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In some places far from Peru with the absence of the Notary Public, but with the presence of the Justice of the Peace, there is legal insecurity in the registry because it is not possible to carry out legal transactions on the real estate. This is what happens in the border province of Purús. Law 29824 reduced the Notarial powers of Justices of the Peace to only grant deed of transfer of possession of real estate that does not exceed 50 Procedural Reference Units (PRU), on the other hand, Law 27755 that creates the Land Registry, in its article 7, stipulates the use of registration forms to register real estate, as long as it does not exceed the economic value of 20 Tax Units (TU). This means that in some remote parts of the province, some citizen should enter into a legal transaction on their property, however, they cannot do so because they do not have a Notary Public, even if they do have a Justice of the Peace Lawyer, who could replace him (her), but who by law is disqualified from doing this job. That is why this article aims to design a recommendation system that allows the citizen to make the correct decision about what to do in each case, in addition to the fact that the system can serve to instruct the Justice of the Peace Lawyer of these communities to teach the citizen on the procedures to follow. This recommendation system is based on deontic logic, which is a variant of modal logic dedicated to reasoning about concepts related to conduct, especially has application in the legal field. The recommender system is a computer program that can be based on a model that serves to support individuals in recommending what action to take based on their needs or desires. [ABSTRACT FROM AUTHOR]

Distance Education enabled educational practices based on digital platforms. Despite its wide adoption, the high dropout rates are a reason for concern for teachers and institutional managers. There are initiatives to mitigate this situation, such as Educational Data Mining (EDM), Learning Analytics (LA), and the use of Recommendation Systems (RS). Although effective in specific aspects, these techniques lack mechanisms for the motivation of students and pedagogical intervention by teachers, as they do not present methodological proposals to encourage learning. Therefore, this article describes an RS model that presents as a differential integration of the pedagogical approach of Active Methodologies with the support of Educational Data Mining and Learning Analytics techniques to identify students with dropout risks and enhance permanence. For this, a prototype was implemented and a case study was carried out with professors from two universities to assess functionality and acceptance. According to the TAM model, more than 87% of teachers agree with the ease of use and 77% agree that RS can be useful in the teaching and learning process of students. Therefore, it can be concluded that the model contributes to teaching practices, encourages collaborative learning, and favors the monitoring of this process and the activities developed by the students. [ABSTRACT FROM AUTHOR]

Social media have established a new way of communicating and understanding social relationships. At the same time, there are downsides, especially, their use of algorithms that have been built and developed under their umbrella and their potential to alter public opinion. This paper tries to analyse the YouTube recommendation system from the perspectives of reverse engineering and semantic mining. The first result is that, contrary to expectations, the issues do not tend to be extreme from the point of view of polarisation in all cases. Next, and through the study of the selected themes, the results do not offer a clear answer to the proposed hypotheses, since, as has been shown in similar works, the factors that shape the recommendation system are very diverse. In fact, results show that polarising content does not behave in the same way for all the topics analysed, which may indicate the existence of moderators –or corporate actions– that alter the relationship between the variables. Another contribution is the confirmation that we are dealing with non-linear, but potentially systematic, processes. Nevertheless, the present work opens the door to further academic research on the topic to clarify the unknowns about the role of these algorithms in our societies. [ABSTRACT FROM AUTHOR]

This article presents the characterization of variables related to the precise fertilization of soils and dairy cattle pastures, for the construction of an intelligent system for the recommendation of fertilization plans. The characterization was carried out through a field study that considered soil analysis and determination of optimum levels of macronutrients in five farms in the north of Antioquia-Colombia. The main result was the establishment of the input and output fuzzy sets, together with the production rules, which were later taken to a functional prototype. From the above, it is concluded that the use of artificial intelligence techniques has great potential for integration with software to support fertilization-related tasks. [ABSTRACT FROM AUTHOR]

In this article we propose a model of recommendation of experimental protocols based on the user's context of use. As a case study, 107 experiments related to 5 experimental protocols of the soil mechanics laboratory tests of the Civil Engineering in the ASTM (American Society for Testing and Materials) standards are analyzed. Starting from the formal specification of the experimental protocol, a representation model is proposed. This model allows to describe the entities that converge in this scientific product, contributing to the formalization of the experimental protocol in a domain of knowledge that has been little explored from this perspective. With the previous result, a recommendation model is presented. This model takes advantage of the concept of the context of use to highlight within the experimental protocol, the entities that characterize the context in which a user performs a new experiment, reproduces, repeats or audits previously performed experiments. This also contributes to considering the recommendation of this type of products that is not yet visible in the repositories of experimental protocols explored in this research. With two previous models, a software prototype is built and using test cases, the representation model and the recommendation model are validated with satisfactory results in a content-based strategy to achieve a recommendation to the user. [ABSTRACT FROM AUTHOR]

Technology has opened the possibility of improving learning environments. However, in the field of medical education, the tools that are used do not provide clear evidence as to whether students are actually learning. Specifically, in the teaching of anatomy, multiple applications have emerged to satisfy the need to access cadaveric material, nevertheless, these lack enriched information on the performance achieved by students and how to adapt learning according to their educational needs. Thus, one of the strategies currently present in this area is gamification. This study implements and uses a gamified educational software platform based on recommender systems and virtual assistants, capable of delivering feedback and strategies to support the appropriation of anatomy knowledge of medical students at the Universidad de La Frontera (UFRO), in the city of Temuco, Chile. Forty-five students participated in the study. The study consisted in the use of various gamified components with artificial intelligence techniques. The main findings of this experience led to the conclusion that the use of gamified components for learning anatomy is a resource that supports student learning. [ABSTRACT FROM AUTHOR]

To rapidly improve the quality of web applications, it is necessary to implement automated testing strategies that allow you to comprehensively evaluate functionality and performance attributes, which are directly affected by the behavior of the hardware of the servers where they are hosted. Just as IT teams monitor the infrastructure that supports the applications, automations should be supported by monitoring platforms with specialized software testing analytics. This article presents the evaluation of four technology stacks, which will make it possible to select the most appropriate one to be included in the development of a platform specialized in software quality analytics. The four stacks in this study are: PALG, TICK, ELK, AM. This article will explain the characteristics and differences of stacks compared to various components such as: database, information filtering, collection of operating system metrics, alert system, data ingestion and visualization. Then simulations and tests carried out on each of them are described to evaluate their capabilities. Finally, conclusions are issued that will allow to select the technology stack that will be used in the development of the system specialized analytics for software testing. [ABSTRACT FROM AUTHOR]

This article offers methodological and theoretical reflections based on the interdisciplinary project "Algorithmic Identities." Initiated in 2019 between researchers from the Pontificia Universidad Católica de Chile and the University of Warwick (UK), this project seeks to understand how personhood is configured in times of algorithms and digital data. We designed a prototype app, "Big Sister," which emulates algorithmic profiling and recommendation systems of major digital platforms. This prototype has served as a twofold social research device. Firstly, it allowed us to produce knowledge from a critical and decolonial doing and undoing; secondly, through digital trace interviews with prototype users, we explored their experiences and stimulate reflection on the mediations of algorithms in their lives. This article examines the prototyping process of this app and some findings of how people in Chile understand, inhabit, and shape data and algorithms daily. Drawing on insights from Latin American authors such as Arturo Escobar, Jesús Martín-Barbero, and Paulo Freire, we argue how the prototyping of an app can move towards a "problematizing" approach to datafication and algorithmic systems in Latin America. [ABSTRACT FROM AUTHOR]

The objective of this work was to propose a theoretical model of pervasive information architecture, focused on the use of recommendation systems to increase the findability of information in university libraries. This research was developed from a multidisciplinary perspective and it is defined as exploratory research since a bibliographic review is carried out. This review allows obtaining information on the theoretical elements necessary to establish the central base of the research. It is also descriptive, as it seeks to bring solutions to issues that arise in the daily life of university libraries. As a result, a model of pervasive information architecture for university libraries is presented with a focus on improving information findability from the inclusion of recommendation systems. It can be concluded that, due to the wide advance in the development of applications and tools for use in libraries, a notable increase in the level of complexity of the information environments of these institutions is perceived, which from the perspective of information architecture creates the need for more refined research projects, whose aim is to improve the information environments of university libraries, always with the main objective of favoring the experiences of users, and helping them in the processes of finding relevant information, at the precise time and with the highest possible quality. [ABSTRACT FROM AUTHOR]

This article aims to identify the way in which the Netflix platform demonstrates the concept of television flow in its catalog, based on the analysis of the original content of fiction series released in 2019. It also recognizes some programming strategies and techniques that implements this video streaming platform, considering the implications of big data and recommendation systems. The methodology contemplates qualitative and quantitative techniques and applies an analysis matrix based on a manual coding of 157 original fiction series, with variables such as launch date, origin of production, season number, chapters and total minutes released per season. Among the main findings, it is evident that Netflix achieves a television flow with permanent premieres throughout the year, the launch of content by seasons with full chapters, as well as the election of Friday and the spring season for the launch of a greater number of fiction productions. It was also found that, although this streaming service seeks a diversification of the catalog with productions from different countries, the original serial fiction content continues to be mainly of American origin. It is concluded that the programming dynamics in the Netflix catalog creates a television flow to promote intensive consumption in the premieres of original fiction series. The catalog is optimized through data-based decisions and promotes viewing with content recommendation systems that allow the creation of an entire Netflix experience. [ABSTRACT FROM AUTHOR]

This article shows the construction of an information model for the exchange of data and information within the context of the development of CubeSat type satellite systems; articulating the development, procedure, description, design, architecture and results that largely refer to the adoption of such guidelines within a scenario of technological interoperability and specifying the functionality in the transmission patterns of telemetry information in academic satellites. The guidelines of the Consultative Committee on Spatial Data Systems (CCSDS) are taken into account in collaboration with the European Space Agency (ESA) and the European Cooperation for Spatial Standardization (ECSS), who have consolidated multiple recommendations to standardize the transfer of information from space systems through various procedures and mechanisms. Additionally, the Extensive Markup Language (XML) was considered, next to the Telemetry and Command Exchange (XTCE) format, in conjunction with the Package Utilization Standard (PUS), the description of Mission Operation Services (MO) and the Message Abstraction (MAL) layer, taking into account that they suggest the modularity and specificity necessary for the development and construction of information systems in satellite technologies. [ABSTRACT FROM AUTHOR]

This work presents a tool called HeuristicTool, by means of which the heuristic evaluation process is supported, allowing evaluators to improve the process of analyzing the results and, in general, the development of said evaluations. During its development and permanent updating, ontologies were included to facilitate the capitalization and transfer of knowledge between the different evaluators. The above through KACTUS, a methodology that proposes steps in relation to: the specification of the application, preliminary design based on ontological categories and a refinement and structuring of the ontology. A group of three experts rated 6 ontology variables, consolidating an average of 4.7 out of 5. In addition, an evaluation of the platform's usability presented satisfactory results, using Jakob Nielsen's battery of heuristics. The main result was an application that gives users the possibility of reviewing structured evaluations in a knowledge base, in order to improve decision making. [ABSTRACT FROM AUTHOR]

New technologies and the era of knowledge have caused the school to cease to hold a monopoly on education and to emerge new models and learning formats. Furthermore, these circumstances have led to a culture of infoxication, disinformation and post-truth. In the network there is no information filter. This context has also favored the creation of new digital textualities based on collective creation. Among them stand out the creepypasta (digital horror stories) as works based on constructive hyperfiction, inspired by urban legends and related to fake news. This research aims to analyze creepypasta as constructions and narrative models in order to take advantage of their potential in formal education. To do this, a corpus of 68 works located in the Suggested Reading section of the affinity space related to these most important texts on the web has been extracted and analyzed: creepypasta. fandom. The results found show that these are metafictional stories that use a great wealth of techniques and narrative resources. Images, documents, news and other document facts are manipulated and resignified. All this positions these texts in a privileged space for the promotion of reading and media and information literacy. [ABSTRACT FROM AUTHOR]

The evolution of technology and the digital empowerment of society have led to the proliferation of Audiovisual Content Webs (ACWs) where users can share information and experiences, along with other commercial resources. ACWs have led to significant changes in the way users can select and access audiovisual content. The design of these websites combines various features, including a user community, streamed content, ticket sales, and recommendations, among others. Each ACW has a specific profile with respect to the mentioned variables. The aim of this study is to identify the critical success factors for a ACW, i.e., which features and the form they should take to attract followers, thus increasing the capacity to prescribe and broadcast content. Using qualitative comparative analysis (QCA), a formal analysis technique that allows the identification of combinations that produce a certain result, a sample of the 12 most representative cinema websites in Spain is analyzed. The results indicate that the incorporation of content recommendation systems and the connection with streaming platforms through which the content can be accessed are key factors in the success of these ACWs. This work contributes academically to the knowledge and explanation of audience behavior in the new audiovisual scenario. From a professional point of view, relevant design suggestions are offered to platform creators. Finally, the limitations of this work are described, and future lines of research are considered. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a method of hybrid context-aware recommender systems, based on ontologies in the environment of the Spatial Data Infrastructure, which will positively impact the effectiveness of decision-making in the tourist applications for mobile users. The method developed has three components: data and semantic framework, other that implements the recommender system and another responsible for displaying the recommendations. The recommender system component implements a spatial, a semantic and collaborative filters. The recommendations generated show the elements of potential interest, these can be displayed in tabular or combined with spatial information. It was found through an experiment and different applied metrics the increase in its effectiveness for mobile users according to their preferences as to the recommendations given in a spatial environment. [ABSTRACT FROM AUTHOR]

This paper presents an architecture for semantic context-aware recommender systems intended to be implemented on organizational settings. We described the use of this architecture in the development of the Find-It! project to validate its utility. The Find-It! project consider contextual factors to recommend people, knowledge objects, places, activities, events, technological resources and services to the visitors of an organization. The architecture defines an inference module to filter the items subject of recommendation by using different contextual dimensions that are modeled in a single ontologies, the novelty of an item, users' perception of recommendations and contextual conditions considered when they were recommended in previous visits. We also defined a mobile client interface capable to adapt to the user characteristics and device. This client interface counts with implicit and explicit mechanisms to identify the user satisfaction degree with the recommendations received and its quality. Experience obtained with the development of the Find-It! project shows a satisfactory performance. In this way, it is adequately considered to use the architecture to develop a wide range of organizational recommender systems. [ABSTRACT FROM AUTHOR]

Advertisement in Digital Signage environments demands the enrichment of the classic approach of recommendation focused on individuals by means of the delivery of ads for a group of people observing a public screen. Even though the accuracy that can be achieved in the recommendations is important, the degree of novelty perceived by the users is even more important. This balance can be obtained not only through the improvement of the recommendation algorithms, but also through an adequate multiscreen cooperation scheme. Therefore, this paper proposes an approach for the construction of a recommendations system for Digital Signage environments based on a Smart TV-Smartphone cooperation scheme. [ABSTRACT FROM AUTHOR]

In the current techno-social environment the importance of providing personalized content for users of social platforms, continually adapting to their needs, is key to the success of the existing platforms. Recommender systems play a crucial role in this regard, but in many cases the level of customization provided is not enough. This article discusses two key aspects related to the development of recommender systems: proactivity and context-awareness. We propose a theoretical reference model for the creation of proactive recommender systems based on context-awareness information. In addition, we check its feasibility in two real-world scenarios where the model has been successfully implemented: banking and personal learning networks. Finally, future courses of action following the contributions proposed are highlighted, with special attention to potential applications in educational environments. [ABSTRACT FROM AUTHOR]

In a Web dominated by social media for information, relationships and communication, the established dynamics between content, people and technology change radically. Given the relevance of the user-generated content in such a scenario and its essentially relational nature, successfully locating the best sources of information requires the development of recommender systems that incorporate the social characteristic of scenarios built on a network that goes beyond the original Internet. The article provides a review of current approaches to the process of recommendation, placing them in the context of consolidated trends associated with the phenomenon of Social Computing, and highlights some lines of development in the area of redefining the problem of recommendation in a context dominated by social networks and user-generated content. [ABSTRACT FROM AUTHOR]

We present the theoretical and methodological foundations for the development of a multi-agent SDI service model for specialized digital libraries, applying semantic web technologies that permit more efficient information management, improving agent-user communication processes and facilitating accurate access to relevant resources. To do this, RSS feeds are used as "current awareness bulletins" to generate personalized bibliographic alerts. The SDI service model has an RSS feeds management module and an information push module. In the first module, resources are represented as RSS feed items and are also semi-automatically assigned subject terms by matching their associated keywords against the terms of a SKOS Core format thesaurus. In the information push module, bibliographic alerts are customized according to the preferences defined on users' profiles. [ABSTRACT FROM AUTHOR]

Information retrieval (IR) deals with representation, organization, and storage of information. IR should provide the user with an easy access to the information in which he or she is interested. However, characterization of the user information needs is a complex problem. Usually, a full description of the user information needs cannot be made directly and a set of keywords should rather be used. Thus, given a set of keywords, an IR system retrieves information, which may be relevant to the user. However, traditional IR systems overload recovery information. Personalization systems, in particular, the recommender ones, have been proposed in order to mitigate the impact of an enormous amount of retrieved information on the user. This paper describes the architecture of PREDICA, an experimental platform for combining advanced query and recommender systems, adaptive interface, data warehouse and knowledge discovery on database in a digital library. PREDICA uses a pre-processing strategy for improving the search of information and personalization strategies for documents' recommendation. Among other open source digital library research projects, like D-Space, Eprints, and Greenstone, PREDICA has an integrated recommender system to provide helpful information to the users. [ABSTRACT FROM AUTHOR]

This article examines the constitutional, statutory, and regulatory regime in Mexico regarding property rights and the protection of biodiversity. It documents the weaknesses of that system and, based on that analysis, makes recommendations for law and regulatory reform. [ABSTRACT FROM AUTHOR]