Review of Tools for Semantics Extraction: Application in Tsunami Research Domain

Immense numbers of textual documents are available in a digital form. Research activities are focused on methods of how to speed up their processing to avoid information overloading or to provide formal structures for the problem solving or decision making of intelligent agents. Ontology learning is one of the directions which contributes to all of these activities. The main aim of the ontology learning is to semi-automatically, or fully automatically, extract ontologies—formal structures able to express information or knowledge. The primary motivation behind this paper is to facilitate the processing of a large collection of papers focused on disaster management, especially on tsunami research, using the ontology learning. Various tools of ontology learning are mentioned in the literature at present. The main aim of the paper is to uncover these tools, i.e., to find out which of these tools can be practically used for ontology learning in the tsunami application domain. Specific criteria are predefined for their evaluation, with respect to the “Ontology learning layer cake”, which introduces the fundamental phases of ontology learning. ScienceDirect and Web of Science scientific databases are explored, and various solutions for semantics extraction are manually “mined” from the journal articles. ProgrammableWeb site is used for exploration of the tools, frameworks, or APIs applied for the same purpose. Statistics answer the question of which tools are mostly mentioned in these journal articles and on the website. These tools are then investigated more thoroughly, and conclusions about their usage are made with respect to the tsunami domain, for which the tools are tested. Results are not satisfactory because only a limited number of tools can be practically used for ontology learning at present.

Intelligent face recognition system based on OpenMV

With the rapid development of e-commerce in Internet technology, online shopping has become the mainstream shopping method accepted and favored by people. E-commerce and online shopping not only bring convenience to people’s life, but also aggravate the surge of express delivery. In order to improve the pick-up efficiency, this paper designs an intelligent pick-up express system based on OpenMV face recognition. The system takes STM32 single chip microcomputer as the core controller, and reads and transmits express information based on OpenMV face recognition; The trolley tracks and avoids obstacles independently, and takes parts according to the planned path of the system. Experiments show that the system can realize express automatic pick-up, and has a broad application prospect.

Yes, no, maybe, I don’t know: Complexity and application of abstract argumentation with incomplete knowledge

argumentation, as originally defined by Dung, is a model that allows the description of certain information about arguments and relationships between them: in an abstract argumentation framework (AF), the agent knows for sure whether a given argument or attack exists. It means that the absence of an attack between two arguments can be interpreted as “we know that the first argument does not attack the second one”. But the question of uncertainty in abstract argumentation has received much attention in the last years. In this paper, we survey approaches that allow to express information like “There may (or may not) be an attack between these arguments”. We describe the main models that incorporate qualitative uncertainty (or ignorance) in abstract argumentation, as well as some applications of these models. We also highlight some open questions that deserve some attention in the future.

Extracting and analyzing inorganic material synthesis procedures in the literature

Analyzing material synthesis procedures in the literature is required to collect structural information of material names and synthesis procedures for designing materials computationally. Since synthesis procedures are mostly written in natural language in paper or technical documents, they need to be extracted and structured into a format that can be handled by a computer through information extraction. Moreover, to represent a synthesis procedure, it is necessary to express information such as conditions and the order of operations in the procedure, but existing databases that compile structural information of material names and synthesis procedures of materials do not provide such information about procedures. It is, therefore, necessary to create a framework that extracts and organizes the information of synthesis procedures in text so that the information is enough for material development such as the order of operations and the links among materials, operations, and conditions. In this study, we construct a pipeline system that extracts synthesis procedures from a text in the form of a flow graph. The extraction system consists of preprocessing, deep learning-based entity extraction, rule-based relation extraction, and selection for paragraph-containing procedures. We applied the system to a large body of literature and extracted flow graphs (procedures) that include about 4 million entities and 3 million relations. We took several statistics on the extracted graphs and performed several analyses on the extracted graphs. We experimentally confirmed that some extracted operations were specific to the target material and the frequently extracted sub-graphs include reasonable operations.

A Study for Advanced Visualization of Sensing Data & Meta Data based WSN

As the 5G environment is established due to the development of network and information and network technologies, wireless sensor network is drawing attention as a technology that will lead the information technology industry in the future. The sensor-based service must collect and process data recognized from various sensors and deliver information to users to understand. However, there is no research to visually express information about sensor resources to make it easier for users to understand. Therefore, this research proposes an efficient visualization model that is both realistic and economical. Then, we designed a scenario applied to real life and built a webpage.

EXPRESS: Information Disclosure Policy and Its Implications: Ratcheting in Supply Chains

Fear of escalating input prices in response to retail success is a commonly-discussed phenomenon affecting supply chains. Such a ratchet effect arises when a retailer feels compelled to modify his investments to better serve the end customers in order to hide positive prospects and restrain future wholesale price hikes. In a two-period model of supply chain interactions, the authors demonstrate that such an endogenous ratchet effect can have multi-faceted reverberations. A retailer fearing price hikes may be tempted to curtail near-term profits to ensure favorable long-term pricing. In response, the supplier can use deep discounts in its initial wholesale prices to convince the retailer to focus on its short-run profits rather than long-run pricing concerns. These deep discounts not only encourage mutually beneficial investments but also alleviate double-marginalization inefficiencies along the supply chain. In light of these results, the authors demonstrate that the mandatory information disclosure policy to reduce the ratchet effect decreases total channel efficiency compared to the case without information disclosure, precisely because mandatory disclosure interrupts the healthy tension among supply chain partners. Thus, the model presents a scenario where ratcheting concerns can create a degree of self-enforcing cooperation that results in socially beneficial responses in supply chains.

Sketch Based Image Retrieval using Deep Learning Based Machine Learning

Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy.

Features of the functioning of the plural form -lar in the Turkic languages from the standpoint of ethnolinguistics

In this article, the author analyzes the Turkic category of numbers. The author has long been attracted by the special functioning of the plurality affix with the indicator -lar in the texts of various Turkic languages, starting with the most ancient monuments written in runic script, ending with the literary works of modern Turkish writers. This special functioning lies in the specific semantics of plural forms, and in how less frequently, compared to Western languages, these forms are used in written speech, not to mention oral. In addition, the ability of the basis of the word to express information about a variety of subjects without the specified indicator has repeatedly fallen into the center of research attention, i.e. in cases where the content is about a variety of subjects, the multiplicity indicator -lar is not used. Based on linguistic methods, the author of the article hypothesized that in the Turkic proto-language the category of number as a morphological means could be absent at all, and information about a variety of objects could be transmitted in other, non-morphological ways. At a new stage of the study, it means using the methods of ethnolinguistic analysis, the author seeks to find a connection between the infrequent use of plural forms and even its hypothetical absence in the language of an older period with the extralinguistic features of the Turkic mentality. This becomes possible if the scientific view is based on such factors that form the mentality as the geographical location of an ethnic group, the size of tribal collectives and the degree of cohesion of the collective members among themselves.

Puberphonia: From classic to modern approach

Voice is a significant component of communication that allows us to express information and emotions, so it is the foundation of verbal communication. Maturation of the body involves dilation of the larynx and lower positioning of the larynx in the neck, resulting in multiple changes in voice quality. The rapid changes in the human larynx during puberty are more evident in males. Such changes can result in voice mutation - puberphonia. Puberphonia, also called mutational dysphonia or mutational falsetto, is the failure of a natural decrease in fundamental frequency or pitch. We can also defined puberphonia as persistent adolescent voice even after puberty in the absence of organic cause. This functional voice disorder can have multiple consequences on the personality and quality of life of an individual that often encounters problems that include psychological, emotional, social, and professional difficulties. This article aims to review the relevant and accessible literature on puberphonia in a comprehensive concise manner, highlighting the etiology, prevalence, clinical manifestation, consequences on quality of life, as well as evolution of the approach and attitude to its treatment.