Universe Of Discourse Research Articles

Generalized Active Information: Extensions to Unbounded Domains

In the last three decades, several measures of complexity have been proposed. Up to this point, most of such measures have only been developed for finite spaces. In these scenarios the baseline distribution is uniform. This makes sense because, among other things, the uniform distribution is the measure of maximum entropy over the relevant space. Active information traditionally assumes a finite interval universe of discourse but can be extended to other cases where maximum entropy is defined. Illustrating this is the purpose of this paper. Disequilibrium from maximum entropy, measured as active information, can be evaluated from baselines with unbounded support.

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a concept. For Artificial Intelligence, only the defined notions can be considered existent. Thus, in relation to AI, an ontology can be understood as part of a program which delineates a collection of descriptions. An ontology, therefore, correlates the labels of the entities in the universe of discourse with wording that holds meaning for humans, explaining what those labels signify, along with the precise principles that force the interpretation and semantic utilization of these labels. An ontology constitutes a proper statement of a logical theory. It is a crucial component of a system with the capability to process, manage, analyze, correlate and reason from the large datasets characterized by heterogeneity. This paper depicts the process of development of a Common Representational Model (CRM) on top of several ontologies, taxonomies and classifications to facilitate computational and data mining functionalities. The building blocks of said CRM are delineated in detail, as well as its application in a specific use case.

A novel hybrid time series forecasting model based on neutrosophic-PSO approach

This article proposed a new time series forecasting model based on neutrosophic set (NS) theory and particle swarm optimization (PSO) algorithm. The proposed model initiated with the representation of time series dataset into NS using three different memberships of NS, i.e., truth-membership, indeterminacy-membership and falsity-membership. This NS representation of time series was referred to as neutrosophic time series (NTS). It was observed that the forecasting accuracy of the proposed model was highly relied on the optimal selection of the universe of discourse of time series dataset. In this study, this problem was resolved by using the PSO algorithm. The proposed model was verified and validated with three different datasets that included the university enrollments dataset of Alabama, TAIFEX index and TSEC weighted index. Experimental results showed that the proposed model outperformed existing benchmark models with average forecasting error rates of 0.80%, 0.015% and 0.90% for the university enrollments, TAIFEX and TSEC, respectively.

Report and repeat: Investigating Facebook’s hate speech removal process

Social media is rife with hate speech. Although Facebook prohibits this content on its site, little is known about how much of the hate speech reported by users is actually removed by the company. Given the enormous power Facebook has to shape the universe of discourse, this study sought to determine what proportion of reported hate speech is removed from the platform and whether patterns exist in Facebook’s decision-making process. To understand how the company is interpreting and applying its own Community Standards regarding hate speech, the authors identified and reported hundreds of comments, posts, and images featuring hate speech to the company (n=311) and recorded Facebook’s decision regarding whether or not to remove the reported content. A qualitative content analysis was then performed on the content that was and was not removed to identify trends in Facebook’s content moderation decisions about hate speech. Of particular interest was whether the company’s 2018 policy update resulted in any meaningful change.Our results indicated that only about half of reported content containing hate speech was removed. The 2018 policy change also appeared to have little impact on the company’s decision-making. The results suggest that Facebook also had substantial issues including: removing misogynistic hate speech, establishing consistency in removing attacks and threats, an inability to consider context in removal decisions, and a general lack of transparency within the hate speech removal processes. Facebook’s failure to effectively remove reported hate speech allows misethnic discourses to spread and perpetuates stereotypes. The paper concludes with recommendations for Facebook and other social media organizations to consider to minimize the amount and impact of hate speech on their platforms.

Análisis de contenido e identificación de conceptos relativos al materialismo en historiografías de la arquitectura moderna

Este artículo presenta los resultados del estudio realizado a partir del método análisis de contenido de una selección de libros sobre la temática del materialismo filosófico. El universo del discurso fue clasificado de acuerdo con el número global de citaciones recibidas desde enero de 2004 a diciembre de 2017, en diferentes bases de datos bajo el uso de herramientas digitales. El objetivo del estudio fue la creación de categorías por medio del análisis inductivo de contenidos, con el fin de construir un sistema de categorización para identificar conceptos relativos a la materialidad presentes en la arquitectura moderna. Fue posible establecer categorías caracterizadas por una fuerte unidad de contexto y con la capacidad descriptiva suficiente para orientar la búsqueda en las historiografías seleccionadas sobre arquitectura moderna. Estas categorías o unidades de significado fueron usadas para medir el grado de semejanza entre códigos y categorías de materialidad. Se encontró que la tendencia de codificación estuvo concentrada en la categoría del idealismo, esto permitiría pensar que la arquitectura de este periodo no se definió desde el carácter contingente de su objeto sino desde condiciones preconcebidas por un ideal social.

Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO

We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points of fuzzy sets in a SFP and fix the position of the fuzzy sets in the Universe of Discourse. In this way, it is possible to isolate the parameters that characterize the fuzziness of the fuzzy sets, which are subject to fine-tuning through particle swarm optimization (PSO). In this paper, we propose a formulation of the parameter space that enables the exploration of all possible levels of fuzziness in a SFP. The experimental results show that the impact of fuzziness is strongly dependent on the defuzzification procedure used in fuzzy rule-based classifiers. Fuzziness has little influence in the case of winner-takes-all defuzzification, while it is more influential in weighted sum defuzzification, which however may pose some interpretation problems.

Assessing Ontology Mappings on a Level of Concepts and Instances

In recent years, the number of domain ontologies on the Internet has been steadily increasing. Many ontologies describe overlapping universes of discourse in various ways, therefore, the need for an efficient ontology alignment method is required. Currently, there are many solutions for this problem. However, the only known way to evaluate their output is to confront it with some pre-prepared reference alignment, therefore making it impossible to incorporate in real-world applications where no reference alignment is given. This paper presents some innovative methods of evaluating ontology alignments which allows assessing their quality without the aforementioned reference alignment. The main contribution are formal foundations of such methods, algorithms developed based on those foundations, and an experimental verification of their usefulness.

CONCEPTUAL MODELING LANGUAGE AGILA MODA

Modeling of data structures has always been an important topic in the discussions of software engineering practice. Recently, the idea of conceptual modeling has lost importance in these discussions. The fact that research in this area has not been pushed further a lot for the last decade can be considered as an evidence. However, this concept has great potential. Especially the idea of creating a paradigm agnostic model depicting facts of the real world –the so called “Universe of Discourse” –instead of concrete data structures following a certain logical data model makes it so powerful and valuable. Hence, it deserves further re-search to find best practices to utilize conceptual modeling effectively. The problems that discouraged software engineers from mak-ing use of conceptual modeling is that the models are hard to understand. Creating themis time-consuming, other stakeholders do not know what to do with them and creating the final data structures requires an additional process step. After all, it is mostly per-ceived as too expensive in time and money without creating an appropriate value.In this article, the existing approaches are exam-ined to find out their weaknesses and the reasons why they did not gain a broader acceptance. Therefore, the important requirements that a conceptual modeling language has to meet for practical fielding are determined. Furthermore, the concepts of semantic mod-eling languages are examined. Using semantics instead of mere structural discussions simplifies access and understanding for non-IT stakeholders. It helps to check the validity of the created data structures against the demands of the real business. In the further course, the concept of semantically irreducible sentence modeling will be discussed which can act as a bridge between semantic and conceptual modeling.With the results of these discussions, theconceptual modeling language AGILA MOD is presented. This model-ing language bases on the idea of depicting semantically irreducible sentences as graphical model. By this, it can act as a common platform all project participants can agree upon building thebridge between IT implementation and business requirements. The models can be created from semantically irreducible sentences and they can be read backwards into semantically irreducible sen-tences making this language easy to understand for all project participants. AGILA MOD is therefore intended to be as easy as possible to get started without a lot of learning efforts. Hence, it bases on the well-known Entity-Relationship language in a simpli-fied variant. A few additional constructs are added that also refer to well-known modeling techniques reducing the efforts of learning new elements nearly to zero.The derivation of AGILA MOD models to a logical model is done by following simple derivation rules making it less time consuming and hence less cost-intensive. This language shall act as a basis for further research targeting towards the new logical models of NoSQL as well as creating a comprehensive framework automating the derivation as much as possible. Additionally, the possibility of making use of polyglot persistence with this approach and the creation of a convenient API shall be considered in future research.

Deontic Visual Signs. Between Normative Force and Constitutive Power

The most of legal theories in the twentieth century have always asserted that rules are product of linguistic utterances and that they have nothing to do with “visual culture”. In this paper I show, on the contrary, that the visual dimension is crucial to understand and found some legal-philosophical discourse. The relationship between images and law is always bi-directional, by the first direction following the way from law to images, and by the second one, vice versa, passing from images into the universe of normative discourse. In these pages I do not explore the second direction; I limit myself to investigate the first way asking two questions relevant for the construction of the legal order: Are there visual signs in the normative language? And, if so, what function do they have?

Universalism and Ecology: An Anthropological Bridge between Radhakamal Mukerjee’s Key Concepts

The wider universe of discourse for this lecture is the interface between social sciences and the humanities. This was the dominant intellectual thrust of the so-called Lucknow School of sociology in the 1960s to which Radhakamal Mukerjee (RKM) belonged. Initially, by juxtaposing RKM’s universalism to the holistic evolutionary theory, I bring together the earlier and later versions of scientific universalism, and RKM’s universalistic stance leaning to the humanities. I next contrast the somewhat narrow paradigm of ecology in RKM, with trans-nationalist ecosystems postulated in the contemporary times. Further, the reverberations of RKM’s humanistic universalism are traced in the ‘text and context’ as well as in the polyglot anthropology movements in the new anthropology. Finally, taking a cue from RKM, I note the contribution of incipient and full-blown anthropo-sociological universalism to the debate about the indigenisation of social sciences.

A multi-criteria group decision making algorithm with quadripartitioned neutrosophic weighted aggregation operators using quadripartitioned neutrosophic numbers in IPQSVNSS environment

In this paper, quadripartitioned neutrosophic numbers (QNNs) are introduced, operations over them have been defined and some of their properties have been studied. QNNs have been implemented in defining the quadripartitioned neutrosophic weighted arithmetic averaging operator and the quadripartitioned neutrosophic weighted geometric averaging operator for ranking the final scores of the alternatives and choosing the most suitable alternative among them. The concept of interval-valued possibility quadripartitioned single-valued neutrosophic soft sets has been utilized to propose an algorithm for a multi-criteria group decision making problem. In this approach, entropy-based weights are allocated to the elements of the universe of discourse under consideration. Finally, the obtained results are compared with existing ones by means of comparative studies.

Optimal granule level selection: A granule description accuracy viewpoint

Granule description has become one of the hot research topics in granular computing (GrC). Rough set theory (RST), as an important research technique for granular computing, can describe any target granule (any subset of a universe of discourse) by the lower and upper approximations. But, there is no measure to evaluate the quality of granule description in RST. Moreover, one can acquire different granule descriptions by decomposing a multi-scale information table into different single-scale information tables. Then, it is important to find the most appropriate single-scale information table for meeting specific granule description accuracy requirement. Inspired by the above problem, this paper is to discuss optimal granule level selection based on the granule description accuracy. First of all, a new granule description accuracy is defined by combining GrC and RST. After that, optimal granule level selection is investigated in a multi-scale information table subject to preserving granule description accuracies for a target granule and a group of target granules, respectively. Specially, for the case of a group of target granules, we put forward optimal granule level selection methods based on three different criteria, commonly used by people in daily life. In addition, considering that the data in real-life will be updated as time goes by, we discuss the impact on the optimal granule level when new objects are added gradually. Finally, the time complexity of the proposed algorithms is analyzed, the reasonability of setting the parameters is explained, some numerical experiments are conducted to show the effectiveness of our methods, and a comparison of our algorithms and the existing ones is made.

Control PI difuso de un sistema de levitación magnética mediante un sistema embebido

In the present work, the design and implementation of a control scheme is presented. The aim of the control scheme is to perform regulation and trajectory tracking tasks in the position of a magnetic levitation system, which acts by electromagnetic repulsion. Such levitation system consists of a beam operated by an active magnetic bearing in pendular configuration. Although the Proportional Integral Derivative (PID) controller shows arithmetic simplicity, ease of use, high robustness and error equal to zero in stable state (Pal & Mudi, 2008), the magnetic levitation system mathematical model is highly non-linear and is subject to uncertainty or variation of its parameters. Therefore, the PID control does not guarantee the fulfillment of trajectory tracking tasks (Precup & Hellendoorn, 2011). In summary, a diffuse PI is used due to the system non-linear dynamics and the hysteresis present in the electromagnet. The controller design was made with the following methodology: the mathematical model and the non-linear characteristics of the system are analyzed; the universes of error discourse (derived from error and control action) are experimentally measured. The experimental data was used for the fuzzification, defuzzification, statement of the rules and controller gains. The implemented rules were designed for a PD-Fuzzy in which a numerical integration of the control action was applied, obtaining a Fuzzy PI. Finally, the implementation was made on the STM32F407G-DISC card, which was programmed with MATLAB-Simulink software tools. The experimental results show that the proposed controller works even below the horizontal, where the behavior can show singularities or physical problems such as magnetization. In compliance with the stated objectives for a range of -5 to 10 radians, these results are maintained even in the presence of disturbances, demonstrating the feasibility of the controller.

Weakness Analysis Learning Mathematics Junior High School in Medan

Mathematics is the science that is structured and deductive object of study that includes an abstract form of facts, concepts, principles and procedures rests on an agreement, deductive thinking patterns, have an empty symbol of sense, pay attention to the universe of discourse and consistent system. Mathematics has a language and a well-defined rules, a clear and systematic reasoning, and the structure and the linkages between the concept is strong. The main elements of the mathematical work is deductive reasoning which works on the basis of assumptions and have a consistent truth. So that mathematics is known as a deductive science, which means that the mathematics starts from an undefined elements, axioms / postulates and ultimately lowering the theorem. Math formed as a result of human thinking which relate to the idea,Mathematics is the fruit of human thought that the truth of a general nature which does not rely on the scientific method that contains the inductive process. Mathematics is one of the most important auxiliary science and useful in everyday life as well as in supporting the development of human resources and the development of science and technology. Such formal mathematics taught since elementary school through college. Therefore, school mathematics learning should take place effectively and with quality.

Bullying and interpersonal conflict from a “dialogic event” perspective

Through discussions in class meetings of “concrete situations” experienced by students in their lived experience in and out of the classroom, educators have been encouraged to guide students to understand how bullying applies to their lives, and to learn the degree to which bullying is present or absent in their relationships with their peers (Olweus, 1993). In observations of and interviews about the class meetings at a private, progressive U.S. middle school, student and teacher discourses in response to students’ interpersonal dynamics are found to exist within separate, parallel universes. The teachers’ discourse universe presumes that the lived experience of students can be understood through and guided by abstract, Kantian-like moral universal imperatives – specifically, the imperative to “feel good” and the imperative not to bully. These imperatives supplant dialogue on the events of students’ experiences toward a focus on who the students are becoming rather than who they are now. This discourse of “half-being” maps the students’ experiences upon what is known, predictable, universal, unsurprising and imagined, and assumes that students are not fully responsible for their own or each other’s well-being. By contrast, the students’ discourse on their interpersonal dynamics is characterized by Bakhtin’s (1993) notion of “being-as-event” discourse, which is highly contextualized, unpredictable, and focuses upon everyone’s responsibility to ongoing dramatic and ontologically charged events (either immediate or recursive in nature). The students’ discursive universe is conducive to dialogue, whereas the teachers’ discursive universe supplants the students’ messy, unpredictable and dialogically responsible discourse, thus arresting the possibility for teachers and peers to provide meaningful and authoritative guidance to dialogic events. The reasons for teachers’ attraction to Kantian-like abstract moral universals as well as the consequences of the supplanting of students’ event-filled discourse with the discourse of bullying are discussed

Confused Terms in Ordinary Language

Confused terms appear to signify more than one entity. Carnap (Meaning and necessity, University of Chicago Press, Chicago, 1956) maintained that any putative name that is associated with more than one object in a relevant universe of discourse fails to be a genuine name. Although many philosophers have agreed with Carnap, they have not always agreed among themselves about the truth-values of atomic sentences containing such terms. Some hold that such atomic sentences are always false, and others claim they are always truth-valueless. Field (J Philos 70:462–481, 1973) maintained that confused terms can still refer, albeit partially, and offered a supervaluational account of their semantic properties on which some atomic sentences with confused terms can be true. After outlining many of the most important theoretical considerations for and against various semantic theories for such terms, we report the results of a study designed to investigate which of these accounts best accords with the truth-value judgments of ordinary language users about sentences containing these terms. We found that naive participants view confused names as capable of successfully referring to one or more objects. Thus, semantic theories that judge them to involve total reference failure do not comport well with patterns of ordinary usage.

A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm

This article acquaints a new method to forecast the time series dataset based on neutrosophic-quantum optimization approach. This study uses neutrosophic set (NS) theory to represent the inherited uncertainty of time series dataset with three different memberships as truth, indeterminacy and false. We refer such representations of time series dataset as neutrosophic time series (NTS). This NTS is further utilized for modeling and forecasting time series dataset. Study showed that the performance of NTS modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. To resolve this issue, this study selects quantum optimization algorithm (QOA) and ensembles with the NTS modeling approach. QOA improves the performance of the NTS modeling approach by selecting the globally optimal universe of discourse and its corresponding intervals from the list of local optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signify the efficiency of the proposed NTS-QOA model over existing benchmark models in terms of average forecasting error rates (AFERs) of 0.44%, 0.066% and 1.27% for the university enrollment, TAIFEX index and TSEC weighted index, respectively.

A NEW HYBRID FUZZY TIME SERIES FORECASTING MODEL BASED ON COMBINING FUZZY C-MEANS CLUSTERING AND PARTICLE SWAM OPTIMIZATION

Fuzzy time series (FTS) model is one of the effective tools that can be used to identify factors in order to solve the complex process and uncertainty. Nowadays, it has been widely used in many forecasting problems. However, establishing effective fuzzy relationships groups, finding proper length of each interval, and building defuzzification rule are three issues that exist in FTS model. Therefore, in this paper, a novel FTS forecasting model based on fuzzy C-means (FCM) clustering and particle swarm optimization (PSO) was developed to enhance the forecasting accuracy. Firstly, the FCM clustering is used to divide the historical data into intervals with different lengths. After generating interval, the historical data is fuzzified into fuzzy sets. Following, fuzzy relationship groups were established based on the appearance history of the fuzzy sets on the right-hand side of the fuzzy logical relationships with the aim to serve for calculating the forecasting output. Finally, the proposed model combined with PSO algorithm was applied to adjust interval lengths and find proper intervals in the universe of discourse for obtaining the best forecasting accuracy. To verify the effectiveness of the forecasting model, three numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange –TAIFEX data and yearly deaths in car road accidents in Belgium) are selected to illustrate the proposed model. The experimental results indicate that the proposed model is better than any existing forecasting models in term of forecasting accuracy based on the first – order and high-order FTS.

A NEW HYBRID FUZZY TIME SERIES FORECASTING MODEL BASED ON COMBINING FUZZY C-MEANS CLUSTERING AND PARTICLE SWAM OPTIMIZATION

Fuzzy time series (FTS) model is one of the effective tools that can be used to identify factors in order to solve the complex process and uncertainty. Nowadays, it has been widely used in many forecasting problems. However, establishing effective fuzzy relationships groups, finding proper length of each interval, and building defuzzification rule are three issues that exist in FTS model. Therefore, in this paper, a novel FTS forecasting model based on fuzzy C-means (FCM) clustering and particle swarm optimization (PSO) was developed to enhance the forecasting accuracy. Firstly, the FCM clustering is used to divide the historical data into intervals with different lengths. After generating interval, the historical data is fuzzified into fuzzy sets. Following, fuzzy relationship groups were established based on the appearance history of the fuzzy sets on the right-hand side of the fuzzy logical relationships with the aim to serve for calculating the forecasting output. Finally, the proposed model combined with PSO algorithm was applied to adjust interval lengths and find proper intervals in the universe of discourse for obtaining the best forecasting accuracy. To verify the effectiveness of the forecasting model, three numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange –TAIFEX data and yearly deaths in car road accidents in Belgium) are selected to illustrate the proposed model. The experimental results indicate that the proposed model is better than any existing forecasting models in term of forecasting accuracy based on the first – order and high-order FTS.

Analogy between concepts

Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework.