Search results for: semantic differences

Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Mladen Hraste, Igor Jelaska, Ivan Granic

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The scope of this research is the identification and explanation of differences of three levels of water polo players in some parameters of effectiveness. The sample for this study was 132 matches of the Adriatic Water Polo League in the 2013/14 competition season. Using the Kruskal-Wallis test and multiple comparisons of mean ranks for all groups at the significance level of α=0, 05, the hypothesis that there are significant differences between groups of respondents in ten of the seventeen variables of effectiveness was confirmed. There is a reasonable possibility that the differences are caused by the degree of learned and implemented tactical knowledge, the degree of scoring ability and the best selection for certain roles in the team. The results of this study can be applied to selection of teams and players, for the selection of the appropriate match concept and for organizing training process.

Keywords: scoring abilities, selection, tactical knowledge, water polo effectiveness

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Mitja Dečman

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The ever-increasing presence and use of information and communication technology (ICT) influences the different social relationships of today's society. Gender differences are especially important from the viewpoint of modern society since ICT can either deepen the existing inequalities or diminish them. In a developed Western world, gender equality has been a well-focused area for decades in many parts of society including education, employment or politics and has led to a decrease in the inequality of women and men in these and other areas. The area of digital equality, or inequality for that matter, is one of the areas where gender differences still exist in many countries of the world. The research presented in this paper focuses on Slovenia, one of the smallest EU member states, being an average achiever in the area of e-society according to the many different European benchmarking indexes. On the other hand, Slovenia is working in an alignment with many European gender equality guidelines and showing good results. The results of our research are based on the analysis of survey data from 2014 to 2017 dealing with Slovenian citizens and their households and the use of ICT. Considering gender issues, the synthesis showed that cultural differences influence some measured ICT indicators but on the other hand the differences are low and only sometimes statistically significant.

Keywords: digital divide, gender inequality, Slovenia, e-society

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Authors: Gabriel Vusanimuzi Nkomo

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The majority of soils in communal areas of Zimbabwe are sandy and inherently infertile and sustainable cultivation is not feasible without addition of plant nutrients. Most farmers find it difficult to raise the capital required for investments in mineral fertilizer and find it cheaper to use low nutrition animal manure. An experiment was conducted to determine the effects of nitrokara biofertiliser on early growth, development and maize yield while also comparing nitrokara biofertiliser on availability of nitrogen and phosphorous in soil. The experiment was conducted at Africa University Farm. The experiment had six treatments (nitrokara +300kg/ha Compound D, nitrokara+ 300kg/ha Compound D(7N;14P;7K) + 75kg/ha Ammonium Nitrate(AN), nitrokara +300kg/ha Compound D +150kg AN, nitrokara +300kg/ha Compound D +225kg/ha AN, nitrokara +300kg/ha Compound D + 300 kg/ha AN and 0 nitrokara+300kg/ha Compound D +0 AN). Early maturing SC 403 maize (Zea mays) was inoculated with nitrokara and a compound mineral fertilizer at 300 kg/ha at planting while ammonium nitrate was applied at 45 days after planting. There were no significant differences (P > 0.05) on emergence % from 5days up to 10 days after planting using maize seed inoculated with nitrokara. Emergence percentage varied with the number of days. At 5 days the emergence % was 62% to a high of 97 % at 10 days after emergence among treatments. There were no significant differences (P > 0.05) on plant biomass on treatments 1 to 6 at 4 weeks after planting as well as at 8 weeks after planting. There were no significant differences among the treatments on the availability of nitrogen after 6 weeks (P > 0.05). However at 8 and 10 weeks after planting there were significant differences among treatments on nitrogen availability (P < 0.05). There were no significant differences among the treatments at week 6 after planting on soil pH (p > 0.05). However there were significant differences among treatments pH at weeks 9 and 12 (p < 0.05). There were significant differences among treatments on phosphorous availability at 6, 8 and 10 weeks after planting (p < 0.05). There were no significant differences among treatments on stem diameter at 3 and 6 weeks after planting (p > 0.05).However at 9 and 12 weeks after planting there were significant differences among treatments on stem diameter (p < 0.05).There were no significant differences among treatments on plant height from week 3 up to week 6 on plant height (P > 0.05).However there were significant differences among treatments at week 9 and 12 (p < 0.05). There were significant differences among treatments on days to early, 50% and 100% anthesis (P < 0.05). There were significant differences during early, 50% and 100% days to silking among the treatments (P < 0.05).Also there were significant differences during early, 50% and 100% days to silking among the treatments (P < 0.05).The study revealed that inoculation of nitrokara biofertiliser at planting with subsequent addition of ammonium nitrate has a positive effect on maize crop development and yield.

Keywords: nitrokara, biofertiliser, symbiotic, plant biomass, inoculated

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Mounia El Jaouhari, Mira Goral, Samir Diouny

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Introduction: Previous research has indicated that language impairments are recognized as a feature of many neurodegenerative disorders, including non-language-led dementia subtypes such as Alzheimer´s disease (AD). In this preliminary study, the focal aim is to quantify the semantic content of naming and connected speech samples of Moroccan patients diagnosed with AD using two tasks taken from the culturally adapted and validated Moroccan version of the Boston Diagnostic Aphasia Examination. Methods: Five individuals with AD and five neurologically healthy individuals matched for age, gender, and education will participate in the study. Participants with AD will be diagnosed on the basis of the Moroccan version of the Diagnostic and Statistial Manual of Mental Disorders (DSM-4) screening test, the Moroccan version of the Mini Mental State Examination (MMSE) test scores, and neuroimaging analyses. The participants will engage in two tasks taken from the MDAE-SF: 1) Picture description and 2) Naming. Expected findings: Consistent with previous studies conducted on English speaking AD patients, we expect to find significant word production and retrieval impairments in AD patients in all measures. Moreover, we expect to find category fluency impairments that further endorse semantic breakdown accounts. In sum, not only will the findings of the current study shed more light on the locus of word retrieval impairments noted in AD, but also reflect the nature of Arabic morphology. In addition, the error patterns are expected to be similar to those found in previous AD studies in other languages.

Keywords: alzheimer's disease, anomia, connected speech, semantic impairments, moroccan arabic

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Authors: Natalia Kajka, Agnieszka Kulik

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The aim of the study is to present the results regarding differences in perception of cognitive progress of children with Attention Deficit Hyperactivity Disorder (ADHD) by adults and children themselves. The experiment was attended by 45 children with ADHD, their parents and teachers. The children attended the 3-month metacognitive training. Both children and adults were examined before and after joining this project. In order to show significant differences between the first and second measurement of the test, non-parametric Wilcoxon tests were performed. The analysis showed statistically significant differences in the change of cognitive functioning in children with ADHD participating in metacognitive training, this was also confirmed by the results of the parents' research. There were no significant differences in the teachers' assessment of these children.

Keywords: ADHD, executive function, Golem effect metacognitive training

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Authors: Massra M. Sabeima, Myriam lamolle, Mohamedade Farouk Nanne

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Adapting the learning resources of an e-learning system to the characteristics of the learners is an important aspect to consider when designing an adaptive e-learning system. However, this adaptation is not a simple process; it requires the extraction, analysis, and modeling of user information. This implies a good representation of the user's profile, which is the backbone of the adaptation process. Moreover, during the e-learning process, collaboration with similar users (same geographic province or knowledge context) is important. Productive collaboration motivates users to continue or not abandon the course and increases the assimilation of learning objects. The contribution of this work is the following: we propose an adaptive e-learning semantic platform to recommend learning resources to learners, using ontology to model the user profile and the course content, furthermore an implementation of a multi-agent system able to progressively generate the learning graph (taking into account the user's progress, and the changes that occur) for each user during the learning process, and to synchronize the users who collaborate on a learning object.

Keywords: adaptative learning, collaboration, multi-agent, ontology

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Authors: Shu-Feng Chang

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During the past years, gender issues have been discussed in many fields. It causes such differences not only in physical field but also in mental field. Gender differences also appear in our daily life, especially in the communication of spoken language. This statement was proved in the descriptions of color. However, the research about describing shape was fewer. The purpose of the study was to determine the description of the shape was different or alike due to gender. If it was different, this difference was dissimilar or as the same as the conclusion of color. Data were collected on the shape descriptions by 15 female and 15male participants in describing five pictures. As a result, it was really different for the descriptions of shape due to gender factor. The findings of shape descriptions were almost as the same as color naming with gender factor.

Keywords: gender, naming, shape, sociolinguistics

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Authors: Joana Portia Sakyi

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The uniqueness of auxiliary verbs and their contribution to grammar as constituents, which act as preverbs to supply additional grammatical or functional meanings to clauses, are well established. Functionally, they relate clauses to tense, aspect, mood, voice, emphasis, and modality, along with the main verbs conveying the appropriate lexical content. There has been an issue in Akan grammar vis-à-vis the status of auxiliary verbs, in terms of whether Akan has auxiliaries or not and even which forms are to be regarded as auxiliaries. We investigate the syntactic, semantic, and pragmatic components of expressions and claim that Akan has auxiliary verbs that contribute the functional or grammatical meaning of modality, tense/aspect, etc., to clauses they occur in. Essentially, we use a self-created corpus data to consider the affix bέ- ‘may’, ‘must’, ‘should’; the form tùmí ‘can’, ‘be able to’; mà ‘to let’, ‘to allow’, ‘to permit’, ‘to make’, or ‘to cause’ someone to do something; the multi-word forms ὲsὲ sέ ‘must’, ‘should’ or ‘have to’ and ètwà sέ ‘must’, ‘should’ or ‘have to’, and assert that they are legitimate modal auxiliaries conveying epistemic, deontic, and dynamic modalities, as well as other meanings in the language.

Keywords: Akan, modality, modal auxiliaries, semantics

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Authors: Chuluundorj Begz

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This paper presents the dynamic, psycho-cognitive approach to study of human verbal thinking on the basis of typologically different languages /as a Mongolian, English and Russian/. Topological equivalence in verbal communication serves as a basis of Universality of mental structures and therefore deep structures. Mechanism of verbal thinking consisted at the deep level of basic concepts, rules for integration and classification, neural networks of vocabulary. In neuro cognitive study of language, neural architecture and neuro psychological mechanism of verbal cognition are basis of a vector-based modeling. Verbal perception and interpretation of the infinite set of meanings and propositions in mental continuum can be modeled by applying tensor methods. Euclidean and non-Euclidean spaces are applied for a description of human semantic vocabulary and high order structures.

Keywords: Euclidean spaces, isomorphism and homomorphism, mental lexicon, mental mapping, semantic memory, verbal cognition, vector space

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Authors: Shuo Liu, Dan Liu

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In the field of named entity recognition, most of the research has been conducted around simple entities. However, for nested named entities, which still contain entities within entities, it has been difficult to identify them accurately due to their boundary ambiguity. In this paper, a hierarchical recognition model is constructed based on the grammatical structure and semantic features of Chinese text for boundary calculation based on lexical features. The analysis is carried out at different levels in terms of granularity, semantics, and lexicality, respectively, avoiding repetitive work to reduce computational effort and using the semantic features of words to calculate the boundaries of entities to improve the accuracy of the recognition work. The results of the experiments carried out on web-based microblogging data show that the model achieves an accuracy of 86.33% and an F1 value of 89.27% in recognizing nested named entities, making up for the shortcomings of some previous recognition models and improving the efficiency of recognition of nested named entities.

Keywords: coarse-grained, nested named entity, Chinese natural language processing, word embedding, T-SNE dimensionality reduction algorithm

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Authors: Zaqiatul Mardiah, Afdol Tharik Wastono, Abdul Muta'ali

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In general, the Arabic preposition ‘ala encodes the sense of UP-DOWN schema. However, the use of the preposition ‘ala can has many extended schemas that still have relation to its primary sense. In this paper, we show how the framework of cognitive linguistics (CL) based on image schemas can be applied to analyze the spatial semantic of the use of preposition ‘ala in the horizontal and vertical axes. The preposition ‘ala is usually used in the locative sense in which one physical entity is UP-DOWN relation to another physical entity. In spite of that, the cognitive analysis of ‘ala justifies the use of this preposition in many situations to seemingly encode non-up down-related spatial relations, and non-physical relation. This uncovers some of the unsolved issues concerning prepositions in general and the Arabic prepositions in particular the use of ‘ala as a sample. Using the Arabic corpus data, we reveal that in many cases and situations, the use of ‘ala is extended to depict relations other than the ones where the Trajector (TR) is actually in up-down relation to the Landmark (LM). The instances analyzed in this paper show that ‘ala encodes not only the spatial relations in which the TR and the LM are horizontally or vertically related to each other, but also non-spatial relations.

Keywords: image schema, preposition, spatial semantic, up-down relation

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Authors: Jamie A. Prowse Turner, Jamie I. D. Campbell, Valerie A. Thompson

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The objective of the current research was to use a dual-process theory framework to explain these age-related differences in reasoning. Seventy-two older (M = 80.0 years) and 72 younger (M = 24.6 years) adults were given a variety of reasoning tests (i.e., a syllogistic task, base rate task, the Cognitive Reflection Test, and a perspective manipulation), as well as independent tests of capacity (working memory, processing speed, and inhibition), thinking styles, and metacognitive ability, to account for these age-related differences. It was revealed that age-related differences were limited to problems that required Type 2 processing and were related to differences in cognitive capacity, individual difference factors, and strategy choice. Furthermore, older adults’ performance can be improved by reasoning from another’s’ perspective and cannot, at this time, be explained by metacognitive differences between young and older adults. All of these findings fit well within a dual-process theory of reasoning, which provides an integrative framework accounting for previous findings and the findings presented in the current manuscript.

Keywords: aging, dual-process theory, performance, reasoning ability

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Authors: Feddaoui Amina

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This paper aimed to help researchers and international companies to the differences and similarities between IFRS (International financial reporting standards) and UK GAAP or UK accounting principles, and to the accounting changes between standard setting of the International Accounting Standards Board and the Accounting Standards Board in United Kingdom. We will use in this study statistical methods to calculate similarities and difference frequencies between the UK standards and IFRS standards, according to the PricewaterhouseCoopers report in 2005. We will use the one simple test to confirm or refuse our hypothesis. In conclusion, we found that the gap between UK GAAP and IFRS is small.

Keywords: accounting, UK GAAP, IFRS, similarities, differences

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Authors: Bianca Suárez Puerta

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Multimodal discourse refers to a method of study the media continuum between reality, screens as a device, audience, author, and media as a production from the audience. For this study we used semantic differential, a method proposed in the sixties by Osgood, Suci and Tannenbaum, starts from the assumption that under each particular way of perceiving the world, in each singular idea, there is a common cultural meaning that organizes experiences. In relation to these shared symbolic dimension, this method has had significant results, as it focuses on breaking down the meaning of certain significant acts into series of statements that place the subjects in front of some concepts. In Colombia, in 2016, a tool was designed to measure the meaning of a multimodal production, specially the acts of sense of transmedia productions that managed to receive funds from the Ministry of ICT of Colombia, and also, to analyze predictable patterns that can be found in calls and funds aimed at the production of culture in Colombia, in the context of the peace agreement, as a request for expressions from a hegemonic place, seeking to impose a worldview.

Keywords: semantic differential, semiotics, transmedia, critical analysis of discourse

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Zou Yihui

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The integration of the traditional landscape of the ancient city and the poet's emotions and symbolization into ancient poetry is the unique cultural gene and spiritual core of the historical city, and re-understanding the historical landscape pattern from the poetry is conducive to continuing the historical city context and improving the current situation of the gradual decline of the poetry of the modern historical urban landscape. Starting from Tang poetry uses semantic analysis methods、combined with text mining technology, entry mining, word frequency analysis, and cluster analysis of the landscape information of Tang Chang'an City were carried out, and the method framework for analyzing the urban landscape form based on poetry text was constructed. Nearly 160 poems describing the landscape of Tang Chang'an City were screened, and the poetic landscape characteristics of Tang Chang'an City were sorted out locally in order to combine with modern urban spatial development to continue the urban spatial context.

Keywords: Tang Chang'an City, poetic texts, semantic analysis, historical landscape

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Authors: Mikhail Zarechnev, Bora I. Kumova

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A syllogism is a deductive inference scheme used to derive a conclusion from a set of premises. In a categorical syllogisms, there are only two premises and every premise and conclusion is given in form of a quantified relationship between two objects. The different order of objects in premises give classification known as figures. We have shown that the ordered combinations of 3 generalized quantifiers with certain figure provide in total of 108 syllogistic moods which can be considered as different inference rules. The classical syllogistic system allows to model human thought and reasoning with syllogistic structures always attracted the attention of cognitive scientists. Since automated reasoning is considered as part of learning subsystem of AI agents, syllogistic system can be applied for this approach. Another application of syllogistic system is related to inference mechanisms on the Semantic Web applications. In this paper we proposed the mathematical model and algorithm for syllogistic reasoning. Also the model of iterative syllogistic reasoning in case of continuous flows of incoming data based on case–based reasoning and possible applications of proposed system were discussed.

Keywords: categorical syllogism, case-based reasoning, cognitive architecture, inference on the semantic web, syllogistic reasoning

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Authors: Hamideh Marefat, Eskandar Samadi

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This study examined the effect of semantics on processing ambiguous sentences containing Relative Clauses (RCs) preceded by a complex Determiner Phrase (DP) by Persian-speaking learners of L2 English with different proficiency and Working Memory Capacities (WMCs). The semantic relationship studied was one between the subject of the main clause and one of the DPs in the complex DP to see if, as predicted by Spreading Activation Model, priming one of the DPs through this semantic manipulation affects the L2ers’ preference. The results of a task using Rapid Serial Visual Processing (time-controlled paradigm) showed that manipulation of the relationship between the subject of the main clause and one of the DPs in the complex DP preceding RC has no effect on the choice of the antecedent; rather, the L2ers' processing is guided by the phrase structure information. Moreover, while proficiency did not have any effect on the participants’ preferences, WMC brought about a difference in their preferences, with a DP1 preference by those with a low WMC. This finding supports the chunking hypothesis and the predicate proximity principle, which is the strategy also used by monolingual Persian speakers.

Keywords: semantics, relative clause processing, ambiguity resolution, proficiency, working memory capacity

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Authors: Hui Min Kong, Rajalakshmi A/P Ganesan

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The present study was conducted to investigate the effect of gender differences in mate selection in a private university. Mate selection is an important process and decision to the people around the world, especially for single people. The future partner we have chosen could be our lifetime friend, supporter, and lover. Mate selection is important to us, but we have never fully understood the evolution of gender differences in mate selection. Besides, there was an insufficient empirical finding of gender differences in mate selection in Malaysia. Hence, the research would allow us to understand our feelings and thoughts about our future partners. The research null hypotheses have stated that there was no significant difference on 18 mate selections characteristics between males and females. A quantitative method was performed to test the hypotheses through independent t-test. There was a total of 373 heterosexual participants with the age range of 18 to 35 in the study. The instrument used was Factors in choosing a mate developed by Buss and Barnes (1986). Results indicated that females (M= 26.69) were found to be highly valued on refinement and neatness, good financial prospect, dependable character, emotional stability and maturity, desire for home and children, favorable social status or rating, similar religious background, ambition and industriousness, mutual attraction, good health and education and intelligence than males (M= 23.25). These results demonstrated that there were 61.11% significant gender differences in mate selections characteristics. Findings of this research have highlighted the importance of human mate selections in Malaysia. Further research is needed to identify the factors that could have a possible moderating effect of gender differences in mate selection.

Keywords: gender differences, mate selections, evolution, future partner

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: Gayani Ranawake, Kate Wilson

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Underrepresentation of women in academia, particularly in science, has been discussed by many scholars for decades. The causes of this underrepresentation are debated to this day. Publication is an important aspect of success in academia, and publication and citation rates are significant metrics in performance review, promotion, and employment. It has been established that men’s and women’s language use in general, both spoken and written, is different. However, no one, to our knowledge, has looked at whether men’s and women’s writing in science is different. If there are significant differences in the writing of men and women, then these differences may affect women’s ability to succeed in science. This study is part of a larger project to explore whether differences can be recognized in the academic science writing of men and women. Mono authored articles from high ranking physics, biology and psychology journals by men and women authors were compared in terms of readability statistics. In particular, the abstract and introduction sections were compared, as these are the first sections encountered by a reviewer, and so may have an important effect on their impression of the work. The Flesch Reading Ease, the percentage of passive sentences and the Flesch-Kincaid Reading Grade Level were calculated for each section of each article, along with counts of numbers of sentences, words per sentence and sentences per paragraph. Significance of differences was tested using the Behrens statistic. It was found that for both physics and biology papers there were no significant differences in the complexity or verbosity of the writing of men and women authors. However, there was a significant difference between the two disciplines, with physics articles being generally more readable (higher readability score) while also more passive (higher number of passive sentences). In contrast, the psychology articles showed a difference between men and women authors which may be significant. The average readability for introductions in women’s articles was 28 which was higher than for men’s articles, which was 19 (higher values indicate more readable). Women’s articles in psychology also had a greater proportion of passive sentences. It can be concluded that, at least in the more traditional sciences, men and women have adopted similar ways of writing, and that disciplinary differences are greater than gender differences. This may not be the case in psychology, which many consider to be more closely aligned with the humanities. Whether the lack of differences is because women have adapted to a masculine way of writing, or whether the genre itself is gender neutral needs further investigation.

Keywords: academic writing, gender differences, readability, science

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Sally Selden, Jyoti Aggarwal

Abstract:

The research on generational expectations about leadership is developing, but little scholarship exists on this topic for public sector organizations. Given the size of the federal workforce, this research study fills an important gap in the knowledge base and will inform public organizations how to approach managing and leading a multigenerational workforce. The research objectives of this study are to explore leadership preferences and motivation within generations and to determine whether these qualities differ by type of federal agency (e.g., law enforcement, human services, etc.). This paper will review the research on generational differences, expectations, and leadership with a focus on studies of public organizations. Using hierarchical linear modeling (HLM), this study will examine how leadership and motivation vary by generation in the federal government workforce, controlling for other demographic characteristics. The study will also examine whether generational differences impact satisfaction and performance. The study will utilize the 2019 Federal Employee Viewpoint Survey.

Keywords: multigenerational workforce, leadership, generational differences, federal workforce

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Authors: Francis Alterado, Jennifer Enriquez

Abstract:

Semantic Based Analysis in Complaint Management System with Analytics is an enhanced tool of providing complaints by the clients as well as a mechanism for Palawan Polytechnic College to gather, process, and monitor status of these complaints. The study has a mobile application that serves as a remote facility of communication between the students and the school management on the issues encountered by the student and the solution of every complaint received. In processing the complaints, text mining and clustering algorithms were utilized. Every module of the systems was tested and based on the results; these are 100% free from error before integration was done. A system testing was also done by checking the expected functionality of the system which was 100% functional. The system was tested by 10 students by forwarding complaints to 10 departments. Based on results, the students were able to submit complaints, the system was able to process accordingly by identifying to which department the complaints are intended, and the concerned department was able to give feedback on the complaint received to the student. With this, the system gained 4.7 rating which means Excellent.

Keywords: technology adoption, emerging technology, issues challenges, algorithm, text mining, mobile technology

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Authors: Wujian Han

Abstract:

In the process of human interaction and communication, speech act verbs are considered to be the most active component and the main means for information transmission, and are also taken as an indication of the structure of linguistic behavior. The theoretical value and practical significance of such everyday built-in metalanguage have long been recognized. This paper, which is part of a bigger study, is aimed to provide useful insights for a more precise and systematic application to speech act verbs translation between English and Chinese, especially with regard to the degree to which generic integrity is maintained in the practice of translation of legal documents. In this study, the corpus, i.e. Chinese legal texts and their English translations, English legal texts, ordinary Chinese texts, and ordinary English texts, serve as a testing ground for examining contrastively the usage of English and Chinese directive speech act verbs in legal genre. The scope of this paper is relatively wide and essentially covers all directive speech act verbs which are used in ordinary English and Chinese, such as order, command, request, prohibit, threat, advice, warn and permit. The researcher, by combining the corpus methodology with a contrastive perspective, explored a range of characteristics of English and Chinese directive speech act verbs including their semantic, syntactic and pragmatic features, and then contrasted them in a structured way. It has been found that there are similarities between English and Chinese directive speech act verbs in legal genre, such as similar semantic components between English speech act verbs and their translation equivalents in Chinese, formal and accurate usage of English and Chinese directive speech act verbs in legal contexts. But notable differences have been identified in areas of difference between their usage in the original Chinese and English legal texts such as valency patterns and frequency of occurrences. For example, the subjects of some directive speech act verbs are very frequently omitted in Chinese legal texts, but this is not the case in English legal texts. One of the practicable methods to achieve adequacy and conciseness in speech act verb translation from Chinese into English in legal genre is to repeat the subjects or the message with discrepancy, and vice versa. In addition, translation effects such as overuse and underuse of certain directive speech act verbs are also found in the translated English texts compared to the original English texts. Legal texts constitute a particularly valuable material for speech act verb study. Building up such a contrastive picture of the Chinese and English speech act verbs in legal language would yield results of value and interest to legal translators and students of language for legal purposes and have practical application to legal translation between English and Chinese.

Keywords: contrastive analysis, corpus-based, directive speech act verbs, legal texts, translation between English and Chinese

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Authors: Samanta Kietytė

Abstract:

The object of this study is the verbal derivatives stemming from the Lithuanian prefix per-. The prefix under examination can be classified as prepositional, having descended from the preposition per, thereby sharing the same prototypical meaning – denoting movement OVER. These frequently co-occur within sentences (1). The aim of this paper is to conduct a semantic analysis of the prefix per- and to propose a possible radial network of its meanings. In essence, the aim is to identify the interrelationships existing between its meanings. 1) Jis peršoko per tvorą/ 3SG.NOM.M jump.PST.3 over fence.ACC.SG. /ʻHe jumped over the fenceʼ. The foundation of this work lies in the methodological and theoretical framework of cognitive linguistics. The prototypical meaning of prefixes consistently embodies spatial dimensions that can be described through image schemas. This entails the identification of the trajectory, the landmark, and the relation between them in the situation described by the prefixed verb. The meanings of linguistic units are not perceived as arbitrary, but rather, they are interconnected through semantic motivation. According to this perspective, a singular meaning within linguistic units is considered as prototypical, while additional meanings are descended (not necessarily directly) from it. For example, one of the per- meanings TRANSFER (2) is derived from the prototypical meaning OVER. 2) Prašau persiųsti vadovo laišką man./ Ask.PRS.1 forward.INF manager.GEN.SG email.ACC.SG 1.SG.DAT/ ʻPlease forward the manager‘s email to meʼ. Certain semantic relations are explained by the conceptual metaphor and metonymy theory. For instances, when prefixed verb has a meaning WIN (3) it is related to the prototypical meaning. In this case, the prefixed verb describes situations of winning in various ways. In the prototypical meaning, the trajector moves higher than the landmark, and winning is metaphorically perceived as being higher. 3) Sūnus peraugo tėvą./ Son.NOM.SG outgrow.PST.3 father.ACC.SG/ ʻThe son has outgrown the fatherʼ. The data utilized for this study was collected from the 2014 grammatically annotated text "Lithuanian Web (LithuanianWaC v2)", consisting of 63,645,700 words. Given that the corpus is grammatically lemmatized, the list of the 793 items was obtained using the wordlist function and specifying that verbs starting with per were searched. The list included not only prefixed verbs but also other verbs whose roots have the same letter sequences as prefixes. Also, words with misspellings, without diacritical marks, and words listed for lemmatization errors were rejected, and a total of 475 derivatives were left for further analysis. The semantic analysis revealed that there are 12 distinct meanings of the prefix per-. The spatial meanings were extracted by determining what a trajector is, what a landmark is, and what the relation between them is. The connection between non-spatial meanings and spatial ones occurs through semantic motivation established by identifying elements that correspond to the trajector and landmark. The analysis reveals that there are no strict boundaries among these meanings, instead showing a continuum that encompasses a central core and a peripheral association with their internal structure, i.e., some derivatives are more prototypical of a particular meaning than others.

Keywords: word-formation, cognitive semantics, metaphor, radial networks, prototype theory, prefix

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

Abstract:

We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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