Search results for: latent semantic indexing

Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: U. S. N. Raju, Kothuri Sai Kiran, Meena G. Kamal, Vinay Nikhil Pabba, Suresh Kanaparthi

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There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efficient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework.

Keywords: video lectures, big video data, video retrieval, hadoop

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Authors: Luís Antonio de Almeida Rodriguez, José Maria Parente de Oliveira, Ednelson Oliveira

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In the last two decades, the world’s aviation authorities have made several attempts to create consensus about a global and accepted approach for applying semantics to web services registry descriptions. This problem has led communities to face a fat and disorganized infrastructure to describe aeronautical web services. It is usual for developers to implement ad-hoc connections among consumers and providers and manually create non-standardized service compositions, which need some particular approach to compose and semantically discover a desired web service. Current practices are not precise and tend to focus on lightweight specifications of some parts of the OWL-S and embed them into syntactic descriptions (SOAP artifacts and OWL language). It is necessary to have the ability to manage the use of both technologies. This paper presents an implementation of the ontology OWL-S that describes a Brazilian Aeronautical Web Service Registry, which makes it able to publish, advertise, make multi-criteria semantic discovery aligned with the ideas of the System Wide Information Management (SWIM) Program, and invoke web services within the Air Traffic Management context. The proposal’s best finding is a generic approach to describe semantic web services. The paper also presents a set of functional requirements to guide the ontology development and to compare them to the results to validate the implementation of the OWL-S Ontology.

Keywords: aeronautical web services, OWL-S, semantic web services discovery, ontologies

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Authors: Oxana Lukoshus

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The methods of corpus linguistics, a well-established field of research, are being increasingly applied in cognitive linguistics. Corpora data are especially useful for different quantitative studies of grammatical and other aspects of language. The main objective of this paper is to demonstrate how present-day corpora can be applied in semantic studies in general and in semantic studies of adjectives in particular. Polysemantic adjectives have been the subject of numerous studies. But most of them have been carried out on dictionaries. Undoubtedly, dictionaries are viewed as one of the basic data sources, but only at the initial steps of a research. The author usually starts with the analysis of the lexicographic data after which s/he comes up with a hypothesis. In the research conducted three polysemantic synonyms true, loyal, faithful have been analyzed in terms of differences and similarities in their semantic structure. A corpus-based approach in the study of the above-mentioned adjectives involves the following. After the analysis of the dictionary data there was the reference to the following corpora to study the distributional patterns of the words under study – the British National Corpus (BNC) and the Corpus of Contemporary American English (COCA). These corpora are continually updated and contain thousands of examples of the words under research which make them a useful and convenient data source. For the purpose of this study there were no special needs regarding genre, mode or time of the texts included in the corpora. Out of the range of possibilities offered by corpus-analysis software (e.g. word lists, statistics of word frequencies, etc.), the most useful tool for the semantic analysis was the extracting a list of co-occurrence for the given search words. Searching by lemmas, e.g. true, true to, and grouping the results by lemmas have proved to be the most efficient corpora feature for the adjectives under the study. Following the search process, the corpora provided a list of co-occurrences, which were then to be analyzed and classified. Not every co-occurrence was relevant for the analysis. For example, the phrases like An enormous sense of responsibility to protect the minds and hearts of the faithful from incursions by the state was perceived to be the basic duty of the church leaders or ‘True,’ said Phoebe, ‘but I'd probably get to be a Union Official immediately were left out as in the first example the faithful is a substantivized adjective and in the second example true is used alone with no other parts of speech. The subsequent analysis of the corpora data gave the grounds for the distribution groups of the adjectives under the study which were then investigated with the help of a semantic experiment. To sum it up, the corpora-based approach has proved to be a powerful, reliable and convenient tool to get the data for the further semantic study.

Keywords: corpora, corpus-based approach, polysemantic adjectives, semantic studies

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Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

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Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

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Authors: Yukiko Sasaki Alam, Shahid Alam

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Syntactic parsing is vital for semantic treatment by many applications related to natural language processing (NLP), because form and content coincide in many cases. However, it has not yet reached the levels of reliable performance. By manually examining and analyzing individual machine translation output errors that involve syntax as well as semantics, this study attempts to discover what is required for improving syntactic and semantic parsing.

Keywords: syntactic parsing, error analysis, machine translation, deep parsing

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Authors: Yi-Hsiu Lai

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The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future.

Keywords: lexical-semantic processing, Mandarin Chinese, naming, category effects

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Authors: Tshaiya Devi Thandauthapani, Adam J. Reeve, Adam S. Long, Ian J. Turner, James S. Sharp

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Fingermarks are a crucial form of evidence for identifying a person at a crime scene. However, visualising latent (hidden) fingermarks can be difficult, and the correct choice of techniques is essential to develop and preserve any fingermarks that might be present. Knives, firearms and other metal weapons have proven to be challenging substrates (stainless steel in particular) from which to reliably obtain fingermarks. In this study, time of flight secondary ion mass spectroscopy (ToF-SIMS) was used to image fingermarks on metal surfaces. This technique was compared to a conventional superglue based fuming technique that was accompanied by a series of contrast enhancing dyes (basic yellow 40 (BY40), crystal violet (CV) and Sudan black (SB)) on three different metal surfaces. The conventional techniques showed little to no evidence of fingermarks being present on the metal surfaces after a few days. However, ToF-SIMS images revealed fingermarks on the same and similar substrates with an exceptional level of detail demonstrating clear ridge definition as well as detail about sweat pore position and shape, that persist for over 26 days after deposition when the samples were stored under ambient conditions.

Keywords: conventional techniques, latent fingermarks, metal substrates, time of flight secondary ion mass spectroscopy

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Authors: Telesphore Tiendrebeogo, Oumarou Sié

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Cloud computing technologies have attracted considerable interest in recent years. Thus, these latters have become more important for many existing database applications. It provides a new mode of use and of offer of IT resources in general. Such resources can be used “on demand” by anybody who has access to the internet. Particularly, the Cloud platform provides an ease to use interface between providers and users, allow providers to develop and provide software and databases for users over locations. Currently, there are many Cloud platform providers support large scale database services. However, most of these only support simple keyword-based queries and can’t response complex query efficiently due to lack of efficient in multi-attribute index techniques. Existing Cloud platform providers seek to improve performance of indexing techniques for complex queries. In this paper, we define a new cloud computing architecture based on a Distributed Hash Table (DHT) and design a prototype system. Next, we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with others clouds systems to show our solution ensures consistence and scalability for Cloud platform.

Keywords: virtual coordinates, cloud, hyperbolic plane, storage, scalability, consistency

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Abdel Rahman Altakhaineh

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This paper has analysed the conceptual relations between the elements of NN compounds in Arabic and compared them to those found in English based on the framework of Conceptual Semantics and a modified version of Parallel Architecture referred to as Relational Morphology. The analysis revealed that the repertoire of possible semantic relations between the two nouns in Arabic NN compounds reproduces that in English NN compounds and that, therefore, the main difference is in headedness (right-headed in English, left-headed in Arabic). Adopting RM allows productive and idiosyncratic elements to interweave with each other naturally. Semantically transparent compounds can be stored in memory or produced and understood online, while compounds with different degrees of semantic idiosyncrasy are stored in memory. Furthermore, the predictable parts of idiosyncratic compounds are captured by general schemas. In compounds, such schemas pick out the range of possible semantic relations between the two nouns. Finally, conducting a cross-linguistic study of the systematic patterns of possible conceptual relationships between compound elements is an area worthy of further exploration. In addition, comparing and contrasting compounding in Arabic and Hebrew, especially as they are both Semitic languages, is another area that needs to be investigated thoroughly. It will help morphologists understand the extent to which Jackendoff’s repertoire of semantic relations in compounds is universal. That is, if a language as distant from English as Arabic displays a similar range of cases, this is evidence for a (relatively) universal set of relations from which individual languages may pick and choose.

Keywords: conceptual semantics, morphology, compounds, arabic, english

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Authors: Fateh Adhnouss, Husam El-Asfour, Kenneth McIsaac, AbdulMutalib Wahaishi, Idris El-Feghia

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Conceptualization is an essential component of semantic ontology-based approaches. There have been several approaches that rely on extensional structure and extensional reduction structure in order to construct conceptualization. In this paper, several limitations are highlighted relating to their applicability to the construction of conceptualizations in dynamic and open environments. These limitations arise from a number of strong assumptions that do not apply to such environments. An intensional structure is strongly argued to be a natural and adequate modeling approach. This paper presents a conceptualization structure based on property relations and propositions theory (PRP) to the model ontology that is suitable for open environments. The model extends the First-Order Logic (FOL) notation and defines the formal representation that enables interoperability between software systems and supports semantic integration for software systems in open, dynamic environments.

Keywords: conceptualization, ontology, extensional structure, intensional structure

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Authors: Emre Basturk, Memet Vezir Kahraman

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In many developing countries, with the rapid economic improvements, energy shortage and environmental issues have become a serious problem. Therefore, it has become a very critical issue to improve energy usage efficiency and also protect the environment. Thermal energy storage system is an essential approach to match the thermal energy claim and supply. Thermal energy can be stored by heating, cooling or melting a material with the energy and then enhancing accessible when the procedure is reversed. The overall thermal energy storage techniques are sorted as; latent heat or sensible heat thermal energy storage technology segments. Among these methods, latent heat storage is the most effective method of collecting thermal energy. Latent heat thermal energy storage depend on the storage material, emitting or discharging heat as it undergoes a solid to liquid, solid to solid or liquid to gas phase change or vice versa. Phase change materials (PCMs) are promising materials for latent heat storage applications due to their capacities to accumulate high latent heat storage per unit volume by phase change at an almost constant temperature. Phase change materials (PCMs) are being utilized to absorb, collect and discharge thermal energy during the cycle of melting and freezing, converting from one phase to another. Phase Change Materials (PCMs) can generally be arranged into three classes: organic materials, salt hydrates and eutectics. Many kinds of organic and inorganic PCMs and their blends have been examined as latent heat storage materials. Organic PCMs are rather expensive and they have average latent heat storage per unit volume and also have low density. Most organic PCMs are combustible in nature and also have a wide range of melting point. Organic PCMs can be categorized into two major categories: non-paraffinic and paraffin materials. Paraffin materials have been extensively used, due to their high latent heat and right thermal characteristics, such as minimal super cooling, varying phase change temperature, low vapor pressure while melting, good chemical and thermal stability, and self-nucleating behavior. Ultraviolet (UV)-curing technology has been generally used because it has many advantages, such as low energy consumption , high speed, high chemical stability, room-temperature operation, low processing costs and environmental friendly. For many years, PCMs have been used for heating and cooling industrial applications including textiles, refrigerators, construction, transportation packaging for temperature-sensitive products, a few solar energy based systems, biomedical and electronic materials. In this study, UV-curable, fatty alcohol containing soybean oil based phase change materials (PCMs) were obtained and characterized. The phase transition behaviors and thermal stability of the prepared UV-cured biobased PCMs were analyzed by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The heating process phase change enthalpy is measured between 30 and 68 J/g, and the freezing process phase change enthalpy is found between 18 and 70 J/g. The decomposition of UVcured PCMs started at 260 ºC and reached a maximum of 430 ºC.

Keywords: fatty alcohol, phase change material, thermal energy storage, UV curing

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Authors: Jasmine H. Gagnon

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The present study examined how grandiose narcissists make adaptive trade-offs between mating investment, parenting investment, and somatic investment relative to individuals without narcissistic personalities. A sample of 509 males and females between the ages of 24 and 35 years old (49.31% female) completed a personality inventory assessing Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience. In a Latent Profile Analysis (LPA), personality inventory scores were used to classify participants into latent groups. The model of best fit identified one grandiose narcissist group and three groups with non-narcissistic personalities. Covariate analyses revealed that individuals with narcissistic traits made significantly more significant somatic investments in comparison to two of the three non-narcissistic latent groups. No other significant differences between the narcissistic and non-pathological groups were found. Thus, grandiose narcissists trade off parenting and mating investments to make more significant somatic investments. That is, they expend a larger portion of their energetic resources on maintaining their physical health and careers and similar quantities of energetic resources on maintaining relationships with their offspring and potential romantic partners as individuals without narcissistic personalities.

Keywords: narcissism, grandiose narcissism, HEXACO, trade-offs, mating, parenting, somatic, dark triad

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Authors: Narmina Mammadova

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In modern linguistics, the comparative study of languages is becoming increasingly popular, the typology and comparison of languages that have different structures is expanding and deepening. Of particular interest is the study of phraseological units, which makes it possible to identify the specific features of the compared languages in all their national identity. This paper gives a brief analysis of the comparative study of somatic phraseological units (SFU) of the Spanish and Turkish languages with the component "hand" in the semantic aspect; identification of equivalents, analogs and non-equivalent units, as well as a description of methods of translation of non-equivalent somatic phraseological units. Comparative study of the phraseology of unrelated languages is of particular relevance since it allows us to identify both general, universal features and differential and specific features characteristic of a particular language. Based on the results of the generalization of the study, it can be assumed that phraseological units containing a somatic component have a high interlingual phraseological activity, which contributes to an increase in the degree of interlingual equivalence.

Keywords: Linguoculturology, Turkish, Spanish, language picture of the world, phraseological units, semantic microfield

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Authors: Lucia Rottava

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The present study focuses on academic literacy and addresses the impact of semantic-discursive resources on the constitution of genres that are produced in such context. The research considers the development of writing in the academic context in Portuguese. Researches that address academic literacy and the characteristics of the texts produced in this context are rare, mainly with focus on the development of writing, considering three variables: the constitution of the writer, the perception of the reader/interlocutor and the organization of the informational text flow. The research aims to map the semantic-discursive resources of the written register in texts of several genres and produced by students in the first semester of the undergraduate course in letters. The hypothesis raised is that writing in the academic environment is not a recurrent literacy practice for these learners and can be explained by the ontogenetic and phylogenetic nature of language development. Qualitative in nature, the present research has as empirical data texts produced in a half-yearly course of Reading and Textual Production; these data result from the proposition of four different writing proposals, in a total of 600 texts. The corpus is analyzed based on semantic-discursive resources, seeking to contemplate relevant aspects of language (grammar, discourse and social context) that reveal the choices made in the reader/writer interrelationship and the organizational flow of the text. Among the semantic-discursive resources, the analysis includes three resources, including (a) appraisal and negotiation to understand the attitudes negotiated (roles of the participants of the discourse and their relationship with the other); (b) ideation to explain the construction of the experience (activities performed and participants); and (c) periodicity to outline the flow of information in the organization of the text according to the genre it instantiates. The results indicate the organizational difficulties of the flow of the text information. Cartography contributes to the understanding of the way writers use language in an effort to present themselves, evaluate someone else’s work, and communicate with readers.

Keywords: academic writing, portuguese mother tongue, semantic-discursive resources, sistemic funcional linguistic

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Authors: Linda Smail, Zineb Azouz

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Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks.

Keywords: Bayesian networks, D-Separation, level two Bayesian networks, factorization of computation

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Authors: Mesnan Silalahi, Dana Indra Sensuse, Indra Budi

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Many research have revealed the fact of the complexity of ontology building process that there is a need to have a new approach which addresses the socio-technical aspects in the collaboration to reach a consensus. Meta-design approach is considered applicable as a method in the methodological model in a socio-technical ontology engineering. Principles in the meta-design framework is applied in the construction phases on the ontology. A portal is developed to support the meta-design principles requirements. To validate the methodological model semantic web applications were developed and integrated in the portal and also used as a way to show the usefulness of the ontology. The knowledge based system will be filled with data of Indonesian medicinal plants. By showing the usefulness of the developed ontology in a web semantic application, we motivate all stakeholders to participate in the development of knowledge based system of medicinal plants in Indonesia.

Keywords: socio-technical, metadesign, ontology engineering methodology, semantic web application

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Authors: M. A. T. AlSudairi, T. G. K. Vasista

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The concept of social media is becoming the top of the agenda for many business executives and public sector executives today. Decision makers as well as consultants, try to identify ways in which firms and enterprises can make profitable use of social media and network related applications such as Wikipedia, Face book, YouTube, Google+, Twitter. While it is fun and useful to participating in this media and network for achieving the communication effectively and efficiently, semantic and sentiment analysis and interpretation becomes a crucial issue. So, the objective of this paper is to provide literature review on social media, network and related technology related to semantics and sentiment or opinion analysis covering business and governance perspectives. In this regard, a case study on the use and adoption of Social media in Saudi Arabia has been discussed. It is concluded that semantic web technology play a significant role in analyzing the social networks and social media content for extracting the interpretational knowledge towards strategic decision support.

Keywords: CRASP methodology, formative assessment, literature review, semantic web services, social media, social networks

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Authors: Hui Shi

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This study investigates a newly emerged usage of Chinese numeral classifier mei (枚) in the cyberspace. In modern Chinese grammar, mei as a classifier should occupy the pre-nominal position, and its valid accompanying nouns are restricted to small, flat, fragile inanimate objects rather than humans. To examine the semantic change of mei, two types of data from Weibo.com were collected. First, 500 mei-included Weibo posts constructed a corpus for analyzing this classifier's word order distribution (post-nominal or pre-nominal) as well as its accompanying nouns' semantics (inanimate or human). Second, considering that mei accompanies a remarkable number of human nouns in the first corpus, the second corpus is composed of mei-involved Weibo IDs from users located in first and third-tier cities (n=8 respectively). The findings show that in the cyber community, mei frequently classifies human-related neologisms at the archaic post-normal position. Besides, the 23 to 29-year-old females as well as Weibo users from third-tier cities are the major populations who adopt mei in their user IDs for self-description and identity expression. This paper argues that the creative usage of mei gains popularity in the Chinese internet due to a humor effect. The marked word order switch and semantic misapplication combined to trigger incongruity and jocularity. This study has significance for research on Chinese cyber neologism. It may also lay a foundation for further studies on Chinese classifier change and Chinese internet communication.

Keywords: Chinese classifier, humor, neologism, semantic change

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Authors: Yogendra Sisodia

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Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: Gabriel Calvo, Carmen Armero, Luigi Spezia

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A basketball player is said to have a hot hand if his/her performance is better than expected in different periods of time. A way to deal with this phenomenon is to make use of latent variables, which can indicate whether the player is ‘on fire’ or not. This work aims to model the hot hand phenomenon through a Bayesian hidden Markov model (HMM) with two states (cold and hot) and two different probability of success depending on the corresponding hidden state. This task is illustrated through a comprehensive simulation study. The simulated data sets emulate the field goal attempts in an NBA season from different profile players. This model can be a powerful tool to assess the ‘streakiness’ of each player, and it provides information about the general performance of the players during the match. Finally, the Bayesian HMM allows computing the posterior probability of any type of streak.

Keywords: Bernoulli trials, field goals, latent variables, posterior distribution

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: G. Swati, D. Haranath

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Commercially available long afterglow /persistent phosphors are proprietary materials and hence the exact composition and phase responsible for their luminescent characteristics such as initial intensity and afterglow luminescence time are not known. Further to generate various emission colors, commercially available persistence phosphors are physically blended with fluorescent organic dyes such as rodhamine, kiton and methylene blue etc. Blending phosphors with organic dyes results into complete color coverage in visible spectra, however with time, such phosphors undergo thermal and photo-bleaching. This results in the loss of their true emission color. Hence, the current work is dedicated studies on inorganic based thermally and chemically stable primary color emitting nanophosphors namely SrAl2O4:Eu2+, Dy3+, (CaZn)TiO3:Pr3+, and Sr2MgSi2O7:Eu2+, Dy3+. SrAl2O4: Eu2+, Dy3+ phosphor exhibits a strong excitation in UV and visible region (280-470 nm) with a broad emission peak centered at 514 nm is the characteristic emission of parity allowed 4f65d1→4f7 transitions of Eu2+ (8S7/2→2D5/2). Sunlight excitable Sr2MgSi2O7:Eu2+,Dy3+ nanophosphors emits blue color (464 nm) with Commercial international de I’Eclairage (CIE) coordinates to be (0.15, 0.13) with a color purity of 74 % with afterglow time of > 5 hours for dark adapted human eyes. (CaZn)TiO3:Pr3+ phosphor system possess high color purity (98%) which emits intense, stable and narrow red emission at 612 nm due intra 4f transitions (1D2 → 3H4) with afterglow time of 0.5 hour. Unusual property of persistence luminescence of these nanophoshphors supersedes background effects without losing sensitive information these nanophosphors offer several advantages of visible light excitation, negligible substrate interference, high contrast bifurcation of ridge pattern, non-toxic nature revealing finger ridge details of the fingerprints. Both level 1 and level 2 features from a fingerprint can be studied which are useful for used classification, indexing, comparison and personal identification. facile methodology to extract high contrast fingerprints on non-porous and porous substrates using a chemically inert, visible light excitable, and nanosized phosphorescent label in the dark has been presented. The chemistry of non-covalent physisorption interaction between the long afterglow phosphor powder and sweat residue in fingerprints has been discussed in detail. Real-time fingerprint development on porous and non-porous substrates has also been performed. To conclude, apart from conventional dark vision applications, as prepared primary color emitting afterglow phosphors are potentional candidate for developing high contrast latent fingerprints.

Keywords: fingerprints, luminescence, persistent phosphors, rare earth

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Authors: Josef Voracek, Jan Sima

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The main purpose of our study is to state the basic types of fitness customers. This paper aims to create a specific customer typology in today’s fitness centres in the region of Prague. Our suggested typology of Prague fitness centres customers is based on answers to the questions: What are the customers like, what are their preferences, and what kinds of services do they use more often in Prague fitness centres? These are the main aspects of the presented typology. A survey was conducted on a sample of 1004 respondents from 48 fitness centres, which ran during May 2012. We used questionnaires and latent class analysis for the assessment and interpretation of data. Gender was especially the main filter criterion. In the population, there were 522 males and 482 females. Data were analysed using the LCA method. We identified 6 segments of typical customers, of which three are male and three are female. Each segment is influenced primarily by the age of customers, from which we can develop further characteristics, such as education, income, marital status, etc. Male segments use the main workout area above all, whilst female segments use a much wider range of services offered, for example, group exercises, personal training, and cardio theatres. LCA method was found to be the most suitable tool, because cluster analysis is very limited in the forms and numbers of variables and indicators. Models of 3 latent classes for each gender are optimal, as it is demonstrated by entropy indices and matrices of the likelihood of the membership to the classes. A probable weak point of the survey is the selection of fitness centres, because of the market in Prague is really specific.

Keywords: customer, fitness, latent class analysis, typology

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Authors: Mohammad Alqahtani, Eric Atwell

Abstract:

This paper reviews and classifies most of the important types of search techniques that have been applied on the holy Quran. Then, it addresses the limitations in these techniques. Additionally, this paper surveys most existing Quranic ontologies and what are their deficiencies. Finally, it explains a new search tool called: A semantic search tool for Al Quran based on Qur’anic ontologies. This tool will overcome all limitations in the existing Quranic search applications.

Keywords: holy Quran, natural language processing (NLP), semantic search, information retrieval (IR), ontology

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Authors: Farzaneh Badinlou, Reza Kormi-Nouri, Monika Knopf, Kamal Kharazi

Abstract:

Research into long-term memory has demonstrated that the richness of the knowledge base cues in memory tasks improves retrieval process, which in turn influences learning and memory performance. The present research investigated the idea that adding cues connected to knowledge can affect memory performance in the context of action memory in children. In action memory studies, participants are instructed to learn a series of verb–object phrases as verbal learning and experience-based learning (learning by doing and learning by observation). It is well established that executing action phrases is a more memorable way to learn than verbally repeating the phrases, a finding called enactment effect. In the present study, a total of 410 students from four grade groups—2nd, 4th, 6th, and 8th—participated in this study. During the study, participants listened to verbal action phrases (VTs), performed the phrases (SPTs: subject-performed tasks), and observed the experimenter perform the phrases (EPTs: experimenter-performed tasks). During the test phase, cued recall test was administered. Semantic relational cues (i.e., well-integrated vs. poorly integrated items) were manipulated in the present study. In that, the participants were presented two lists of action phrases with high semantic integration between verb and noun, e.g., “write with the pen” and with low semantic integration between verb and noun, e.g., “pick up the glass”. Results revealed that experience-based learning had a better results than verbal learning for both well-integrated and poorly integrated items, though manipulations of semantic relational cues can moderate the enactment effect. In addition, children of different grade groups outperformed for well- than poorly integrated items, in flavour of older children. The results were discussed in relation to the effect of knowledge-based information in facilitating retrieval process in children.

Keywords: action memory, enactment effect, knowledge-based cues, school-aged children, semantic relational cues

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Authors: Mohamed M. Diab, Fahmy E. Mohamed, Alaa Balbaa

Abstract:

Background: Myofascial pain syndrome a common type of non-articular musculoskeletal pain, is a condition associated with regional pain and muscle tenderness characterized by the presence of hypersensitive nodules. Objectives: the purpose of this study is to compare between the effects of manual progressive ischemic pressure versus the effect of post isometric facilitation in the treatment of Rhomboid latent myofascial trigger points. Methods: six patients had participated in this study. Patients divided into two groups. Group A treated by manual progressive ischemic pressure and traditional physical therapy program. Group B treated by post isometric facilitation and traditional physical therapy program. Treatment program was for 6 sessions over two week’s period. Result: Statistical analysis revealed that there is no significant difference in post treatment from pretreatment in pain severity (VAS) in myofascial trigger points with Rhomboid muscles) and Pain pressure threshold (PPT) for tenderness at both groups (A,B). Conclusion: ischemic pressure technique appear to be no more effective than post isometric facilitation in treatment of rhomboids latent myofacial trigger point.

Keywords: Rhmoiboid trigger point, myofacila trigger point, ischemic pressure, post isometric facilitation

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Authors: Shin-Hyung Cho, Dong-Kyu Kim, Seung-Young Kho

Abstract:

Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal relationship between severity of the crash and risk factors by adopting the latent factors of crashes. The aim of this study was to develop a structural equation or model based on truck-involved and non-truck-involved crashes, including five latent variables, i.e. a crash factor, environmental factor, road factor, driver’s factor, and severity factor. To clarify the unique characteristics of truck-involved crashes compared to non-truck-involved crashes, a confirmatory analysis method was used. To develop the model, we extracted crash data from 10,083 crashes on Korean freeways from 2008 through 2014. The results showed that the most significant variable affecting the severity of a crash is the crash factor, which can be expressed by the location, cause, and type of the crash. For non-truck-involved crashes, the crash and environment factors increase severity of the crash; conversely, the road and driver factors tend to reduce severity of the crash. For truck-involved crashes, the driver factor has a significant effect on severity of the crash although its effect is slightly less than the crash factor. The multiple group analysis employed to analyze the differences between the heterogeneous groups of drivers.

Keywords: crash severity, structural structural equation modeling (SEM), truck-involved crashes, multiple group analysis, crash on freeway

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Authors: Sushma Ghogale

Abstract:

With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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