Search results for: multimodal system

Authors: Armineh Nourbakhsh, Sameena Shah, Carolyn Rose

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Layout-aware language models have been used to create multimodal representations for documents that are in image form, achieving relatively high accuracy in document understanding tasks. However, the large number of parameters in the resulting models makes building and using them prohibitive without access to high-performing processing units with large memory capacity. We propose an alternative approach that can create efficient representations without the need for a neural visual backbone. This leads to an 80% reduction in the number of parameters compared to the smallest SOTA model, widely expanding applicability. In addition, our layout embeddings are pre-trained on spatial and visual cues alone and only fused with text embeddings in downstream tasks, which can facilitate applicability to low-resource of multi-lingual domains. Despite using 2.5% of training data, we show competitive performance on two form understanding tasks: semantic labeling and link prediction.

Keywords: layout understanding, form understanding, multimodal document understanding, bias-augmented attention

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Authors: Neda Razavi Kaleibar, Bahloul Salmani

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The present study dealt with the multimodal analysis of two cinematic adaptations of The Great Gatsby as intersemiotic translation. The assessment in this study went beyond the faithfulness based on repetition, addition, deletion, and creation which limit the analysis from other aspects. In fact, this research aimed to pinpoint the role of multimodality in examining the intersemiotic translations of the novel into film by means of analyzing different applied modes. Through a qualitative type of research, the analysis was conducted based on the theory proposed by Burn as Kineikonic mode theory derived from the concept of multimodality. The results of the study revealed that due to the applied modes, each adaptation represents a sense and meaning different from the other one. Analyzing the results and discussions, it was concluded that not only the modes have an undeniable role in film adaptations, but rather multimodal analysis including different nonverbal modes can be a useful and functional choice for analyzing the intersemiotic translations.

Keywords: cinematic adaptation, intersemiotic translation, kineikonic mode, multimodality

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Authors: Inas Hussein

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Communication in the modern world is increasingly becoming multimodal due to globalization and the digital space we live in which have remarkably affected how people communicate. Accordingly, Multimodal Discourse Analysis (MDA) is an emerging paradigm in discourse studies with the underlying assumption that other semiotic resources such as images, colours, scientific symbolism, gestures, actions, music and sound, etc. combine with language in order to  communicate meaning. One of the effective multimodal media that combines both verbal and non-verbal elements to create meaning is political cartoons. Furthermore, since political and social issues are mirrored in political cartoons, these are regarded as potential objects of discourse analysis since they not only reflect the thoughts of the public but they also have the power to influence them. The aim of this paper is to analyze some selected cartoons on the recognition of Jerusalem as Israel's capital by the American President, Donald Trump, adopting a multimodal approach. More specifically, the present research examines how the various semiotic tools and resources utilized by the cartoonists function in projecting the intended meaning. Ten political cartoons, among a surge of editorial cartoons highlighted by the Anti-Defamation League (ADL) - an international Jewish non-governmental organization based in the United States - as publications in different Arabic-language newspapers in Egypt, Saudi Arabia, UAE, Oman, Iran and UK, were purposively selected for semiotic analysis. These editorial cartoons, all published during 6^th–18^th December 2017, invariably suggest one theme: Jewish and Israeli domination of the United States. The data were analyzed using the framework of Visual Social Semiotics. In accordance with this methodological framework, the selected visual compositions were analyzed in terms of three aspects of meaning: representational, interactive and compositional. In analyzing the selected cartoons, an interpretative approach is being adopted. This approach prioritizes depth to breadth and enables insightful analyses of the chosen cartoons. The findings of the study reveal that semiotic resources are key elements of political cartoons due to the inherent political communication they convey. It is proved that adequate interpretation of the three aspects of meaning is a prerequisite for understanding the intended meaning of political cartoons. It is recommended that further research should be conducted to provide more insightful analyses of political cartoons from a multimodal perspective.

Keywords: Multimodal Discourse Analysis (MDA), multimodal text, political cartoons, visual modality

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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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: Sharifa Alshahrani

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Comics is a multimodal text wherein meaning is created by taking in all modes of expression at once. It uses two different semiotic modes, the verbal and the visual modes, together to make meaning and these different semiotic modes can be socially and culturally shaped to give meaning. Therefore, comics translation cannot treat comics as a monomodal text by translating only the verbal mode inside or outside the speech balloons as the cultural differences are encoded in the visual mode as well. Due to the development of the internet and editing software, comics translation is not anymore confined to the publishing houses and official translation as scanlation, or the fan translation took the initiative in translating comics for being emotionally attracted to the culture and genre. Scanlation is carried out by volunteering fans who translate out of passion. However, quality is one of the debatable issues relating to scanlation and fan translation. This study will investigate how the dynamic multimodal relationship in comics is exploited and interpreted in the translation by exploring the translation strategies and procedures adopted by the publishing houses and scanlation in interpreting comics into Arabic using three analytical frameworks; cultural references model, multimodal relation model and translation strategies and procedures models.

Keywords: comics, multimodality, translation, scanlation

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Authors: Mariam Waheed Mekheimar

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Nascent research is conducted to the advancement of discourse analysis to include different modes as images, sound, and text. The focus of this study will be to elucidate how images are embedded with texts in an audio-visual medium as cinema to send political messages; it also seeks to broaden our understanding of politics beyond a relatively narrow conceptualization of the 'political' through studying non-traditional discourses as the cinematic discourse. The aim herein is to develop a systematic approach to film analysis to capture political meanings in films. The method adopted in this research is Multimodal Discourse Analysis (MDA) focusing on embedding visuals with texts. As today's era is the era of images and that necessitates analyzing images. Drawing on the writings of O'Halloran, Kress and Van Leuween, John Bateman and Janina Wildfeuer, different modalities will be studied to understand how those modes interact in the cinematic discourse. 'People at the top movie' is selected as an example to unravel the political meanings throughout film tackling the cinematic representation of the notion of social justice.

Keywords: Egyptian cinema, multimodal discourse analysis, people at the top, social justice

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Authors: Victor Milanes, Martha Hubertz

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In the wake of COVID-19, all aspects of life have been estranged, and humanity has been forced to shift toward a more technologically integrated mode of operation. Essential work such as Healthcare, business, and public policy are a few notable industries that were initially dependent upon face-to-face modality but have completely reimagined their operation style. Unique to these fields, education was particularly strained because academics, teachers, and professors alike were obligated to shift their curriculums online over the course of a few weeks while also maintaining the expectation that they were educating their students to a similar level accomplished pre-pandemic. This was notable as research indicates two key concepts: Students prefer face-to-face modality, and due to the disruption in academic continuity/style, there was a negative impact on student's overall education and performance. With these two principles in mind, this study aims to inquire what online strategies could be best employed by teachers to educate their students, as well as what strategies could be adopted in a multimodal setting if deemed necessary by the instructor or outside convoluting factors (Such as the case of COVID-19, or a personal matter that demands the teacher's attention away from the classroom). Strategies and methods will be cross-analyzed via a ranking system derived from various recognized teaching assessments, in which engagement, retention, flexibility, interest, and performance are specifically accounted for. We expect to see an emphasis on positive social pressure as a dominant factor in the improved propensity for education, as well as a preference for visual aids across platforms, as research indicates most individuals are visual learners.

Keywords: technological integration, multimodal teaching, education, student engagement

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Authors: Tomasz Sapiński, Dorota Kamińska, Adam Pelikant, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari

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People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman’s emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.

Keywords: body movement, emotion recognition, emotional corpus, facial expressions, gestures, multimodal database, speech

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Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

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In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

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Authors: Moustafa Ibrahim Moustafa

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The purpose of this study was to determine the immediate and long term effects of a multimodal program, with the addition of cervical sagittal curve restoration and forward head correction, on severity of dizziness, disability, frequency of dizziness, and severity of cervical pain. 72 patients with cervicogenic dizziness, definite hypolordotic cervical spine, and forward head posture were randomized to experimental or a control group. Both groups received the multimodal program, additionally, the study group received the Denneroll cervical traction. All outcome measures were measured at three intervals. The general linear model indicated a significant group × time effects in favor of experimental group on measures of anterior head translation (F=329.4 P < .0005), cervical lordosis (F=293.7 P < .0005), severity of dizziness (F=262.1 P < .0005), disability (F=248.9 P < .0005), frequency of dizziness (F=53.9 P < .0005), and severity of cervical pain (F=350.1 P < .0005). The addition of Dennroll cervical traction to a multimodal program can positively affect dizziness management outcomes.

Keywords: randomized controlled trial, traction, dizziness, cervical

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Authors: Raheel Shakoor Siddiqui, Shahbaz Malik, Manikandar Srinivas Cheruvu, Sanjay Narayana Murthy, Livio DiMascio

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Background: elderly traumatic hip fracture patients frequently present to trauma services globally. Rising low energy falls amongst an osteoporotic aging population is the commonest cause for injury. Hip fractures in this population are a major cause for severe pain, morbidity and mortality. The term hip fracture is interchangeable with neck of femur fracture, fractured neck of femur or proximal femur fracture. Hip fracture pain management protocols and guidelines suggest conventional analgesia, nerve block and opioid based treatment as rescue analgesia. There is a current global opioid crisis with overuse, abuse and dependence. Adverse opioid related complications in vulnerable elderly patients further adds to morbidity and mortality. Systematic reviews in literature have evidenced superiority of multimodal analgesia in osteoarthritic primary joint replacements compared to opioids however, this has not yet been conducted for elderly traumatic hip fracture patients. Aims: The primary aim of this systematic review is to provide standardised evidence following Cochrane and PRISMA guidance in determining advantages of perioperative multimodal analgesia over conventional opioid based treatments in elderly traumatic hip fractures. Methods: 5 databases were searched from January 2000-2023 which identified 8 randomised controlled trials and 446 total participants. These trials met defined PICOS eligibility criteria of patient mean age ≥ 65 years presenting with a unilateral traumatic fractured neck of femur for operative intervention. Analgesic intervention with perioperative multimodal analgesia has been compared to conventional opioid based analgesia. Outcomes of interest include, primarily, the change in postoperative opioid consumption within a 0-30 postoperative period and secondarily, the change in postoperative adverse events and complications. A qualitative synthesis has been performed due to clinical heterogenicity and variance amongst trials. Results: GRADE evidence of moderate quality supports perioperative multimodal analgesia leads to a reduction in postoperative opioid consumption however, low quality evidence supports a reduction of adverse effects and complications. Conclusion: Perioperative multimodal analgesia whether used preoperative, intraoperative and/or postoperative leads to a reduction in postoperative opioid consumption for elderly traumatic hip fracture patients. This review recommends the use of perioperative multimodal analgesia as part of hip fracture pain protocols however, caution and clinical judgement should be used as the risk of adverse effects may not be lower.

Keywords: trauma, orthopaedics, hip, fracture, neck of femur fracture, analgesia, multimodal analgesia, opioid

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Authors: Rasha Ali Dheyab

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In today's increasingly interconnected and technologically-driven world, communication has evolved beyond traditional verbal exchanges. This paper delves into the fascinating realm of multimodal communication, a dynamic field at the intersection of linguistics, gesture studies, and technology. The study of how humans convey meaning through a combination of spoken language, gestures, facial expressions, and digital platforms has gained prominence as our modes of interaction continue to diversify. This exploration begins by examining the foundational theories in linguistics and gesture studies, tracing their historical development and mutual influences. It further investigates the role of nonverbal cues, such as gestures and facial expressions, in augmenting and sometimes even altering the meanings conveyed by spoken language. Additionally, the paper delves into the modern technological landscape, where emojis, GIFs, and other digital symbols have emerged as new linguistic tools, reshaping the ways in which we communicate and express emotions. The interaction between traditional and digital modes of communication is a central focus of this study. The paper investigates how technology has not only introduced new modes of expression but has also influenced the adaptation of existing linguistic and gestural patterns in online discourse. The emergence of virtual reality and augmented reality environments introduces yet another layer of complexity to multimodal communication, offering new avenues for studying how humans navigate and negotiate meaning in immersive digital spaces. Through a combination of literature review, case studies, and theoretical analysis, this paper seeks to shed light on the intricate interplay between language, gesture, and technology in the realm of multimodal communication. By understanding how these diverse modes of expression intersect and interact, we gain valuable insights into the ever-evolving nature of human communication and its implications for fields ranging from linguistics and psychology to human-computer interaction and digital anthropology.

Keywords: multimodal communication, linguistics ., gesture studies., emojis., verbal communication., digital

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Authors: Yi Meng, Yun Gao

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Having spent significant periods studying and working in North America and Europe, we, as two Chinese art educators, have been profoundly shaped by both Eastern and Western cultures. Consequently, our ambition is to enrich students' learning experiences by delving into and merging both cultural perspectives for innovative, creative expressions. This exposition draws on our action research study on students' visual literacy practices in a visual literacy course at a prominent Chinese university. The central premise was to explore innovative art forms by cross-utilizing various aspects of diverse cultures. By examining distinct cultural elements, we encouraged students to break away from familiar approaches and forge new paths in their creative endeavors. In implementing our curriculum, we utilized a multimodal pedagogy that deviated from the predominant print-based presentations typically employed in our classroom settings. This pedagogical approach effectively encouraged students to critically analyze the artifact, imbue it with their understanding and perspectives, and then produce an original piece. This approach also motivated students to leverage the semiotic potential of various communicative modes to address diverse cultural issues through their multimodal designs. To demonstrate the potential for cultural amalgamation, we utilized the artwork of Hong Kong-based artist Tik Ka. His works epitomize the fusion of Chinese traditions with Western pop culture, which served as a visual and conceptual reference point for students. Seeing how these distinct cultural elements could coexist and enrich each other in Tik Ka's work was inspiring and motivating for the students. Taken together, these pedagogical strategies helped create a dialogical space where students could actively experience, analyze, and negotiate complex modes of expression. This environment fostered active learning, encouraging students to apply their knowledge, question their assumptions, and reconsider their perspectives. Overall, such a unique approach to visual literacy education has the potential to reshape students' understanding of both cultures. By encouraging them to critically engage with their multimodal designs, we promoted an in-depth, nuanced appreciation of these diverse cultural heritages. The students no longer just interpreted and replicated images—they actively contributed to a dynamic and ongoing conversation between cultures.

Keywords: multimodal pedagogy, creative expressions, visual literacy education, multimodal designs

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Authors: Khaled Mohammed

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This paper presents novel face recognition and identification approaches for the real-time attendance management problem in large companies/factories and government institutions. The proposed uses the Minimum Ratio (MR) approach for employee identification. Capturing the authentic face variability from a sequence of video frames has been considered for the recognition of faces and resulted in system robustness against the variability of facial features. Experimental results indicated an improvement in the performance of the proposed system compared to the Previous approaches at a rate between 2% to 5%. In addition, it decreased the time two times if compared with the Previous techniques, such as Extreme Learning Machine (ELM) & Multi-Scale Structural Similarity index (MS-SSIM). Finally, it achieved an accuracy of 99%.

Keywords: attendance management system, face detection and recognition, live face recognition, minimum ratio

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Authors: Ashleigh Cicconi

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Teachers may not have access to necessary and effective strategies for managing stress, trauma, and emotional exhaustion, which can lead to burnout. This practice-based research focused on the exploration of teacher well-being through participation in a wellness program in order to mitigate high stress levels and feelings of burnout. The purpose of this qualitative research was to explore how a multimodal, trauma-informed yoga and arts-based mindfulness program impacted stress levels and overall well-being for teachers in a school setting. The case study approach was used to investigate participant perceptions of interactions between multimodal accessibility, a trauma-informed wellness program, and teacher well-being. A sample size of 10 teachers employed full-time at a public high school in the Mid-Atlantic region were recruited via email correspondence to participate in the eight-week wellness program. Data were triangulated across semi-structured interviews, journal entries, and focus group guided questions, and transcripts were uploaded into the NVivo software application for thematic analysis. Data showed perceptions of improvements in overall well-being from participation in the wellness program and that utilizing trauma-informed practices may be an effective coping skill for stress. The multimodal design of the program was perceived to positively impact participation and accessibility to wellness strategies. Findings from this study suggest that the inclusion of trauma-informed practices within a wellness program may be effective for managing stress and trauma experienced by teachers, thereby aiding in improvement in overall well-being. Findings also suggest that multimodality may be effective for increasing participation in and accessibility to wellness strategies.

Keywords: trauma informed practices, wellness program, teacher wellbeing, accessible program, multimodal

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Authors: Zhen Li

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In contemporary China, the rise of consumer culture and feminism has facilitated the ‘She-Economy’, where Chinese women’s consumption power has been boosted along with the thriving Chinese economy. Domestic reality TV shows such as Sisters Who Make Waves (hereafter SWMW) target female audiences by bringing women's issues such as age, appearance, and balance between family and career to the discussion. Against this backdrop, this study adopted multimodal content analysis to investigate how SWMW failed to live up to the feminist goals the show had claimed and how serious women’s issues were consumed and capitalized by the consumer media culture from consumer culture and feminist perspectives. The findings reveal that while the female-themed work claims to uncover the charm that age brings to women over their thirties, it merely mentions female anxiety and uses feminism in disguise to achieve commercial success without in-depth thinking and discussion of what real-life issues women in China are tackling. They further show that the mass media-promoted modern femininity combined with consumerism deepens anxiety over aging among female audiences. The study sheds light on understanding the new development of Chinese femininity and the impact of consumer culture on feminist consciousness in contemporary China.

Keywords: consumer culture, feminism, multimodal content analysis, she-economy

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Authors: Dike Ibegbulem

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This paper interrogates the chances of Nigeria achieving its target of making the list of the first 70 countries in World Bank’s Ease of Doing Business (EoDB) rankings by the year 2023. That is, in light of existing conflicts in policies relating to the door-to-door carriage of goods and multimodal transport operations (MTOs) in the country. Drawing on the famed Legal Origins theory plus data from World Bank; and using Singapore as a touchstone, the paper unveils how amongst the top-ranked Commonwealth jurisdictions, positive correlations have been recorded over the past years between certainty in their policies on MTOs on the one hand; and their Enforcing Contracts (EC) and Doing Business (DB) indices on the other. The paper postulates that to increase Nigeria’s chances of achieving her DB2023 objective, legislative and curial policies on MTOs and door-to-door carriage of goods have to be realigned in line with prevailing policies in highly-ranked Commonwealth jurisdictions of the Global North. Her appellate courts, in particular, will need some unshackling from English pedigrees which still delimit admiralty jurisdiction to port-to-port shipping, to the exclusion of door-to-door carriage of goods beyond navigable waters. The paper identifies continental and domestic instruments, plus judicial precedents, which provide bases for expanding admiralty jurisdiction to adjudication of claims derived from door-to-door or multimodal transport contracts and other allied maritime-plus contracts. It prescribes synergy between legislative and curial policies on MTOs and door-to-door carriage of goods as species of admiralty – an emerging trend in top-ranked Commonwealth jurisdictions of the Global North.

Keywords: admiralty jurisdiction, legal origins, world bank, ease of doing business, enforcing contracts, multimodal transport operation, door-to-door, carriage of goods by sea, combined transport shipping

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Shaza Melies, Abeer Refky, Nihad Mansoor

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Multimodality involves the communication between verbal and visual components in various discourses. A painting represents a form of communication between the artist and the viewer in terms of colors, shades, objects, and the title. This paper aims to present how multimodality can be used to decode the verbal and visual dimensions a painting holds. For that purpose, this study uses Kress and van Leeuwen’s theoretical framework of visual grammar for the analysis of the multimodal semiotic resources of selected paintings of Salvador Dali. This study investigates the visual decoding of the selected paintings of Salvador Dali and analyzing their social and political meanings using Kress and van Leeuwen’s framework of visual grammar. The paper attempts to answer the following questions: 1. How far can multimodality decode the verbal and non-verbal meanings of surrealistic art? 2. How can Kress and van Leeuwen’s theoretical framework of visual grammar be applied to analyze Dali’s paintings? 3. To what extent is Kress and van Leeuwen’s theoretical framework of visual grammar apt to deliver political and social messages of Dali? The paper reached the following findings: the framework’s descriptive tools (representational, interactive, and compositional meanings) can be used to analyze the paintings’ title and their visual elements. Social and political messages were delivered by appropriate usage of color, gesture, vectors, modality, and the way social actors were represented.

Keywords: multimodal analysis, painting analysis, Salvador Dali, visual grammar

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Authors: S. Adhirai, R. P. Mahapatra, Paramjit Singh

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Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Keywords: optimization, optimal value, objective function, optimization problems, meta-heuristic optimization algorithms, Whale Optimization Algorithm, implementation, MATLAB

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Authors: Visvaganthie Moodley

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While rhetoric goes back as far as Aristotle who focalised its meaning as the “art of persuasion”, most scholars have focused on elocutio and dispositio canons, neglecting the rhetorical impact of multimodal texts, such as documentaries. Film documentaries are being increasingly rhetoric, often used by wildlife conservationists for influencing people to become more mindful about humanity’s connection with nature. This paper examines the award-winning film documentary, “My Octopus Teacher”, which depicts naturalist, Craig Foster’s unique discovery and relationship with a female octopus in the southern tip of Africa, the Cape of Storms in South Africa. It is anchored in Leech and Short’s (2007) framework of linguistic and stylistic categories – comprising lexical items, grammatical features, figures of speech and other rhetoric features, and cohesiveness – with particular foci on diction, anthropomorphic language, metaphors and symbolism. It also draws on Kress and van Leeuwen’s (2006) multimodal analysis to show how verbal cues (the narrator’s commentary), visual images in motion, visual images as metaphors and symbolism, and aural sensory images such as music and sound synergise for rhetoric effect. In addition, the analysis of “My Octopus Teacher” is guided by Nichol’s (2010) narrative theory; features of a documentary which foregrounds the credibility of the narrative as a text that represents real events with real people; and its modes of construction, viz., the poetic mode, the expository mode, observational mode and participatory mode, and their integration – forging documentaries as multimodal texts. This paper presents a multimodal rhetoric discussion on the sequence of salient episodes captured in the slow moving one-and-a-half-hour documentary. These are: (i) The prologue: on the brink of something extraordinary; (ii) The day it all started; (iii) The narrator’s turmoil: getting back into the ocean; (iv) The incredible encounter with the octopus; (v) Establishing a relationship; (vi) Outwitting the predatory pyjama shark; (vii) The cycle of life; and (viii) The conclusion: lessons from an octopus. The paper argues that wildlife documentaries, characterized by plausibility and which provide researchers the lens to examine the ideologies about animals and humans, offer an assimilation of the various senses – vocal, visual and audial – for engaging viewers in stylized compelling way; they have the ability to persuade people to think and act in particular ways. As multimodal texts, with its use of lexical items; diction; anthropomorphic language; linguistic, visual and aural metaphors and symbolism; and depictions of anthropocentrism, wildlife documentaries are powerful resources for promoting wildlife conservation and conscientizing people of the need for establishing a harmonious relationship with nature and humans alike.

Keywords: documentaries, multimodality, rhetoric, style, wildlife, conservation

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Authors: Jiandong Lv, Xingang Wang, Cuiling Shao

Abstract:

The rapid development of social media platforms has made it one of the important news sources. While it provides people with convenient real-time communication channels, fake news and rumors are also spread rapidly through social media platforms, misleading the public and even causing bad social impact in view of the slow speed and poor consistency of artificial rumor detection. We propose an end-to-end rumor detection model-TIMF, which captures the dependencies between multimodal data based on the interactive attention mechanism, uses a transformer for cross-modal feature sequence mapping and combines hybrid fusion strategies to obtain decision results. This paper verifies two multi-modal rumor detection datasets and proves the superior performance and early detection performance of the proposed model.

Keywords: hybrid fusion, multimodal fusion, rumor detection, social media, transformer

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

Abstract:

Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: N. Dunleavy, E. Lovemore, K. Siljeur, D. Jackson, M. Hendricks, M. Hoosain, N. Plastow, S. Marais

Abstract:

Background: The work requirements of sewing machine operators cause physical and emotional strain. Past ergonomic interventions have been provided to alleviate physical concerns; however, a holistic, multimodal intervention was needed to improve these factors. Aim: The study aimed to examine the effect of an occupational therapy programme on sewing machine operators’ pain, mental health, and productivity within a factory in the South African context. Methods: A pilot randomised control trial was conducted with 22 sewing machine operators within a single factory. Stratified randomisation was used to determine the experimental (EG) and control groups (CG), using measures for pain intensity, level of depression (mental health), and productivity rates as stratification variables. The EG received the multimodal intervention, incorporating education, seating adaptations, and mental health intervention. In three months, the CG will receive the same intervention. Pre- and post-intervention testing have occurred with upcoming three- and six-month follow-ups. Results: Immediate results indicate a statistically significant decrease in pain in both experimental and control groups; no change in productivity scores and depression between the two groups. This may be attributed to external factors. The values for depression further showed no statistical significance between the two groups and within pre-and post-test results. The Statistical Program for Social Sciences (SPSS) version-24 was used as the data analysis testing, where all the tests will be evaluated at a 5% significance level. Contribution of research: The research adds to the body of knowledge informing the Occupational Therapy role in work settings, providing evidence on the effectiveness of workplace-based multimodal interventions. Conclusion: The study provides initial data on the effectiveness of a pilot randomised control trial on pain and mental health in South Africa. Results indicated no quantitative change between the experimental and control groups; however, qualitative data suggest a clinical significance of the findings.

Keywords: ergonomics programme, occupational therapy, sewing machine operators, workplace-based multimodal interventions

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Sijia Guo

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The development of new technologies and the falling cost of high-speed Internet access have made it easier for institutes and language teachers to opt different ways to communicate with students at distance. The emergence of web-conferencing applications, which integrate text, chat, audio / video and graphic facilities, offers great opportunities for language learning to through the multimodal environment. This paper reports on data elicited from a Ph.D. study of using web-conferencing in the teaching of first-year Chinese class in order to promote learners’ collaborative learning. Firstly, a comparison of four desktop videoconferencing (DVC) tools was conducted to determine the pedagogical value of the videoconferencing tool-Blackboard Collaborate. Secondly, the evaluation of 14 campus-based Chinese learners who conducted five one-hour online sessions via the multimodal environment reveals the users’ choice of modes and their learning preference. The findings show that the tasks designed for the web-conferencing environment contributed to the learners’ collaborative learning and second language acquisition.

Keywords: computer-mediated communication (CMC), CALL evaluation, TBLT, web-conferencing, online Chinese teaching

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Authors: Shomaila Sadaf, Margarethe Olbertz-Siitonen

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This study looks at identity and othering in discourses around sensitive issues in social media. More specifically, the study explores the multimodal resources and narratives through which the other is formed, and identities are ascribed in online spaces. As an integral part of social life, media spaces have become an important site for negotiating and ascribing identities. In line with recent research, identity is seen hereas constructions of belonging which go hand in hand with processes of in- and out-group formations that in some cases may lead to othering. Previous findings underline that identities are neither fixed nor limited but rather contextual, intersectional, and interactively achieved. The goal of this study is to explore and develop an understanding of how people co-construct the ‘other’ and ascribe certain identities in social media using multiple modes. In the beginning of the year 2018, the British government decided to include relationships, sexual orientation, and sex education into the curriculum of state funded primary schools. However, the addition of information related to LGBTQ+in the curriculum has been met with resistance, particularly from religious parents.For example, the British Muslim community has voiced their concerns and protested against the actions taken by the British government. YouTube has been used by news companies to air video stories covering the protest and narratives of the protestors along with the position ofschool officials. The analysis centers on a YouTube video dealing with the protest ofa local group of parents against the addition of information about LGBTQ+ in the curriculum in the UK. The video was posted in 2019. By the time of this study, the videos had approximately 169,000 views andaround 6000 comments. In deference to multimodal nature of YouTube videos, this study utilizes multimodal discourse analysis as a method of choice. The study is still ongoing and therefore has not yet yielded any final results. However, the initial analysis indicates a hierarchy of ascribing identities in the data. Drawing on multimodal resources, the media works with social categorizations throughout the documentary, presenting and classifying involved conflicting parties in the light of their own visible and audible identifications. The protesters can be seen to construct a strong group identity as Muslim parents (e.g., clothing and reference to shared values). While the video appears to be designed as a documentary that puts forward facts, the media does not seem to succeed in taking a neutral position consistently throughout the video. At times, the use of images, soundsand language contributes to the formation of “us” vs. “them”, where the audience is implicitly encouraged to pick a side. Only towards the end of the documentary this problematic opposition is addressed and critically reflected through an expert interview that is – interestingly – visually located outside the previously presented ‘battlefield’. This study contributes to the growing understanding of the discursive construction of the ‘other’ in social media. Videos available online are a rich source for examining how the different social actors ascribe multiple identities and form the other.

Keywords: identity, multimodal discourse analysis, othering, youtube

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Authors: Nissa Phloimontri

Abstract:

This research explores the current physical conditions of motorcycle taxi stands located near the BTS stations in the central business district (CBD) and the comfort conditions of motorcycle taxi drivers. The criteria set up for physical stand survey and assessment are the integration of multimodal access design guidelines. After the survey, stands that share similar characteristics are classified into a series of typologies. Based on the environmental comfort model, questionnaires and in-depth interviews are conducted to evaluate the comfort levels of drivers including physical, functional, and psychological comfort. The results indicate that there are a number of motorcycle taxi stands that are not up to standard and are not conducive to the work-related activities of drivers. The study concludes by recommending public policy for integrated paratransit stops that support the multimodal transportation and seamless mobility concepts within the specific context of Bangkok as well as promote the quality of work life of motorcycle taxi drivers.

Keywords: motorcycle taxi, paratransit stops, environmental comfort, quality of work life

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