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In 1965, Jack Edmonds characterized pairs of graphs G and G* with a bijection between their edge sets that form a pair of dual graphs realizing the vertices and countries of a map embedded in a surface. A necessary condition is that, if d = (d1, , dn) and t = (t1, , tm) denote the degree sequences of two such graphs, then ∑ i = 1 n d i = ∑ j = 1 m t j = 2 l , where l is the number of edges in each of the two graphs and χ = n + m - l is the Euler characteristic of the surface. However, this condition is not sufficient, and it is an open question to characterize bi-vectors (d, t) that are geographic, that is, that can be realized as the degree sequences of pairs G and G* of surface-embedded graphs. The above question is a special case of the following one. A multigraph G is even if each vertex has even degree and 3-colored if G is equipped with a fixed proper coloring of its vertex set assigning each vertex a color in the set {1,2,3}. Let G be a 3-colored even multigraph embedded in a surface S so that every face is a triangle. Denote by d = (d1, , dn), t = (t1, , tm), and δ = (δ1, ..., , δk) the sequences of half-degrees of vertices of G of colors 1, 2, and 3, respectively. Then, ∑ i = 1 n d i = ∑ j = 1 m t j = ∑ µ = 1 k t µ = l , where χ = n + k + m - l is the Euler characteristic of the surface S. A tri-vector (d, t, δ) satisfying the above conditions is called feasible. A feasible tri-vector is called geographic if it is realized by a 3-colored triangulation of a surface. Geographic tri-vectors extend the concept of geographic bi-vectors. We present a dataset of geographic bi-vectors and tri-vectors, along with realizations proving that they are geographic.
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The Bosnian language holds significant importance as a member of the West-South Slavic subgroup within the Slavic branch of the Indo-European linguistic family. With approximately 2.5 million speakers in Europe, including 1.87 million individuals in Bosnia and Herzegovina alone, the Bosnian language constitutes the mother tongue for a considerable portion of the population. In Natural Language Processing (NLP) tasks related to the Bosnian language, besides removing stop words, it is important to consider the influence of other linguistic elements. Bosnian text contains words derived from diminishers, relative intensifiers, minimizers, maximizers, boosters, and approximators. These words contribute to the overall meaning and sentiment analysis of the text. By including these elements in NLP models and algorithms, researchers can achieve more accurate and nuanced analysis of Bosnian language data, enhancing the effectiveness of NLP applications. The two lists of sentiment annotated words that present the core of the Bosnian sentiment-annotated lexicon, a list of the stopwords, and a list of Affirmative and non-Affrimative words (AnAwords) composed mostly of intensifiers and diminishers, were used to construct a dataset that presents the base for sentiment analysis in the Bosnian language.
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Background: Online implementation of suicide prevention interventions offers many advantages, facilitating the dissemination of large-scale suicide prevention interventions. An online tool iAlive aimed at raising awareness and increasing suicide prevention competences in lay people was developed and implemented in Slovenia. Aims: To develop, implement, and evaluate the iAlive tool. Method: Following the development and implementation of the tool, a nonrandomized controlled study with 310 participants was conducted. One hundred fifty-six of them fully completed the study [intervention group (used the iAlive tool): N = 85, control group (did not use the tool): N = 71]. Perceived competences in engaging with a suicidal person were assessed in both groups at baseline and at follow-up (3-4 weeks apart), which also represents the time of the intervention. Results: A significant effect of time and condition [F(1,149) = 6.62, p = .011, ηp2 = .043] showed that the intervention group assessed their perceived competences on intervention exposure more positively compared to the control group. Limitations: Additional data on different populations and people's engagement with the tool in relation to perceived competences are needed. Conclusion: The study suggests that the interactive online tool iAlive effectively increases perceived competences in engaging with a suicidal person. These results provide a background for further dissemination of the tool.
Asunto(s)
Prevención del Suicidio , Humanos , Masculino , Femenino , Adulto , Persona de Mediana Edad , Adulto Joven , Eslovenia , Intervención basada en la Internet , Evaluación de Programas y Proyectos de Salud , Internet , Ideación Suicida , AdolescenteRESUMEN
The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for the IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architectures that harness the potential of edge devices, reduce network latency, and lower infrastructure costs to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. In this paper, we introduce a decentralized architecture tailored for large-scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. We propose a leader election consensus protocol for permissioned distributed networks that only requires one series of messages in order to commit to a change. The solution combines a blockchain consensus protocol using Verifiable Delay Functions (VDF) to achieve decentralized randomness, fault tolerance, transparency, and no single point of failure. We validate our solution by testing and analyzing the performance of our reference implementation. Our results show that nodes are able to reach consensus consistently, and the VDF proofs can be used as an entropy pool for decentralized randomness. We show that our system can perform autonomous real-time application migrations. Finally, we conclude that the implementation is scalable by testing it on 100 consensus nodes running 200 applications.
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The dataset presented in this paper aims to address the challenge of automatic extraction of stop words in Natural Language Processing (NLP) for the low-resource Karakalpak language spoken by approximately two million people in Uzbekistan. To accomplish this, we have created a corpus of 23 Karakalpak language school textbooks, which we have named the Karakalpak Language School Corpus (KAASC). Using the KAASC corpus, we have constructed lists of stop words using three methods based on Term Frequency-Inverse Document Frequency (TF-IDF): unigram, bigram, and collocation methods, respectively. The resulting lists of stop words, along with a list of URLs used to construct the corpus, make up the described dataset in this paper.
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Filtering stop words is an important task when processing text queries to search for information in large data sets. It enables a reduction of the search space without losing the semantic meaning. The stop words, which have only grammatical roles and not contributing to information content still add up to the complexity of the query. Existing mathematical models that are used to tackle this problem are not suitable for all families of natural languages [1]. For example, they do not cover families of languages to which Uzbek can be included. In the present work, the collocation method of this problem is o ered for families of languages that include the Uzbek language as well. This method concerns the so-called agglutinative languages, in which the task of recognizing stop words is much more difficult, since the stop words are "masked" in the text. In this work the unigram, the bigram and the collocation methods are applied to the "School corpus" that corresponds to the type of languages being studied.
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A smart floor with 16 embedded pressure sensors was used to record 420 simulated fall events performed by 60 volunteers. Each participant performed seven fall events selected from the guidelines defined in a previous study. Raw data were grouped and well organized in CSV format. The data was collected for the development of a non-intrusive fall detection solution based on the smart floor. Indeed, the collected data can be used to further improve the current solution by proposing new fall detection techniques for the correct identification of accidental fall events on the smart floor. The gathered fall simulation data is associated with participants' demographic characteristics, useful for future expansions of the smart floor solution beyond the fall detection problem.