Search results for: inference on the semantic web

Authors: Ben Khayut, Lina Fabri, Maya Avikhana

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The models of the modern Artificial Narrow Intelligence (ANI) cannot: a) independently and continuously function without of human intelligence, used for retraining and reprogramming the ANI’s models, and b) think, understand, be conscious, cognize, infer, and more in state of Uncertainty, and changes in situations, and environmental objects. To eliminate these shortcomings and build a new generation of Artificial Intelligence systems, the paper proposes a Conception, Model, and Method of Computational Psycholinguistic Cognitive Situational-Fuzzy Self-Controlled Brain and Mind System (CPCSFSCBMSUU) using a neural network as its computational memory, operating under uncertainty, and activating its functions by perception, identification of real objects, fuzzy situational control, forming images of these objects, modeling their psychological, linguistic, cognitive, and neural values of properties and features, the meanings of which are identified, interpreted, generated, and formed taking into account the identified subject area, using the data, information, knowledge, and images, accumulated in the Memory. The functioning of the CPCSFSCBMSUU is carried out by its subsystems of the: fuzzy situational control of all processes, computational perception, identifying of reactions and actions, Psycholinguistic Cognitive Fuzzy Logical Inference, Decision making, Reasoning, Systems Thinking, Planning, Awareness, Consciousness, Cognition, Intuition, Wisdom, analysis and processing of the psycholinguistic, subject, visual, signal, sound and other objects, accumulation and using the data, information and knowledge in the Memory, communication, and interaction with other computing systems, robots and humans in order of solving the joint tasks. To investigate the functional processes of the proposed system, the principles of Situational Control, Fuzzy Logic, Psycholinguistics, Informatics, and modern possibilities of Data Science were applied. The proposed self-controlled System of Brain and Mind is oriented on use as a plug-in in multilingual subject Applications.

Keywords: computational brain, mind, psycholinguistic, system, under uncertainty

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Authors: Tianyue Wan

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Building a child-friendly city (CFC) contributes to improving the quality of urbanization. It also forms a local system committed to fulfilling children's rights and development. Yet, the work related to CFC is still at the initial stage in China. Therefore, taking Wuhan, the most populous city in central China, as the pilot city would offer some reference for other cities. Based on the analysis of theories and practice examples, this study puts forward the challenges of building a child-friendly city under the particularity of China's national conditions. To handle these challenges, this study uses four methods to collect status data: literature research, site observation, research inquiry, and semantic differential (SD). And it adopts three data analysis methods: case analysis, geographic information system (GIS) analysis, and analytic hierarchy process (AHP) method. Through data analysis, this study identifies the evaluation system and appraises the current situation of Wuhan. According to the status of Wuhan's child-friendly city, this study proposes three strategies: 1) construct the evaluation system; 2) establish a child-friendly space system integrating 'point-line-surface'; 3) build a digitalized service platform. At the same time, this study suggests building a long-term mechanism for children's participation and multi-subject supervision from laws, medical treatment, education, safety protection, social welfare, and other aspects. Finally, some conclusions of strategies about CFC are tried to be drawn to promote the highest quality of life for all citizens in Wuhan.

Keywords: action plan, child friendly city, construction strategy, urban space

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Marie Komorna, Katerina Hadkova

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One reason why hearing disabilities as compared to other disabilities are considered to be less serious, is the belief that deaf and hard of hearing persons can read and write without problems and can therefore fairly easily compensate for problems related to their limited ability to hear sound. However in reality this is not the case, especially as regards written Czech, deaf persons are often not able to communicate their message clearly to its recipients. Their inability to communicate fully in written language is one of the most severe problems facing a number of deaf persons, a problem which they face and which makes it difficult for them to function in a sound-based environment. Despite this fact, this issue is one which has been given only a minimum of attention in the Czech Republic. That is why we decided to focus our research on this issue, specifically targeting written communication of deaf pupils in primary and secondary schools. The paper summarizes the background and objectives of this research. The written work of deaf respondents was obtained in response to a narrative based on a series of images which depicted a continuous storyline. Based on an analysis of the obtained written work we tried to describe the specifics of the narrative abilities of the deaf authors of these texts. We also analyzed other aspects and specific traits of text written by deaf authors at a phonetic-phonological, lexical-semantic, morphological and syntactic, respectively pragmatic level. Based on the results of the project it will be possible to increase knowledge of the communication abilities of deaf persons in written Czech. The obtained data may be used during future research and for teaching purposes and/or education concepts for teaching Czech to deaf pupils.

Keywords: communication competence, deaf, narrative, written texts

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Authors: Hui Shi

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This article examines the online popularity of Chinese neologism simida, which is a loanword derived from Korean declarative sentence-final suffix seumnida. Facilitated by corpus data obtained from Weibo, the Chinese counterpart of Twitter, this study analyzes the morphological and syntactical processes behind simida’s coinage, as well as the causes of its prevalence on Chinese social media. The findings show that simida is used by Weibo bloggers in two manners: (1) as an alternative word of 'Korea' and 'Korean'; (2) as a redundant sentence-final particle which adds a Korean-like speech style to a statement. Additionally, Weibo user profile analysis further reveals demographical distribution patterns concerning this neologism and highlights young Weibo users in the third-tier cities as the leading adopters of simida. These results are accounted for under the theoretical framework of social indexicality, especially how variations generate style in the indexical field. This article argues that the creation of such an ethnically-targeted neologism is a linguistic demonstration of Chinese netizen’s two-sided attitudes toward the previously heated Korean-wave. The exotic suffix seumnida is borrowed to Chinese as simida due to its high-frequency in Korean cultural exports. Therefore, it gradually becomes a replacement of Korea-related lexical items due to markedness, regardless of semantic prosody. Its innovative implantation to Chinese syntax, on the other hand, reflects Chinese netizens’ active manipulation of language for their online identity building. This study has implications for research on the linguistic construction of identity and style and lays the groundwork for linguistic creativity in the Chinese new media.

Keywords: Chinese neologism, loanword, humor, new media

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Authors: Kenneth D. Aiello, M. Simeone, Manfred Laubichler

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It is increasingly difficult to analyze emerging scientific fields as contemporary scientific fields are more dynamic, their boundaries are more porous, and the relational possibilities have increased due to Big Data and new information sources. In biomedicine, where funding, medical categories, and medical jurisdiction are determined by distinct boundaries on biomedical research fields and definitions of concepts, ambiguity persists between the microbiome, metabolome, and metagenome research fields. This ambiguity continues despite efforts by institutions and organizations to establish parameters on the core concepts and research discourses. Further, the explosive growth of microbiome, metabolome, and metagenomic research has led to unknown variation and covariation making application of findings across subfields or coming to a consensus difficult. This study explores the evolution and variation of knowledge within the microbiome, metabolome, and metagenome research fields related to ambiguous scholarly language and commensurable theoretical frameworks via a semantic analysis of key concepts and narratives. A computational historical framework of cultural evolution and large-scale publication data highlight the boundaries and overlaps between the competing scientific discourses surrounding the three research areas. The results of this study highlight how discourse and language distribute power within scholarly and scientific networks, specifically the power to set and define norms, central questions, methods, and knowledge.

Keywords: biomedicine, conceptual change, history of science, philosophy of science, science of science, sociolinguistics, sociology of knowledge

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Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: Ryoo Hye Jin Agnes

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This study aims to investigate the efficacy of clickers in fostering L2 class interaction. In an L2 classroom, active learner-to-learner interactions and learner-to-teacher interactions play an important role in language acquisition. In light of this, introducing learning tools that promote such interactions would benefit L2 classroom by fostering interaction. This is because the anonymity of clickers allows learners to express their needs without the social risks associated with speaking up in the class. clickers therefore efficiently help learners express their level of understanding during the process of learning itself. This allows for an evaluative feedback loop where both learners and teachers understand the level of progress of the learners, better enabling classrooms to adapt to the learners’ needs. Eventually this tool promotes participation from learners. This, in turn, is believed to be effective in fostering classroom interaction, allowing learning to take place in a more comfortable yet vibrant way. This study is finalized by presenting the result of an experiment conducted to verify the effectiveness of this approach when teaching pragmatic aspect of Korean expressions with similar semantic functions. The learning achievement of learners in the experimental group was found higher than the learners’ in a control group. A survey was distributed to the learners, questioning them regarding the efficacy of clickers, and how it contributed to their learning in areas such as motivation, self-assessment, increasing participation, as well as giving feedback to teachers. Analyzing the data collected from the questionnaire given to the learners, the study presented data suggesting that this approach increased the scope of interactivity in the classroom, thus not only increasing participation but enhancing the type of classroom participation among learners. This participation in turn led to a marked improvement in their communicative abilities.

Keywords: second language acquisition, interaction, clickers, learner response system, output from learners, learner’s cognitive process

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Authors: Zelda Jansen Van Rensburg

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Purpose: This study investigates the perceived disparities between Quantity Surveying and Commercial Management in the construction industry, questioning if these differences are substantive or merely semantic. It aims to challenge the conventional notion of Commercial Managers’ superiority by critically evaluating QS and CM roles, exploring CM integration possibilities, examining qualifications for aspiring Commercial Managers, assessing regulatory frameworks, and considering terminology redefinition for global QS professional enhancement. Design: Utilizing mixed methods like literature reviews, surveys, interviews, and document analyses, this research examines the QS-CM relationship. Insights from industry professionals, academics, and regulatory bodies inform the investigation into changing QS roles. Findings: Empirical data highlight evolving roles, showcasing areas of convergence and divergence between QSs and CM. Potential CM integration into QS practice and qualifications for aspiring Commercial Managers are identified. Limitations/Implications: Limitations include potential bias in self-reported data and findings. Nevertheless, the research informs future practices and educational approaches in QS and CM, reflecting the changing roles and responsibilities of Quantity Surveyors. Practical Implications: Findings inform industry practitioners, educators, and regulators, stressing the need to adapt to changing QS roles and integrate CM principles where applicable. Value to the Conference Theme: Aligned with ‘Evolving roles and responsibilities of Quantity Surveyors,’ this research offers insights crucial for understanding the changing dynamics within the QS profession and informs strategies to navigate these shifts effectively.

Keywords: quantity surveying, commercial management, cost engineering, quantity survey

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Authors: Chan Kwong Tung

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Since Baddeley and Hitch’s seminal research in 1974 on working memory (WM), this topic has been of great interest to language educators. Although there are some variations in the definitions of WM, recent findings in WM have contributed vastly to our understanding of language learning, especially its effects on second language acquisition (SLA). For example, the phonological component of WM (PWM) and the executive component of WM (EWM) have been found to be positively correlated with language learning. This paper discusses two general, yet highly relevant WM findings that could directly affect the effectiveness of Chinese Language (CL) vocabulary teaching and learning, as well as the quality of its assessment. First, PWM is found to be critical for the long-term learning of phonological forms of new words. Second, EWM is heavily involved in interpreting the semantic characteristics of new words, which consequently affects the quality of learners’ reading comprehension. These two ideas are hardly discussed in the Chinese literature, both conceptual and empirical. While past vocabulary acquisition studies have mainly focused on the cognitive-processing approach, active processing, ‘elaborate processing’ (or lexical elaboration) and other effective learning tasks and strategies, it is high time to balance the spotlight to the WM (particularly PWM and EWM) to ensure an optimum control on the teaching and learning effectiveness of such approaches, as well as the validity of this language assessment. Given the unique phonological, orthographical and morphological properties of the CL, this discussion will shed some light on the vocabulary acquisition of this Sino-Tibetan language family member. Together, these two WM concepts could have crucial implications for the design, development, and planning of vocabularies and ultimately reading comprehension teaching and assessment in language education. Hopefully, this will raise an awareness and trigger a dialogue about the meaning of these findings for future language teaching, learning, and assessment.

Keywords: Chinese Language, working memory, vocabulary assessment, vocabulary teaching

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Authors: Balasooriya B. M. C. M., Sujeeva N., Thowfeek Z., Siddiqa Omo, Liyanagunawardana J. E., Jayawardana Saiu, Manathunga S. S., Katulanda G. W.

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Introduction and objectives: Adrenal venous sampling (AVS) is the gold standard to discriminate unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and only performed in a limited number of centers worldwide. To the best of our knowledge, Except for one study conducted in India, no other research studies on this area have been conducted in South Asia. This study aimed to evaluate the effectiveness of AVS in the management of primary aldosteronism. Methods: A total of 32 patients who underwent AVS at the National Hospital of Sri Lanka from April 2021 to April 2023 were enrolled. Demographic, clinical and laboratory data were obtained retrospectively. A procedure was considered successful when adequate cannulation of both adrenal veins was demonstrated. Cortisol gradient across the adrenal vein (AV) and the peripheral vein was used to establish the success of venous cannulation. Lateralization was determined by the aldosterone gradient between the two sides. Continuous and categorical variables were summarized with mean, SD, and proportions, respectively. The mean and standard deviation of the contralateral suppression index (CSI) were estimated with an intercept-only Bayesian inference model. Results: Of the 32 patients, the average age was 52.47 +26.14 and 19 (59.4%) were males. Both AVs were successfully cannulated in 12 (37.5%). Among them, lateralization was demonstrated in 11(91.7%), and one was diagnosed as a bilateral disease. There were no total failures. Right AV cannulation was unsuccessful in 18 (56.25%), of which lateralization was demonstrated in 9 (50%), and others were inconclusive. Left AV cannulation was unsuccessful only in 2 (6.25%); one was lateralized, and the other remained inconclusive. The estimated mean of the CSI was 0.33 (89% credible interval 0.11-0.86). Seven patients underwent unilateral adrenalectomy and demonstrated significant improvement in blood pressure during follow-up. Two patients await surgery. Others were treated medically. Conclusions: Despite failure due to procedural difficulties, AVS remained useful in the management of patients with PA. Moreover, the success of the procedure needs experienced hands and advanced equipment to achieve optimal outcomes in PA.

Keywords: adrenal venous sampling, lateralization, contralateral suppression index, primary aldosteronism

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: business intelligence, business intelligence capability, decision making, decision quality

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Authors: Juliet Udoudom

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Inappropriate complement selection constitutes one of the major features of non-standard complementation in the Nigerian users of English output of sentence construction. This paper investigates complement clause choice in Written Educated Nigerian English (ENE) and offers some results. It aims at determining preferred and dispreferred patterns of complement clause selection in respect of verb heads in English by selected Nigerian users of English. The complementation data analyzed in this investigation were obtained from experimental tasks designed to elicit complement categories of Verb – Noun -, Adjective – and Prepositional – heads in English. Insights from the Government – Binding relations were employed in analyzing data, which comprised responses obtained from one hundred subjects to a picture elicitation exercise, a grammaticality judgement test, and a free composition task. The findings indicate a general tendency for clausal complements (CPs) introduced by the complementizer that to be preferred by the subjects studied. Of the 235 tokens of clausal complements which occurred in our corpus, 128 of them representing 54.46% were CPs headed by that, while whether – and if-clauses recorded 31.07% and 8.94%, respectively. The complement clause-type which recorded the lowest incidence of choice was the CP headed by the Complementiser, for with a 5.53% incident of occurrence. Further findings from the study indicate that semantic features of relevant embedding verb heads were not taken into consideration in the choice of complementisers which introduce the respective complement clauses, hence the that-clause was chosen to complement verbs like prefer. In addition, the dispreferred choice of the for-clause is explicable in terms of the fact that the respondents studied regard ‘for’ as a preposition, and not a complementiser.

Keywords: complement, complement clause complement selection, complementisers, government-binding

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Authors: Wenbo Wang, Yi-Fang Brook Wu

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The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Travis Morris

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The purpose of this study is to measure the intersection of environmental security entities in terrorist propaganda. To the best of author’s knowledge, this is the first study of its kind to examine this intersection within terrorist propaganda. Rosoka, natural language processing software and frame analysis are used to advance our understanding of how environmental frames function as emotive triggers. Violent jihadi demagogues use frames to suggest violent and non-violent solutions to their grievances. Emotive triggers are framed in a way to leverage individual and collective attitudes in psychological warfare. A comparative research design is used because of the differences and similarities that exist between two variants of violent jihadi propaganda that target western audiences. Analysis is based on salience and network text analysis, which generates violent jihadi semantic networks. Findings indicate that environmental frames are used as emotive triggers across both data sets, but also as tactical and information data points. A significant finding is that certain core environmental emotive triggers like “water,” “soil,” and “trees” are significantly salient at the aggregate level across both data sets. All environmental entities can be classified into two categories, symbolic and literal. Importantly, this research illustrates how demagogues use environmental emotive triggers in cyber space from a subcultural perspective to mobilize target audiences to their ideology and praxis. Understanding the anatomy of propaganda construction is necessary in order to generate effective counter narratives in information operations. This research advances an additional method to inform practitioners and policy makers of how environmental security and propaganda intersect.

Keywords: propaganda analysis, emotive triggers environmental security, frames

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Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

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Authors: Chrysovalanto Sofia Karatosidi, Dimitra Iordanoglou

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The overlap of leadership skills with personality traits, beliefs, values, and the integration of cognitive abilities, analytical and critical thinking skills into leadership competencies raises the need to segregate further and investigate them. Hence, the domains of cognitive functions that contribute to leadership effectiveness should also be identified. Organizational cognitive neuroscience and neuroleadership can shed light on the study of these critical leadership skills. As the first part of our research, this pilot study aims to explore the relationships between higher-order cognitive functions (executive functions), trait emotional intelligence (EI), personality, and general cognitive ability in leadership. Twenty-six graduate and postgraduate students were assessed on neuropsychological tests that measure important aspects of executive functions (EF) and completed self-reported questionnaires about trait EI, personality, leadership styles, and leadership effectiveness. Specifically, we examined four core EF—fluency (phonemic and semantic), information updating and monitoring, working memory, and inhibition of prepotent responses. Leadership effectiveness was positively associated with phonemic fluency (PF), which involves mental flexibility, in turn, an increasingly important ability for future leaders in this rapidly changing world. Transformational leadership was positively associated with trait EI, extraversion, and openness to experience, a result that is following previous findings. The relationship between specific EF constructs and leadership effectiveness emphasizes the role of higher-order cognitive functions in the field of leadership as an individual difference. EF brings a new perspective into leadership literature by providing a direct, non-invasive, scientifically-valid connection between brain function and leadership behavior.

Keywords: cognitive neuroscience, emotional intelligence, executive functions, leadership

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Authors: Lijuan Zhou, Mengqi Wu, Changyong Niu

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Attributed graph clustering can utilize the graph topology and node attributes to uncover hidden community structures and patterns in complex networks, aiding in the understanding and analysis of complex systems. Utilizing contrastive learning for attributed graph clustering can effectively exploit meaningful implicit relationships between data. However, existing attributed graph clustering methods based on contrastive learning suffer from the following drawbacks: 1) Complex data augmentation increases computational cost, and inappropriate data augmentation may lead to semantic drift. 2) The selection of positive and negative samples neglects the intrinsic cluster structure learned from graph topology and node attributes. Therefore, this paper proposes a method called self-supervised Attributed Graph Clustering with Dual Contrastive Loss constraints (AGC-DCL). Firstly, Siamese Multilayer Perceptron (MLP) encoders are employed to generate two views separately to avoid complex data augmentation. Secondly, the neighborhood contrastive loss is introduced to constrain node representation using local topological structure while effectively embedding attribute information through attribute reconstruction. Additionally, clustering-oriented contrastive loss is applied to fully utilize clustering information in global semantics for discriminative node representations, regarding the cluster centers from two views as negative samples to fully leverage effective clustering information from different views. Comparative clustering results with existing attributed graph clustering algorithms on six datasets demonstrate the superiority of the proposed method.

Keywords: attributed graph clustering, contrastive learning, clustering-oriented, self-supervised learning

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: José Luis Gallizo, Pilar Gargallo, Ramon Saladrigues, Manuel Salvador

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This study offers an exploratory statistical analysis of the persistence of annual profits across a sample of firms from different European Union (EU) countries. To this end, a hierarchical Bayesian dynamic model has been used which enables the annual behaviour of those profits to be broken down into a permanent structural and a transitory component, while also distinguishing between general effects affecting the industry as a whole to which each firm belongs and specific effects affecting each firm in particular. This breakdown enables the relative importance of those fundamental components to be more accurately evaluated by country and sector. Furthermore, Bayesian approach allows for testing different hypotheses about the homogeneity of the behaviour of the above components with respect to the sector and the country where the firm develops its activity. The data analysed come from a sample of 23,293 firms in EU countries selected from the AMADEUS data-base. The period analysed ran from 1999 to 2007 and 21 sectors were analysed, chosen in such a way that there was a sufficiently large number of firms in each country sector combination for the industry effects to be estimated accurately enough for meaningful comparisons to be made by sector and country. The analysis has been conducted by sector and by country from a Bayesian perspective, thus making the study more flexible and realistic since the estimates obtained do not depend on asymptotic results. In general terms, the study finds that, although the industry effects are significant, more important are the firm specific effects. That importance varies depending on the sector or the country in which the firm carries out its activity. The influence of firm effects accounts for around 81% of total variation and display a significantly lower degree of persistence, with adjustment speeds oscillating around 34%. However, this pattern is not homogeneous but depends on the sector and country analysed. Industry effects depends also on sector and country analysed have a more marginal importance, being significantly more persistent, with adjustment speeds oscillating around 7-8% with this degree of persistence being very similar for most of sectors and countries analysed.

Keywords: dynamic models, Bayesian inference, MCMC, abnormal returns, persistence of profits, return on assets

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Authors: Marilei Amadeu Sabino

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The aim of this study is to establish differences between the terms ‘false cognates’, ‘false friends’ and ‘deceptive cognates’, usually considered to be synonyms. It will be shown they are not synonyms, since they do not designate the same linguistic process or phenomenon. Despite their differences in meaning, many pairs of formally similar words in two (or more) different languages are true cognates, although they are usually known as ‘false’ cognates – such as, for instance, the English and Italian lexical items ‘assist x assistere’; ‘attend x attendere’; ‘argument x argomento’; ‘apology x apologia’; ‘camera x camera’; ‘cucumber x cocomero’; ‘fabric x fabbrica’; ‘factory x fattoria’; ‘firm x firma’; ‘journal x giornale’; ‘library x libreria’; ‘magazine x magazzino’; ‘parent x parente’; ‘preservative x preservativo’; ‘pretend x pretendere’; ‘vacancy x vacanza’, to name but a few examples. Thus, one of the theoretical objectives of this paper is firstly to elaborate definitions establishing a distinction between the words that are definitely ‘false cognates’ (derived from different etyma) and those that are just ‘deceptive cognates’ (derived from the same etymon). Secondly, based on Set Theory and on the concepts of equal sets, subsets, intersection of sets and disjoint sets, this study is intended to elaborate some theoretical and practical questions that will be useful in identifying more precisely similarities and differences between cognate words of different languages, and according to graphic interpretation of sets it will be possible to classify them and provide discernment about the processes of semantic changes. Therefore, these issues might be helpful not only to the Learning of Second and Foreign Languages, but they could also give insights into Foreign and Second Language Teaching Methodology. Acknowledgements: FAPESP – São Paulo State Research Support Foundation – the financial support offered (proc. n° 2017/02064-7).

Keywords: deceptive cognates, false cognates, foreign language learning, teaching methodology

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Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: Amani Hassan Abu Atieh

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This study aims at examining the patterns of ideology reflected in the written discourse of women writers in the media of Jordan; Lana Mamkgh is taken as an example. This study critically analyzes the discursive, linguistic, and cognitive representations that she employs as an agent in the institutionalized discourse of the media. Grounded in van Dijk’s critical discourse analysis approach to Sociocognitive Discourse Studies, the present study builds a multilayer framework that encompasses van Dijk’s triangle: discourse, society, and cognition. Specifically, the study attempts to analyze, at both micro and macro levels, the underlying cognitive processes and structures, mainly ideology and discursive strategies, which are functional in the production of women’s discourse in terms of meaning, forms, and functions. Cognitive processes that social actors adopt are underlined by experience/context and semantic mental models on the one hand and social cognition on the other. This study is based on qualitative research and adopts purposive sampling, taking as an example a sample of an opinion article written by Lana Mamkgh in the Arabic Jordanian Daily, Al Rai. Taking her role as an agent in the public sphere, she stresses the National and feminist ideologies, demonstrating the use of assertive, evaluative, and expressive linguistic and rhetorical devices that appeal to the logic, ethics, and emotions of the addressee. Highlighting the agency of Jordanian writers in the media, the study sought to achieve the macro goal of dispensing political and social justice to the underprivileged. Further, the study seeks to prove that the voice of Jordanian women, viewed as underrepresented and invisible in the public arena, has come through clearly.

Keywords: critical discourse analysis, sociocognitive theory, ideology, women discourse, media

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Authors: Monday Agbonica Bello Eje

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The language of the print media is characterized by the use of apposition. This linguistic element function strategically in journalistic discourse where it is communicatively necessary to name individuals and provide information about them. Linguistic studies on the language of the print media with bias for apposition have largely dwelt on other areas but the examination of the typology of appositive reference in newspaper discourse. Yet, it is capable of revealing ways writers communicate and provide information necessary for readers to follow and understand the message. The study, therefore, analyses the patterns of appositional occurrences and the typology of reference in newspaper articles. The data were obtained from The Punch and Daily Trust Newspapers. A total of six editions of these newspapers were collected randomly spread over three months. News and feature articles were used in the analysis. Guided by the referential theory of meaning in discourse, the appositions identified were subjected to analysis. The findings show that the semantic relation of coreference and speaker coreference have the highest percentage and frequency of occurrence in the data. This is because the subject matter of news reports and feature articles focuses on humans and the events around them; as a result, readers need to be provided with some form of detail and background information in order to identify as well as follow the discourse. Also, the non-referential relation of absolute synonymy and speaker synonymy no doubt have fewer occurrences and percentages in the analysis. This is tied to a major feature of the language of the media: simplicity. The paper concludes that appositions is mainly used for the purpose of providing the reader with much detail. In this way, the writer transmits information which helps him not only to give detailed yet concise descriptions but also in some way help the reader to follow the discourse.

Keywords: apposition, discourse, newspaper, Nigeria, reference

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Authors: A. Chowdhury, P. Egodawatta, J. M. McGree, A. Goonetilleke

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Hydrologic models are increasingly used as tools to predict stormwater quantity and quality from urban catchments. However, due to a range of practical issues, most models produce gross errors in simulating complex hydraulic and hydrologic systems. Difficulty in finding a robust approach for model calibration is one of the main issues. Though automatic calibration techniques are available, they are rarely used in common commercial hydraulic and hydrologic modelling software e.g. MIKE URBAN. This is partly due to the need for a large number of parameters and large datasets in the calibration process. To overcome this practical issue, a framework for automatic calibration of a hydrologic model was developed in R platform and presented in this paper. The model was developed based on the time-area conceptualization. Four calibration parameters, including initial loss, reduction factor, time of concentration and time-lag were considered as the primary set of parameters. Using these parameters, automatic calibration was performed using Approximate Bayesian Computation (ABC). ABC is a simulation-based technique for performing Bayesian inference when the likelihood is intractable or computationally expensive to compute. To test the performance and usefulness, the technique was used to simulate three small catchments in Gold Coast. For comparison, simulation outcomes from the same three catchments using commercial modelling software, MIKE URBAN were used. The graphical comparison shows strong agreement of MIKE URBAN result within the upper and lower 95% credible intervals of posterior predictions as obtained via ABC. Statistical validation for posterior predictions of runoff result using coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME) was found reasonable for three study catchments. The main benefit of using ABC over MIKE URBAN is that ABC provides a posterior distribution for runoff flow prediction, and therefore associated uncertainty in predictions can be obtained. In contrast, MIKE URBAN just provides a point estimate. Based on the results of the analysis, it appears as though ABC the developed framework performs well for automatic calibration.

Keywords: automatic calibration framework, approximate bayesian computation, hydrologic and hydraulic modelling, MIKE URBAN software, R platform

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Authors: Christina Tafadzwa

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Global HIV and AIDS trends show that Sub-Saharan Africa is the hardest-hit region, and women are disproportionately affected and infected by HIV. The trend is conspicuous in South Africa, where adolescent girls and young women (AGYW), female university students included, bear the burden of HIV infection. Although the female condom (FC) is the only female-oriented HIV and AIDS technology that provides dual protection against unwanted pregnancy and HIV, its uptake and use remain erratic, especially among the youth and young women in institutions of higher learning. This paper explores empirical evidence from the University of Venda (UniVen), which is in the rural areas of Limpopo Province in South Africa, and also among higher learning institutions experiencing low uptake and use of the FC. A phenomenological approach consisting of in-depth interviews was utilized to collect data from a total of 20 female university students at UniVen who were purposively sampled based on their participation in HIV and AIDS dialogues and campaigns conducted on campus. The findings that were analysed thematically revealed that notions of rurality and sociocultural beliefs surrounding women's sexual and reproductive health are key structural factors that influence the low use and uptake of the FC at the rural university. The evidence thus far revealed that female students are discouraged from collecting or initiating FC because of cultural dictates or prescripts which place the responsibility to collect and initiate condom use on men. Hence the inference that UniVen female students' realities are compounded by notions of rurality and society's patriarchal nature that intersect and limit women's autonomy in matters of sex. Guided by the women empowerment theory, this paper argues that such practices take away UniVen female students' agency to decide on their sexual and reproductive health. The normalisation of socio-cultural and harmful gender practices is also a retrogression in the women's health agenda. The paper recommends a holistic approach that engages traditional and community leaders, particularly men, to unlearn and uproot harmful gender norms and patriarchal elements that hinder the promotion and use of the FC.

Keywords: female condom, UniVen, socio-cultural factors, female students, HIV and AIDS

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Authors: Tangyie Evani, Edmond Biloa, Emmanuel Nforbi, Lem Lilian Atanga, Kom Beatrice

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The dynamics of languages in contact necessitates a close study of how its users negotiate meanings from shared values in the process of cross-cultural communication. A transverse analysis of the situation demonstrates the existence of complex efforts on connecting cultural knowledge to cross-linguistic competencies within a widening range of communicative exchanges. This paper sets to examine the psychology of cross-cultural communication in a multi-linguistic setting like Cameroon where many local and international languages are in close contact. The paper equally analyses the pertinence of existing macro sociological concepts as fundamental knowledge traits in literal and idiomatic cross semantic mapping. From this point, the article presents a path model of connecting sociolinguistics to the increasing adoption of a widening range of communicative genre piloted by the on-going globalisation trends with its high-speed information technology machinery. By applying a cross cultural analysis frame, the paper will be contributing to a better understanding of the fundamental changes in the nature and goals of cross-cultural knowledge in pragmatics of communication and cultural acceptability’s. It emphasises on the point that, in an era of increasing global interchange, a comprehensive inclusive global culture through bridging gaps in cross-cultural communication would have significant potentials to contribute to achieving global social development goals, if inadequacies in language constructs are adjusted to create avenues that intertwine with sociocultural beliefs, ensuring that meaningful and context bound sociolinguistic values are observed within the global arena of communication.

Keywords: cross-cultural communication, customary language, literalisms, primary meaning, subclasses, transubstantiation

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Authors: Valeria Abreu Minero, Edward Barker, Hannah Dickson, Francois Husson, Sandra Flynn, Jennifer Shaw

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Purpose – The purpose of this paper is to identify offender typologies based on aspects of the offenders’ psychopathology and their associations with crime scene behaviours using data derived from the National Confidential Enquiry into Suicide and Safety in Mental Health concerning homicides in England and Wales committed by offenders in contact with mental health services in the year preceding the offence (n=759). Design/methodology/approach – The authors used multiple correspondence analysis to investigate the interrelationships between the variables and hierarchical agglomerative clustering to identify offender typologies. Variables describing: the offender’s mental health history; the offenders’ mental state at the time of offence; characteristics useful for police investigations; and patterns of crime scene behaviours were included. Findings – Results showed differences in the offender’s histories in relation to their crime scene behaviours. Further, analyses revealed three homicide typologies: externalising, psychosis and depression. Analyses revealed three homicide typologies: externalising, psychotic and depressive. Practical implications – These typologies may assist the police during homicide investigations by: furthering their understanding of the crime or likely suspect; offering insights into crime patterns; provide advice as to what an offender’s offence behaviour might signify about his/her mental health background; findings suggest information concerning offender psychopathology may be useful for offender profiling purposes in cases of homicide offenders with schizophrenia, depression and comorbid diagnosis of personality disorder and alcohol/drug dependence. Originality/value – Empirical studies with an emphasis on offender profiling have almost exclusively focussed on the inference of offender demographic characteristics. This study provides a first step in the exploration of offender psychopathology and its integration to the multivariate analysis of offence information for the purposes of investigative profiling of homicide by identifying the dominant patterns of mental illness within homicidal behaviour.

Keywords: offender profiling, mental illness, psychopathology, multivariate analysis, homicide, crime scene analysis, crime scene behviours, investigative advice

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Authors: Carine Kawakami

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In French, as well as in English, there are two types of psychological predicate constructions depending on where the experiencer argument is realized; the first type is in the subject position (e.g. Je regrette d’être venu ici. ‘I regret coming here'), hereinafter called ES construction, and the second type is in the object position (e.g. Cette nouvelle m’a surpris. ‘This new surprised me.'), referred as EO construction. In the previous studies about psychological predicates, the syntactic position of the experiencer argument has been just a matter of its connection with the syntactic or semantic structure of the predicate. So that few attentions have been paid to how two types of realization of experiencer are related to the conceptualization of psychological event and to the function of the sentence describing the psychological event, in the sense of speech act theory. In this research, focusing on the French phenomena limited to the first personal pronoun and the present tense, the ES constructions and the EO constructions will be analyzed from cognitive and functional approach. It will be revealed that, due to the possibility to be used in soliloquy and the high co-occurrence with ça (‘it’), the EO constructions may have expressive function to betray what speaker feels in hic et nunc, like interjection. And in the expressive case, the experiencer is construed as a locus where a feeling appears spontaneously and is construed subjectively (e.g. Ah, ça m’énerve! ‘Oh, it irritates me!'). On the other hand, the ES constructions describe speaker’s mental state in an assertive manner rather than the expressive and spontaneously way. In other words, they describe what speaker feels to the interlocutor (e.g. Je suis énervé. ‘I am irritated.'). As a consequence, when the experiencer argument is realized in the subject position, it is construed objectively and have a participant feature in the sense of cognitive grammar. Finally, it will be concluded that the choice of construction type, at least in French, is correlated to the conceptualization of the psychological event and the discourse feature of its expression.

Keywords: french psychological verb, conceptualization, expressive function, assertive function, experiencer realization

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