman compensation, we argue that the chairman may be inclined to conspire with the CEO in earnings management efforts at the expense of monitoring on behalf of investors. Supporting our argument, we find evidence that the gap between chairman and CEO compensation is less at firms where …
Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation
2016
This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outpe…
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
2017
There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II fro…
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
2018
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. The…
On the Complexity of Solving Subtraction Games
2018
We study algorithms for solving Subtraction games, which sometimes are referred to as one-heap Nim games. We describe a quantum algorithm which is applicable to any game on DAG, and show that its query compexity for solving an arbitrary Subtraction game of $n$ stones is $O(n^{3/2}\log n)$. The best known deterministic algorithms for solving such games are based on the dynamic programming approach. We show that this approach is asymptotically optimal and that classical query complexity for solving a Subtraction game is generally $\Theta(n^2)$. This paper perhaps is the first explicit "quantum" contribution to algorithmic game theory.
Quantum-over-classical Advantage in Solving Multiplayer Games
2020
We study the applicability of quantum algorithms in computational game theory and generalize some results related to Subtraction games, which are sometimes referred to as one-heap Nim games. In quantum game theory, a subset of Subtraction games became the first explicitly defined class of zero-sum combinatorial games with provable separation between quantum and classical complexity of solving them. For a narrower subset of Subtraction games, an exact quantum sublinear algorithm is known that surpasses all deterministic algorithms for finding solutions with probability $1$. Typically, both Nim and Subtraction games are defined for only two players. We extend some known results to games for t…
Software startup education: gamifying growth hacking
2021
Startups seek to create highly scalable business models. For startups, growth is thus vital. Growth hacking is a marketing strategy advocated by various startup practitioner experts. It focuses on using low cost practices while utilizing existing platforms in creative ways to gain more users for the service. Though topics related to growth hacking such as marketing on a general level have been extensively studied in the past, growth hacking as a practitioner-born topic has not seen much interest among the academia. To both spark interest in growth hacking, and to facilitate teaching growth hacking in the academia, we present two board games intended to serve as an engaging introduction to g…
At Your Service: Coffee Beans Recommendation From a Robot Assistant
2020
With advances in the field of machine learning, precisely algorithms for recommendation systems, robot assistants are envisioned to become more present in the hospitality industry. Additionally, the COVID-19 pandemic has also highlighted the need to have more service robots in our everyday lives, to minimise the risk of human to-human transmission. One such example would be coffee shops, which have become intrinsic to our everyday lives. However, serving an excellent cup of coffee is not a trivial feat as a coffee blend typically comprises rich aromas, indulgent and unique flavours and a lingering aftertaste. Our work addresses this by proposing a computational model which recommends optima…
Optimization of Cultural Heritage Virtual Environments for Gaming Applications
2020
Serious games are games with a purpose beyond entertainment and are widely acknowledged as fruitful tools for learning and developing skills across multiple domains, including educational enhancement. In the last few years, the world of serious games has widely increased. The use of these types of games can aid in classrooms to not only help the students learn concepts but also to improve their motivation to do so. However, designing games necessitates very specialized personnel and the process can often be costly and slow. The adaptions of the design to the implantation phase are also difficult and the process needs more focus. The challenge of this study was to create a game within the co…