Progressive Content Generation Based on Cyclic Graph for Generate Dungeon

Dungeon is level in game consisting collection of rooms and doors with obstacles inside. To make good level, takes a lot of time. With Procedural Content Generation (PCG), dungeons can be created automatically. One of the approaches in PCG to create levels is progressive. Progressive approach produces timeline as representation of the interactions in the game. Timeline representation that is in the form of one straight line is good for endless runner, but for dungeon, the levels are linear. In this research, the timeline is changed to cyclic graph. Cyclic graph is formed using graph grammar algorithm. This research aims to build dungeon that has not linear and minimal dead ends. To eliminate linearity in dungeons, branching in dungeons needs to be formed. The steps carried out in this research are designing graph grammar rules, generating population of graphs, evaluating graphs with fitness values, and building dungeons. Four functions are used to determine the fitness value: shortest vertices, average duration, replayability, and variation. Dungeons produced with progressive approach manage to minimize linearity in dungeons. Dungeon formation is very dependent on the rule grammar that forms it. With the evaluation process, linear dungeons resulting from grammar rules can be minimized

Sentiment Analysis System for Mapping Hate Speech Against Women in Social Media using GIS System

This study aims to map hate speech against women in the Middle East using a Geographic Information System GIS and sentiment analysis with the goal of identifying patterns The hate speech terms that were utilized in the research were gathered from more than 3600 women in the study region according to the dat

Expressions of psychological stress on Twitter: detection and characterisation

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Long-term psychological stress is a significant predictive factor for individual mental health and short-term stress is a useful indicator of an immediate problem. Traditional psychology studies have relied on surveys to understand reasons for stress in general and in specific contexts. The popularity and ubiquity of social media make it a potential data source for identifying and characterising aspects of stress. Previous studies of stress in social media have focused on users responding to stressful personal life events. Prior social media research has not explored expressions of stress in other important domains, however, including travel and politics. This thesis detects and analyses expressions of psychological stress in social media. So far, TensiStrength is the only existing lexicon for stress and relaxation scores in social media. Using a word-vector based word sense disambiguation method, the TensiStrength lexicon was modified to include the stress scores of the different senses of the same word. On a dataset of 1000 tweets containing ambiguous stress-related words, the accuracy of the modified TensiStrength increased by 4.3%. This thesis also finds and reports characteristics of a multiple-domain stress dataset of 12000 tweets, 3000 each for airlines, personal events, UK politics, and London traffic. A two-step method for identifying stressors in tweets was implemented. The first step used LDA topic modelling and k-means clustering to find a set of types of stressors (e.g., delay, accident). Second, three word-vector based methods - maximum-word similarity, context-vector similarity, and cluster-vector similarity - were used to detect the stressors in each tweet. The cluster vector similarity method was found to identify the stressors in tweets in all four domains better than machine learning classifiers, based on the performance metrics of accuracy, precision, recall, and f-measure. Swearing and sarcasm were also analysed in high-stress and no-stress datasets from the four domains using a Convolutional Neural Network and Multilayer Perceptron, respectively. The presence of swearing and sarcasm was higher in the high-stress tweets compared to no-stress tweets in all the domains. The stressors in each domain with higher percentages of swearing or sarcasm were identified. Furthermore, the distribution of the temporal classes (past, present, future, and atemporal) in high-stress tweets was found using an ensemble classifier. The distribution depended on the domain and the stressors. This study contributes a modified and improved lexicon for the identification of stress scores in social media texts. The two-step method to identify stressors follows a general framework that can be used for domains other than those which were studied. The presence of swearing, sarcasm, and the temporal classes of high-stress tweets belonging to different domains are found and compared to the findings from traditional psychology, for the first time. The algorithms and knowledge may be useful for travel, political, and personal life systems that need to identify stressful events in order to take appropriate action.European Union's Horizon 2020 research and innovation programme under grant agreement No 636160-2, the Optimum project (www.optimumproject.eu)

Sustenabilitatea educației doctorale în economie și afaceri

Volumul ”Sustenabilitatea educației doctorale în economie și afaceri” valorifică ideile și cercetările doctoranzilor de la Universitatea “Alexandru Ioan Cuza” din Iași, școala doctorală de economie și administrarea afacerilor. Lucrările au fost prezentate, prin postere sau în plen, în conferința finală a proiectului SESYR, finanțat prin programul european Jean Monnet. Structurarea volumului în patru subcapitole generice are ca scop valorificarea domeniilor considerate prin filosofia proiectului:managementul proiectelor, antreprenoriat si angajabilitate pentru tinerii cercetători. O colecție de 24 de articole având 35 de autori, oferă un mediu de dezbatere științifică provocatoare pentru publicul cititor din domeniul economic. Focalizarea subiectelor din articolele prezente pe motivațiile de cercetare ale doctoranzilor și postdoctoranzilor face ca acest volum să reprezinte un debut publicistic pentru unii autori iar pentru alții, o consolidare a vocației. Diseminarea pasiunilor în astfel de contexte consolidează colaborarea și deschiderea spre noi subiecte investigative. Volumul este destinat studenților, cercetătorilor și profesorilor și îl propunem ca reper bibliografic pentru dezvoltarea altor idei de cercetare și inovare în arealul nostru tematic