251 research outputs found
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
OddAssist - An eSports betting recommendation system
It is globally accepted that sports betting has been around for as long as the sport itself. Back in
the 1st century, circuses hosted chariot races and fans would bet on who they thought would
emerge victorious. With the evolution of technology, sports evolved and, mainly, the
bookmakers evolved. Due to the mass digitization, these houses are now available online, from
anywhere, which makes this market inherently more tempting. In fact, this transition has
propelled the sports betting industry into a multi-billion-dollar industry that can rival the sports
industry.
Similarly, younger generations are increasingly attached to the digital world, including
electronic sports – eSports. In fact, young men are more likely to follow eSports than traditional
sports. Counter-Strike: Global Offensive, the videogame on which this dissertation focuses, is
one of the pillars of this industry and during 2022, 15 million dollars were distributed in
tournament prizes and there was a peak of 2 million concurrent viewers. This factor, combined
with the digitization of bookmakers, make the eSports betting market extremely appealing for
exploring machine learning techniques, since young people who follow this type of sports also
find it easy to bet online.
In this dissertation, a betting recommendation system is proposed, implemented, tested, and
validated, which considers the match history of each team, the odds of several bookmakers and
the general feeling of fans in a discussion forum.
The individual machine learning models achieved great results by themselves. More specifically,
the match history model managed an accuracy of 66.66% with an expected calibration error of
2.10% and the bookmaker odds model, with an accuracy of 65.05% and a calibration error of
2.53%.
Combining the models through stacking increased the accuracy to 67.62% but worsened the
expected calibration error to 5.19%. On the other hand, merging the datasets and training a
new, stronger model on that data improved the accuracy to 66.81% and had an expected
calibration error of 2.67%.
The solution is thoroughly tested in a betting simulation encapsulating 2500 matches. The
system’s final odd is compared with the odds of the bookmakers and the expected long-term
return is computed. A bet is made depending on whether it is above a certain threshold. This
strategy called positive expected value betting was used at multiple thresholds and the results
were compared.
While the stacking solution did not perform in a betting environment, the match history model
prevailed with profits form 8% to 90%; the odds model had profits ranging from 13% to 211%;
and the dataset merging solution profited from 11% to 77%, all depending on the minimum
expected value thresholds.
Therefore, from this work resulted several machine learning approaches capable of profiting
from Counter Strike: Global Offensive bets long-term.É globalmente aceite que as apostas desportivas existem há tanto tempo quanto o próprio
desporto. Mesmo no primeiro século, os circos hospedavam corridas de carruagens e os fãs
apostavam em quem achavam que sairia vitorioso, semelhante às corridas de cavalo de agora.
Com a evolução da tecnologia, os desportos foram evoluindo e, principalmente, evoluíram as
casas de apostas. Devido à onda de digitalização em massa, estas casas passaram a estar
disponíveis online, a partir de qualquer sítio, o que torna este mercado inerentemente mais
tentador. De facto, esta transição propulsionou a indústria das apostas desportivas para uma
indústria multibilionária que agora pode mesmo ser comparada à indústria dos desportos.
De forma semelhante, gerações mais novas estão cada vez mais ligadas ao digital, incluindo
desportos digitais – eSports. Counter-Strike: Global Offensive, o videojogo sobre o qual esta
dissertação incide, é um dos grandes impulsionadores desta indústria e durante 2022, 15
milhões de dólares foram distribuídos em prémios de torneios e houve um pico de espectadores
concorrentes de 2 milhões. Embora esta realidade não seja tão pronunciada em Portugal, em
vários países, jovens adultos do sexo masculino, têm mais probabilidade de acompanharem
eSports que desportos tradicionais. Este fator, aliado à digitalização das casas de apostas,
tornam o mercado de apostas em eSports muito apelativo para a exploração técnicas de
aprendizagem automática, uma vez que os jovens que acompanham este tipo de desportos têm
facilidade em apostar online.
Nesta dissertação é proposto, implementado, testado e validado um sistema de recomendação
de apostas que considera o histórico de resultados de cada equipa, as cotas de várias casas de
apostas e o sentimento geral dos fãs num fórum de discussão – HLTV. Deste modo, foram
inicialmente desenvolvidos 3 sistemas de aprendizagem automática.
Para avaliar os sistemas criados, foi considerado o período de outubro de 2020 até março de
2023, o que corresponde a 2500 partidas. Porém, sendo o período de testes tão extenso, existe
muita variação na competitividade das equipas. Deste modo, para evitar que os modelos
ficassem obsoletos durante este período de teste, estes foram re-treinados no mínimo uma vez
por mês durante a duração do período de testes.
O primeiro sistema de aprendizagem automática incide sobre a previsão a partir de resultados
anteriores, ou seja, o histórico de jogos entre as equipas. A melhor solução foi incorporar os
jogadores na previsão, juntamente com o ranking da equipa e dando mais peso aos jogos mais
recentes. Esta abordagem, utilizando regressão logística teve uma taxa de acerto de 66.66%
com um erro expectável de calibração de 2.10%.
O segundo sistema compila as cotas das várias casas de apostas e faz previsões com base em
padrões das suas variações. Neste caso, incorporar as casas de aposta tendo atingido uma taxa
de acerto de 65.88% utilizando regressão logística, porém, era um modelo pior calibrado que o
modelo que utilizava a média das cotas utilizando gradient boosting machine, que exibiu uma
taxa de acerto de 65.06%, mas melhores métricas de calibração, com um erro expectável de
2.53%.
O terceiro sistema, baseia-se no sentimento dos fãs no fórum HLTV. Primeiramente, é utilizado
o GPT 3.5 para extrair o sentimento de cada comentário, com uma taxa geral de acerto de
84.28%. No entanto, considerando apenas os comentários classificados como conclusivos, a taxa de acerto é de 91.46%. Depois de classificados, os comentários são depois passados a um
modelo support vector machine que incorpora o comentador e a sua taxa de acerto nas partidas
anteriores. Esta solução apenas previu corretamente 59.26% dos casos com um erro esperado
de calibração de 3.22%.
De modo a agregar as previsões destes 3 modelos, foram testadas duas abordagens.
Primeiramente, foi testado treinar um novo modelo a partir das previsões dos restantes
(stacking), obtendo uma taxa de acerto de 67.62%, mas com um erro de calibração esperado
de 5.19%. Na segunda abordagem, por outro lado, são agregados os dados utilizados no treino
dos 3 modelos individuais, e é treinado um novo modelo com base nesse conjunto de dados
mais complexo. Esta abordagem, recorrendo a support vector machine, obteve uma taxa de
acerto mais baixa, 66.81% mas um erro esperado de calibração mais baixo, 2.67%.
Por fim, as abordagens são postas à prova através de um simulador de apostas, onde sistema
cada faz uma previsão e a compara com a cota oferecia pelas casas de apostas. A simulação é
feita para vários patamares de retorno mínimo esperado, onde os sistemas apenas apostam
caso a taxa esperada de retorno da cota seja superior à do patamar.
Esta cota final é depois comparada com as cotas das casas de apostas e, caso exista uma casa
com uma cota superior, uma aposta é feita. Esta estratégia denomina-se de apostas de valor
esperado positivo, ou seja, apostas cuja cota é demasiado elevada face à probabilidade de se
concretizar e que geram lucros a longo termo. Nesta simulação, os melhores resultados, para
uma taxa de mínima de 5% foram os modelos criados a partir das cotas das casas de apostas,
com lucros entre os 13% e os 211%; o dos dados históricos que lucrou entre 8% e 90%; e por
fim, o modelo composto, com lucros entre os 11% e os 77%.
Assim, deste trabalho resultaram diversos sistemas baseados em machine learning capazes de
obter lucro a longo-termo a apostar em Counter Strike: Global Offensive
Esport from a sociological perspective. Reflections on the social dimension of electronic competitive gaming
Competitive computer and video gaming, commonly known as esport, has evolved from a subculture to a mainstream phenomenon in the last three decades. Due to various parallels with traditional sports in terms of professionalization, sportsmanship, marketing, or media coverage, esport is often referred to as a sport. At the same time, esport is characterized by a new form of movement culture in sports, where virtual and real worlds overlap. This dissertation examines the social dimensions of esport and competitive gaming from a sports sociological perspective to investigate the impact esport has on society. It sheds light on the academic discourse surrounding esport and explores theoretical and practical implications for sport and society. The digitalization and technological advancements have significantly influenced the development of esport, leading to its discussion as part of popular media and sports culture. Despite some counterarguments regarding the legitimacy of esport as a sport, it has evolved into a thriving ecosystem and a multi-million-dollar industry with many links to the traditional sport system. However, a key difference between esport and traditional sports is that esport takes place in both the digital and real world, while traditional sports are exclusively practiced in physical spaces. Players immerse themselves in the virtual world of gaming and are physically and mentally connected to it. This poses unique demands on players compared to other sporting activities. In esport, players engage in real competitions, are aware of their physical and mental performance, and utilize the interplay between the digital and real worlds to surpass their opponents. In this context, the role of the body in esport is an emerging research topic and differs from the extensive exploration of the body in traditional sports. Due to the disruptive nature of esport in the realm of traditional sports, this dissertation focuses on one of the fundamental questions of sports sociology: the impact of sport, in this case esport, on society. To do so, different social dimensions of esport are investigated by answering the following research questions:
• What societal impact does esport have?
• What role does the body play in esport and competitive gaming?
• What effect does the shifting focus from physical to digital corporeality have on players' behavior and the ecosystem?
After initially providing an overview of relevant definitions, the origins, and the current state of research on esport, the thesis then explains the theoretical background concerning the role of digitalization in sports, the relevance of immersion, and the interface between the virtual and real worlds in esport and competitive gaming. Subsequently, two scoping reviews and a conceptual paper address the research questions, which are discussed and summarized in the final part, thus providing the basis for new research on the societal impact as well as other social dimensions of esport
Producing Affection : Affect and Mediated Intimacy in Pokémon
Pokémon is a global multimedia franchise formed around a core series of videogames and a variety of characters to collect, learn about, and play with. Throughout its decades of development, Pokémon has grown into a media mix comprising of digital and analog games, animations, comics, toys, and a plethora of branded merchandize, all centering on the Pokémon characters and the audience’s relationship with them.
In this thesis, I explore how affection is formed and distributed in Pokémon. I view the relationship with Pokémon characters as a form of mediated intimacy, theorizing it as feelings of affection and closeness expressed through and aimed at technology. Through this, I discuss how technological and fantastical bodies wield agency and actively participate in the formation of everyday affects. By drawing primarily on game studies and affect studies, I develop an interdisciplinary method for playing and reading media texts for their affects and use it to analyze the media mix of Pokémon and the affective relations therein.
I focus primarily on the Pokémon videogames that serve as the core product of the entire media mix. I examine what it means to construct an entire media mix based on videogames and play and suggest this as a key interpretive arrangement for understanding the mediated intimacy of Pokémon.
This study presents the mediated intimacy of Pokémon as the result of the ludic and technological foundations of the Pokémon media mix, at the heart of which is the role-playing form of the original videogames and the way they have positioned audiences as participants and characters in the world of Pokémon. In this playful environment that overlaps fiction and everyday reality, the media mix guides its players to conduct a form of affective labor to access and traverse the textual whole of Pokémon and furthermore aligns this effort with the diegetic theme of caretaking as captured on the transmedia bodies of Pokémon.
Additionally, this work contributes to the theorization and rethinking of intimacies by exploring affection in human and non-human networks as an entanglement of biological and technological actors.Tuotettua kiintymystä. Pokémonin affekti ja medioitu intiimiys
Pokémon on globaali monimediakokonaisuus. Sen keskiössä on joukko videopelejä sekä niiden hahmoja, joita kerätään, joista opitaan ja joiden kanssa leikitään. Pokémonista on vuosikymmenten mittaan kasvanut mediatuotteiden rypäs, media mix: monista tuote- ja julkaisukanavista koostuva kokonaisuus, joka sisältää digitaalisia ja analogisia pelejä, animaatioita, sarjakuvia, leluja ja brändituotteita, joissa kaikissa korostuvat Pokémon-hahmot sekä yleisön suhde niihin.
Väitöskirjassani tarkastelen, miten kiintymystä rakennetaan ja levitetään Pokémonissa. Tutkin Pokémon-hahmoihin muodostettuja suhteita medioidun intiimiyden käsitteen kautta. Tutkimuksessani suhteet näyttäytyvät kiintymyksellisten tunteiden tiivistyminä sekä läheisyytenä, jota ilmaistaan teknologian avulla ja sitä kohtaan. Näin tarkastelen, miten teknologisten sekä fantastisten kehojen toimijuus näkyy arkipäiväisten affektien muodostumisessa. Ammentamalla pelitutkimuksesta ja affektitutkimuksesta kehitän monitieteisen metodin mediatekstien pelaamiseen ja lukemiseen, ja käytän sitä Pokémonin media mixin, sen affektien ja sen piirissä muodostettujen kiintymyssuhteiden analysointiin.
Keskityn erityisesti Pokémon-videopeleihin, jotka toimivat koko media mixin ydintuotteena. Tutkin, miten Pokémonin media mix on rakennettu ensisijaisesti pelilliselle ja leikilliselle pohjalle, ja ehdotan tätä tulkintamallia keskeiseksi Pokémonin medioidun intiimiyden ymmärtämiselle.
Tutkimuksen tuloksena esitän Pokémonin medioidun intiimiyden muodostuvan Pokémonin media mixin leikillisistä ja teknologisista juurista, joiden perustana on alkuperäisten Pokémon-videopelien roolipelillinen rakenne sekä se, miten sen avulla pelaajat on asemoitu hahmoiksi Pokémonin maailmaan. Fiktiota ja todellisuutta sekoittavassa leikillisessä ympäristössä Pokémonin media mix ohjaa pelaajia hoivan ja huolenpidon teemojen kautta tekemään tunnetyötä tuoteperheen mediatekstien parissa ja piirtää tämän työn tulokset Pokémon-hahmojen monimediakehoille.
Lisäksi väitöstutkimukseni osallistuu intiimiyden laajempaan teoretisointiin ja uudelleenmäärittelyyn tarkastelemalla elollisten ja leikillisesti elävien toimijoiden suhteita biologisena ja teknologisena yhteenliittymän
Cyber-Human Systems, Space Technologies, and Threats
CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts
Large Language Models (LLMs) have exhibited remarkable performance on various
Natural Language Processing (NLP) tasks. However, there is a current hot debate
regarding their reasoning capacity. In this paper, we examine the performance
of GPT-3.5, GPT-4, and BARD models, by performing a thorough technical
evaluation on different reasoning tasks across eleven distinct datasets. Our
paper provides empirical evidence showcasing the superior performance of
ChatGPT-4 in comparison to both ChatGPT-3.5 and BARD in zero-shot setting
throughout almost all evaluated tasks. While the superiority of GPT-4 compared
to GPT-3.5 might be explained by its larger size and NLP efficiency, this was
not evident for BARD. We also demonstrate that the three models show limited
proficiency in Inductive, Mathematical, and Multi-hop Reasoning Tasks. To
bolster our findings, we present a detailed and comprehensive analysis of the
results from these three models. Furthermore, we propose a set of engineered
prompts that enhances the zero-shot setting performance of all three models.Comment: Accepted for publication at Elsevier's Natural Language Processing
Journa
Future Perspectives on Positive Psychology:A Research Agenda
Just over two decades ago, Martin Seligman's inaugural lecture as the new president of the APA marked the dawn of Positive Psychology. Seligman called for a science of positive subjective experiences, positive individual states/traits/behaviours, and positive societal factors that improves the quality of life and wellbeing. Since then, this sub-discipline of psychology has shown extraordinary and inspiring growth in both the academy (e.g. research papers/books) and practice (e.g. establishment of professional associations, annual conferences). Positive psychology has increased our collective understanding of the factors that make life worth living, the drivers that enhance wellbeing and the elements that undermine them. It has given birth to many new theories, research models and methodologies that aim to measure, interpret, model and optimize the conditions that lead to flourishing individuals and thriving societies. It has also spawned a magnitude of sub-disciplines ranging from positive ageing, positive coaching, wellbeing therapies, positive relationships, positive health, positive organizational psychology etc. Despite building out its own identity, positive psychology has also been adopted in many adjacent fields like organizational studies, education, health, risk management, and even architectural sciences.In its relatively short life, positive psychology has provided new insights into the human condition and innovative means to solve complex individual, organizational and societal problems. Positive psychology has brought balance to psychology by establishing a platform to focus on more than just "fixing what is wrong" through focusing on optimizing what already works well. As a collective, we believe that positive psychology can continue to play a vital role in the future by deepening our understanding of 'positivity' and developing practical tools, methodologies, and interventions to enhance people, organizations, and societies' functioning.But what does the future of positive psychology hold? What are the strengths, opportunities, aspirations and results of positive psychology? And how can we, as a collective, build out the credibility and impact of the discipline's future? For us, these are some of the most challenging goals of positive psychology. With the rapid development of the field, detailed research and practice 'roadmaps' are required to direct the discipline's collective energies.This book address such by collating a series of research agendas about the future of positive psychology in different speciality areas. Specifically, the aim was to identify the limitations in our current understanding of the different theories, models, methods and interventions on which positive psychology is built and propose a roadmap for addressing such in the future. This aided in setting a specific, measurable, attainable, realistic and time-bound research agenda to direct the future development of positive psychology. Contributions discuss the current state of theory and research in positive psychology and presents a research agenda for future research
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