data correlations
Recently Published Documents

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.

AbstractMost accounts of responsibility focus on one type of responsibility, moral responsibility, or address one particular aspect of moral responsibility such as agency. This article outlines a broader framework to think about responsibility that includes causal responsibility, relational responsibility, and what I call “narrative responsibility” as a form of “hermeneutic responsibility”, connects these notions of responsibility with different kinds of knowledge, disciplines, and perspectives on human being, and shows how this framework is helpful for mapping and analysing how artificial intelligence (AI) challenges human responsibility and sense-making in various ways. Mobilizing recent hermeneutic approaches to technology, the article argues that next to, and interwoven with, other types of responsibility such as moral responsibility, we also have narrative and hermeneutic responsibility—in general and for technology. For example, it is our task as humans to make sense of, with and, if necessary, against AI. While from a posthumanist point of view, technologies also contribute to sense-making, humans are the experiencers and bearers of responsibility and always remain in charge when it comes to this hermeneutic responsibility. Facing and working with a world of data, correlations, and probabilities, we are nevertheless condemned to make sense. Moreover, this also has a normative, sometimes even political aspect: acknowledging and embracing our hermeneutic responsibility is important if we want to avoid that our stories are written elsewhere—through technology.

The main task of vocational education institutions is to implement the relevant knowledge and skills, alongside the acquisition of primary or secondary education. In the context of sports schools, the main emphasis is on achieving results in tournaments, games, and competitions. However, in parallel with the implementation of the sports field, the task of sports schools is to promote the opportunities and desires of students in the field of education. The author points out that too much emphasis is placed on the realization of sports goals, without paying attention to the accents of students’ personal development – for example, the importance of education. The aim of the research is related to the actualization of education in the context of sports schools. Two main guidelines are emphasized – the interest of sports schools in educating students, as well as the athletes’ own opinion about the education and its connection with sport. Within the framework of the research, the author wants to analyze the obtained data correlations. For example, the relationship between students’ current progress and their desire to continue their studies at university. Analyze the results and make recommendations to improve the situation. Data were collected from three professional sports schools in Latvia – Vidzeme and Riga region. The total number of respondents is 147. The survey was conducted anonymously, with respondents aged 13–16 years. Data processed using Windows SPSS and Microsoft Exel programs.

The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algorithms was performed in the discrete domain of the solution space, where we used the well-known knapsack problem in a variety of data correlations. The results obtained show that the proposed CMA-PBIL algorithm can perform better than standard PBIL in some cases. Therefore, the proposed algorithm can be a reasonable alternative to the PBIL algorithm in the discrete space domain.

Abstract The final result in a 400 m hurdles race (400mH) is relative to the motor preparation, technique of clearing hurdles as well as the adopted strategy of the race, including temporal aspects (split times in particular parts of the race) and spatial elements (the number of strides taken between subsequent hurdles). The objective of the study was to identify an optimal strategy for the 400mH race, including the stride pattern and split times. Data employed for this study were derived from results of 273 races held during the men’s finals of international events (Olympic Games, World and European Championships) held from 1968 to 2015. To determine the strategies in the race, three main hurdle sections were identified – 1-4H, 4-7H and 7-10H. In each part, the fast (best results), average and slow (worst results) performing groups of hurdlers were distinguished. The analysis of adopted strategies was carried out taking into account 26 variables (main, basic, temporal and spatial). Basic statistical data, correlations and analysis of variance (ANOVA) were used. Results highlight the use of a variety of strategies, of which selection depends, among others, on body composition and the level of motor abilities (speed, speed endurance and explosive strength), as well as hurdling technique. Especially, the endurance strategy appears to be the most effective one, as it is a characteristic of best performances of many hurdlers. The analysis demonstrates that at the highest sports level the strategy of 400 m hurdles should be analyzed individually.