Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

The Effect of Market Entry on Innovation: Evidence from UK University Incubators

This paper investigates the effect of market entry of new firms on incumbent firms' innovative activity measured as patent applications. The basic assumption is that the effect of entry varies by geographical distance between entrants and incumbents due to the presence of localized unobserved spillovers. In order to avoid endogeneity problems commonly associated with the timing of entry and entrants' location choice, I analyze entry induced by the establishment of university business incubators, which are usefully exogenous in time and space. The results show that entry has a statistically and economically significantly positive strategic effect on incumbent patenting which is attenuated by the geographical distance between entrant and incumbent.Patents, market entry, incubators, spillover

The effect of prize structure and feedback policy on employee effort: a tournament theory approach

One of the main focus of management is on ways to motivate employees to improve their performance, initially at the level of individuals, and ultimately at the level of the organization (Denisi & Pritchard, 2006). So, it is critical to set effective performance appraisal systems to stimulate employees’ efforts. Since tournament theory arose out of the labor economics literature (Lazear & Rosen, 1981), it has expanded to a wide range of other disciplines including management. Most of the previous studies on prize structure using this theory have focused on a two-level prize while, in real life, practitioners always adopt a multiple level prize structure. In addition, previous studies on feedback in a dynamic tournament have unveiled agents’ reactions, however, there are few studies on feedback in multiple agents’ tournaments with multiple level prize structure. These two gaps between theory and practice have motivated the research in this thesis. Based on tournament theory, we study the effect of prize structure and feedback policy on employee efforts in a multi-person tournament. The experimental method is used to compare the efforts in four situations, including two-level prize structure with full feedback policy, multiple level prize structure with full feedback, two-level prize structure with no feedback, and multiple level prize structure with no feedback. After the experiment, six participants were invited to join a focus group interview for further insights on the experiment. As a supplement, a single case study of a factory in China is conducted and data collected through document analysis and a questionnaire distributed to employees. The results show that the subjects’ efforts in a multiple level prize structure is higher than that in a two-level prize structure in a multi-person tournament. Under both the policy of full feedback of own and relative performance information, and under no feedback policy, the effort in multiple level prize is also higher than that in two-level prize. These findings may contribute to develop the tournament theory in terms of prize structure in a multi-person tournament, and to bridge the gap between academia and industry since results could guide practitioners in the industry to apply a multiple level prize structure into employee performance management systems in order to maximize employee’s efforts and the overall output.A melhoria do desempenho dos trabalhadores é uma das principais preocupações da gestão quer a nível individual quer organizacional (Denisi & Pritchard, 2006), pelo que é necessário conceber sistemas de avaliação que promovam o esforço desenvolvido. A teoria dos torneios, proveniente da literatura da economia do trabalho (Lazear & Rosean, 1981), é precisamente um desses sistemas depois de se ter expandido para a gestão e para outras disciplinas. Contudo, muitos dos estudos sobre estruturas de prémios que utilizam esta teoria, têm-se concentrado em prémios com dois níveis enquanto na prática as organizações utilizam estruturas de múltiplos níveis. Além disso, embora existam trabalhos anteriores que têm revelado as reações dos agentes em torneios dinâmicos, são poucos os estudos sobre essas mesmas reações em estruturas de prémios de múltiplos níveis. Foi esta contradição entre a teoria e a prática que motivou esta tese. Com base na teoria dos torneios, a tese estuda o efeito da estrutura de prémios e da política de "feedback" seguida pela organização sobre os esforços dos trabalhadores num torneio com vários sujeitos. Utilizou-se o método experimental para se compararem os esforços em quatro situações: estrutura de prémios de dois níveis e política com e sem "feedback"; estrutura de prémios de níveis múltiplos com e sem "feedback". Finda a experiência, convidaram-se 6 participantes para um grupo de discussão a fim de se obterem mais esclarecimentos sobre a prova. Em complemento estudou-se o caso de uma empresa fabril na China através de análise documental e de um questionário distribuído aos empregados. Os resultados demonstram que, num torneio com múltiplos sujeitos, os esforços são superiores quando é utilizada uma estrutura de prémios de níveis múltiplos. O mesmo acontece em caso de política de "feedback" integral ou mesmo quando não existe "feedback". Estes resultados podem contribuir para ajudar a desenvolver a teoria dos torneios no que se refere à estrutura de prémios em torneios com múltiplos sujeitos e podem também aproximar a teoria da prática ajudando os gestores na implementação de sistemas que maximizem o desempenho dos trabalhadores

Strategic interaction in the Prisoner's Dilemma: A game-theoretic dimension of conflict research

This four-part enquiry treats selected theoretical and empirical developments in the Prisoner's Dilemma. The enquiry is oriented within the sphere of game-theoretic conflict research, and addresses methodological and philosophical problems embedded in the model under consideration. In Part One, relevant taxonomic criteria of the von Neumann- Morgenstern theory of games are reviewed, and controversies associated with both the utility function and game-theoretic rationality are introduced. In Part Two, salient contributions by Rapoport and others to the Prisoner's Dilemma are enlisted to illustrate the model's conceptual richness and problematic wealth. Conflicting principles of choice, divergent concepts of rational choice, and attempted resolutions of the dilemma are evaluated in the static mode. In Part Three, empirical interaction among strategies is examined in the iterated mode. A computer-simulated tournament of competing families of strategies is conducted, as both a complement to and continuation of Axelrod's previous tournaments. Combinatoric sub-tournaments are exhaustively analyzed, and an eliminatory ecological scenario is generated. In Part Four, the performance of the maximization family of strategies is subjected to deeper analysis, which reveals critical strengths and weaknesses latent in its decision-making process. On the whole, an inter-modal continuity obtains, which suggests that the maximization of expected utility, weighted toward probabilistic co-operation, is a relatively effective strategic embodiment of Rapoport's ethic of collective rationality

Sport Modalities, Performance and Health

Sport modalities are highly practiced in order to improve many aspects of human beings, including performance and health. The increasing interest in the quantitative and qualitative aspects of sport training is ascribable to the fact that several training systems and new methodologies are appearing in all sport modalities. These methodologies can have different effects on the organism depending on the degree of training.On the other hand, some of the main objectives in sport research are to describe match activity and to detect effective performance indicators. A better knowledge of players' performance adaptations and game dynamics during competition is extremely useful for optimizing the training process. The need to develop training methodologies according to actions occurring during the game is essential for each sport

Congress UPV Proceedings of the 21ST International Conference on Science and Technology Indicators

This is the book of proceedings of the 21st Science and Technology Indicators Conference that took place in València (Spain) from 14th to 16th of September 2016. The conference theme for this year, ‘Peripheries, frontiers and beyond’ aimed to study the development and use of Science, Technology and Innovation indicators in spaces that have not been the focus of current indicator development, for example, in the Global South, or the Social Sciences and Humanities. The exploration to the margins and beyond proposed by the theme has brought to the STI Conference an interesting array of new contributors from a variety of fields and geographies. This year’s conference had a record 382 registered participants from 40 different countries, including 23 European, 9 American, 4 Asia-Pacific, 4 Africa and Near East. About 26% of participants came from outside of Europe. There were also many participants (17%) from organisations outside academia including governments (8%), businesses (5%), foundations (2%) and international organisations (2%). This is particularly important in a field that is practice-oriented. The chapters of the proceedings attest to the breadth of issues discussed. Infrastructure, benchmarking and use of innovation indicators, societal impact and mission oriented-research, mobility and careers, social sciences and the humanities, participation and culture, gender, and altmetrics, among others. We hope that the diversity of this Conference has fostered productive dialogues and synergistic ideas and made a contribution, small as it may be, to the development and use of indicators that, being more inclusive, will foster a more inclusive and fair world

Where are you talking about? Advances and Challenges of Geographic Analysis of Text with Application to Disease Monitoring

The Natural Language Processing task we focus on in this thesis is Geoparsing. Geoparsing is the process of extraction and grounding of toponyms (place names). Consider this sentence: "The victims of the Spanish earthquake off the coast of Malaga were of American and Mexican origin." Four toponyms will be extracted (called Geotagging) and grounded to their geographic coordinates (called Toponym Resolution). However, our research goes further than any previous work by showing how to distinguish the literal place(s) of the event (Spain, Malaga) from other linguistic types/uses such as nationalities (Mexican, American), improving downstream task accuracy. We consolidate and extend the Standard Evaluation Framework, discuss key research problems, then present concrete solutions in order to advance each stage of geoparsing. For geotagging, as well as training a SOTA neural Location-NER tagger, we simplify Metonymy Resolution with a novel minimalist feature extraction combined with an LSTM-based classifier, matching SOTA results. For toponym resolution, we deploy the latest deep learning methods to achieve SOTA performance by augmenting neural models with hitherto unused geographic features called Map Vectors. With each research project, we provide high-quality datasets and system prototypes, further building resources in this field. We then show how these geoparsing advances coupled with our proposed Intra-Document Analysis can be used to associate news articles with locations in order to monitor the spread of public health threats. To this end, we evaluate our research contributions with production data from a real-time downstream application to improve geolocation of news events for disease monitoring. The data was made available to us by the Joint Research Centre (JRC), which operates one such system called MediSys that processes incoming news articles in order to monitor threats to public health and make these available to a variety of governmental, business and non-profit organisations. We also discuss steps towards an end-to-end, automated news monitoring system and make actionable recommendations for future work. In summary, the thesis aims are twofold: (1) Generate original geoparsing research aimed at advancing each stage of the pipeline by addressing pertinent challenges with concrete solutions and actionable proposals. (2) Demonstrate how this research can be applied to news event monitoring to increase the efficacy of existing biosurveillance systems, e.g. European Commission’s MediSys.I was generously funded by DREAM CDT, which was funded by NERC of UKRI

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Methods for improving entity linking and exploiting social media messages across crises

Entity Linking (EL) is the task of automatically identifying entity mentions in texts and resolving them to a corresponding entity in a reference knowledge base (KB). There is a large number of tools available for different types of documents and domains, however the literature in entity linking has shown the quality of a tool varies across different corpus and depends on specific characteristics of the corpus it is applied to. Moreover the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real world applications. In the first part of this thesis I explore an approximation of the difficulty to link entity mentions and frame it as a supervised classification task. Classifying difficult to disambiguate entity mentions can facilitate identifying critical cases as part of a semi-automated system, while detecting latent corpus characteristics that affect the entity linking performance. Moreover, despiteless the large number of entity linking tools that have been proposed throughout the past years, some tools work better on short mentions while others perform better when there is more contextual information. To this end, I proposed a solution by exploiting results from distinct entity linking tools on the same corpus by leveraging their individual strengths on a per-mention basis. The proposed solution demonstrated to be effective and outperformed the individual entity systems employed in a series of experiments. An important component in the majority of the entity linking tools is the probability that a mentions links to one entity in a reference knowledge base, and the computation of this probability is usually done over a static snapshot of a reference KB. However, an entity’s popularity is temporally sensitive and may change due to short term events. Moreover, these changes might be then reflected in a KB and EL tools can produce different results for a given mention at different times. I investigated the prior probability change over time and the overall disambiguation performance using different KB from different time periods. The second part of this thesis is mainly concerned with short texts. Social media has become an integral part of the modern society. Twitter, for instance, is one of the most popular social media platforms around the world that enables people to share their opinions and post short messages about any subject on a daily basis. At first I presented one approach to identifying informative messages during catastrophic events using deep learning techniques. By automatically detecting informative messages posted by users during major events, it can enable professionals involved in crisis management to better estimate damages with only relevant information posted on social media channels, as well as to act immediately. Moreover I have also performed an analysis study on Twitter messages posted during the Covid-19 pandemic. Initially I collected 4 million tweets posted in Portuguese since the begining of the pandemic and provided an analysis of the debate aroud the pandemic. I used topic modeling, sentiment analysis and hashtags recomendation techniques to provide isights around the online discussion of the Covid-19 pandemic