Search results for: self-supervised representation learning

Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Tianqi Wu, Min Wang

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Research investigating language acquisition from a constructionist perspective has demonstrated that language is learned as constructions at various linguistic levels, which is related to factors of frequency, semantic prototypicality, and form-meaning contingency. However, previous research on construction learning tended to focus on clause-level constructions such as verb argument constructions but few attempts were made to study morpheme-level constructions such as the progressive construction, which is regarded as a source of acquisition problems for English learners from diverse L1 backgrounds, especially for those whose L1 do not have an equivalent construction such as German and Chinese. To trace the developmental trajectory of Chinese EFL learners’ use of the progressive with respect to verb frequency, verb-progressive contingency, and verbal prototypicality and generality, a learner corpus consisting of three sub-corpora representing three different English proficiency levels was extracted from the Chinese Learners of English Corpora (CLEC). As the reference point, a native speakers’ corpus extracted from the Louvain Corpus of Native English Essays was also established. All the texts were annotated with C7 tagset by part-of-speech tagging software. After annotation all valid progressive hits were retrieved with AntConc 3.4.3 followed by a manual check. Frequency-related data showed that from the lowest to the highest proficiency level, (1) the type token ratio increased steadily from 23.5% to 35.6%, getting closer to 36.4% in the native speakers’ corpus, indicating a wider use of verbs in the progressive; (2) the normalized entropy value rose from 0.776 to 0.876, working towards the target score of 0.886 in native speakers’ corpus, revealing that upper-intermediate learners exhibited a more even distribution and more productive use of verbs in the progressive; (3) activity verbs (i.e., verbs with prototypical progressive meanings like running and singing) dropped from 59% to 34% but non-prototypical verbs such as state verbs (e.g., being and living) and achievement verbs (e.g., dying and finishing) were increasingly used in the progressive. Apart from raw frequency analyses, collostructional analyses were conducted to quantify verb-progressive contingency and to determine what verbs were distinctively associated with the progressive construction. Results were in line with raw frequency findings, which showed that contingency between the progressive and non-prototypical verbs represented by light verbs (e.g., going, doing, making, and coming) increased as English proficiency proceeded. These findings altogether suggested that beginning Chinese EFL learners were less productive in using the progressive construction: they were constrained by a small set of verbs which had concrete and typical progressive meanings (e.g., the activity verbs). But with English proficiency increasing, their use of the progressive began to spread to marginal members such as the light verbs.

Keywords: Construction learning, Corpus-based, Progressives, Prototype

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Ting Li Liu

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‘Leftover woman’ or ‘3S’ woman is the term used to describe a well-educated, high income, independent woman who is single and never married around 30 years in Chinese society. With the naming of this demographic of ‘leftover women’, their family, dating culture, mate selection and marriage attract public concern. Massive media representations of ‘leftover women’ occur daily; the research aims to present several media representations of women’s anxiety about their singlehood and related marital issues around thirty. The research triangulates two areas of media representation of ‘leftover women’: films and audience reviews on ‘Douban Movie’ website. Drawing on traditional media studies, Fairclough’s critical discourse analysis combined with multimodal techniques is applied to the research to analyze the representations of ‘leftover women’ and their implications for marital culture in China, in conjunction with a feminist perspective. The conference paper will discuss two case studies: the film ‘The last woman standing’ and ‘I Do’. Paying attention to different aspects of ‘leftover women’, the research aims to re-examine the representations of ‘leftover women’ in selected scenes, such as their age anxiety, family, marriage, dating process, careers, etc. The paper also includes public beliefs about ‘leftover women’ from online audience reviews. In conclusion, the emergence of ‘leftover women’ is a reflection of Chinese tradition’s impact on people’s lives and new changes in Chinese families and their attitude to marriage.

Keywords: leftover women, marriage, family, media culture, China

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Authors: Siti Rodliyah, Andrik Purwasito, Bani Sudardi, Abdullah Wakit

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This study employs sociological-ethnographic basic method and the cultural studies paradigm as the approach in understanding the habitus within the customary marriage of the East Nusa Tenggara society who require belis as a bride-price. The conceptual basis underlying the application of habitus theory and symbolic power in East Nusa Tenggara (NTT) society refers to the Bourdieu’s framework. This study is a result of participatory observation on habitus of a marital system using belis observed by the NTT society as a cognitive structure which connects individuals to the social activities of the customary marriage and makes it unquestionable habits. Knowledge of the social world under the pretext of prosperity for the recipients (family) of a bride-price can be a political instrument for the sustainability of power relations. The ritual-mythical system in the society has never been fully present as a neutral habit. The habitus reflected in the marital relationship among the NTT society enables the men to obtain and exercise their power relations. The sustainability of power relations can be seen from the representation of the social status of a girl and the properties attached to her. This is what gave birth to a symbolic rule, in which the social rules about bride-price or belis eventually will serve the interests of those who occupy a dominant position in the social structure, namely the rich men.

Keywords: belis, habitus, East Nusa Tenggara, marital system, power, symbolic

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Mahmoud Nabilu

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The present study explored the effect of using Mobile listening applications on developing listening skills by Iranian intermediate EFL learners. Fifty male intermediate English learners whose age range was between 15 and 20, participated in the study. The participants were placed in two groups on the basis of their scores on a placement test. Therefore, the participants of the study were homogenized in terms of general proficiency, and groups were assigned as one experimental group and one control group. The experimental group was instructed by the treatment which was using mobile applications to develop their listening skills while the control group received traditional methods. The research data were obtained from the 40-item multiple-choice tests as a pre-test and a post-test. The results of the t-test clearly revealed that the learners in the experimental group performed better in the post-test than the pre-test. This implies that using a mobile application for developing listening skills as a treatment was effective in helping the language learners perform better on post-test. However, a statistically significant difference was found between the post-tests scores of the two groups. The mean of the experimental group was greater compared to the control group. The participants were Iranian and from an Iranian Language Institute, so care should be taken while generalizing the results to the learners of other nationalities. However, in the researcher's view, the findings of this study have valuable implications for teachers and learners, methodologists and syllabus designers, linguists and MALL/CALL (mobile/computer-assisted language learning) experts. Using the result of the present paper is an aim of raising the consciousness of a better technique of developing listening skills in order to make language learning more efficient for the learners.

Keywords: Mobile listening applications, intermediate EFL learners, MALL, CALL

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Authors: Christine Ivy A. Nogot

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Under the colony of Spain for more than three centuries, the Philippines has a deep-rooted structure of Western ideologies and colonialism. The late 19th century, the period of Enlightenment, created a significant impact on our history when a group of middle-class Filipino men were sent to Europe to study. They were called Ilustrados, a Spanish word for erudite. They were the enlightened; the well-educated, intellectual scholars. Their writings provide intellectual grounds for the awakening of national consciousness that eventually prompted national movements and revolutions. They helped to establish a postcolonial society. In the modern era, Miguel Syjuco, a Filipino expatriate, wrote a novel and titled it Ilustrado. It is a representation of the liberal mind of the diasporic author in contemporary discourse. It provides a critical examination of the ilustrado in transition through the character of Miguel, who is also an expatriate writer. Using Syjuco’s award-winning novel as the primary text and anchored on Said’s concept of Orientalism, this paper examines how the depiction of features of the Eastern world is presented in the literary discourse. This paper looks into Said’s concept of orientalism as a hegemonic discursive structure and shows how Western superiority influences the Eastern culture in literary discourse. It explores Gramsci’s theory of cultural hegemony to explore Said’s argument that Western powers conquer the orient through culture and ideology. This paper presents how dominant ideologies and the social context redefine the ilustrado in the contemporary era.

Keywords: cultural hegemony, ilustrado, orientalism, postcolonial

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Authors: Carol S. Binnekade, Deon Scott, Christina C. Shuttleworth, Annelien A. Van Rooyen

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Higher education institutions are constantly challenged to deliver skilled graduates into the workplace. Employers expect graduates to have the required technical knowledge as well as various pervasive skills. This also applies to accountants who need to know the technical requirements of financial reporting and be able to communicate with individuals, teams and clients at a high level. Accountants need to develop effective business conversational skills and use these skills to communicate up, down and across organizations, taking into consideration cultural and gender diversity. In addition, they need to master business writing and presentation skills. However, providing students with these skills in a distance-learning environment where interaction between students and instructors is limited, is a challenge for academics. The study on which this paper reports, forms part of a larger body of research, which explored the perceptions of accounting practitioners of the communication skills (or lack thereof) of recently qualified accounting students. Feedback (qualitative and quantitative) was obtained from various accounting practitioners in South Africa. Taking into consideration that distance learners communicate mainly with their instructors via email communication and their assignments are submitted using various word processor software, the researchers were of the opinion that the accounting graduates would be capable of communicating effectively once they entered the workplace. However, the research findings, inter alia, suggested that the accounting graduates lacked communication skills and that training was needed to differentiate between business and social communication once they entered the workplace. Recommendations on how these communication challenges may be addressed by higher education institutions are provided.

Keywords: accounting practitioners, communication skills, distance education, pervasive skills

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Authors: Alvin Uchenna Ugwu

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The United Nation’s 2030 agenda and Global Action Programme (GAP) for implementation of the Sustainable Development Goals (SDGs), has mandated all sectors in the societies, including education, to develop strategies towards actualizing sustainability in all facets of the society, by the year 2030. Education is no doubt a key tool for social change. However, educational institutions in most African nations need a paradigmatic shift to strike a balance between policies (curricular) and practices, with regards to Education for Sustainable Development (ESD). The paradigm shift in this regard is described as whole-institution/school approach. The whole institution approaches advocate action-focused ESD. In other words, ESD policy and curriculum makers, formal and non-formal education institutions, need to ‘practice what they preach’. This paper is developed from an ongoing study carried out by the author and guided by two research questions: -What are the views of intermediate phase science and technology preservice teachers on the ESD content included in the science and technology modules? -What challenges or enable intermediate phase science and technology pre-service teachers to learn about ESD in science and technology modules? The study drew from the views and experiences of preservice science teachers, learning about ESD in a university’s college of education in South Africa. Using qualitative case study research design, the research data were generated via questionnaires and focus group discussions. Analysis of generated data indicates that universities and institutions of higher learning need to demonstrate practical involvement while implementing ESD in societies, rather than just standing as knowledge media. Findings of the study further suggest that natural sciences and technology courses in teacher education programmes and other institutions of higher learning, should be perceived as key transformative tools in shaping the consciousness of students towards integrating and fostering ESD in developing countries such as South Africa. Thus, this paper seeks to promote ‘Whole Institution Involvement’ in teacher education colleges in South Africa, as a measure of improving ESD in higher education settings. The paper suggests that in order to achieve ESD in higher education settings and beyond, policies and practices should be reexamined beyond rhetoric and buzzwords. The paper further argues that implementation of ESD is largely influenced by context, hence two different contexts should be examined empirically.

Keywords: education for sustainable development, higher education institutions, pre-service science teachers, qualitative case study research, whole institution involvement

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Authors: Margarida Maria Cabugueira Csutódio dos Santos, Joana Filipa Pintéus Pereira

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The way children understand the concept of health and illness influences their reaction in contexts where these concepts are present (e.g.,illness; vaccination). The recognition of the importance of children's beliefs/representations about health and disease has led to the development of models that seek to explain the development process of these concepts. In the construction of their representations, children are influenced not only by their cognitive competence but also by their life experiences. In the last 3 years, children have experienced a pandemic health crisis that has exposed them to anomalous and stressful situations. Objective: the aim of this study was (1) to identify children’s representations about disease (including symptoms, causes, control/treatment) and prevention (including health procedures and vaccines) and (2) whether COVID19 is mentioned and influences their representations. Methodology: a qualitative study in which 67 children with 7 to 10 years old (mean 8,8) participated. A semi-structured interview was used following the Bibace and Walsh model, focusing on the representation of the disease and its prevention. Results show a marked influence of the lived experience with regard to causes of the disease, disease control and treatment, and adherence to vaccination. Age-dependent differences were found with older children being able to talk about illness and contamination process and younger displaying more basic, concrete and rigid representations. Conclusions: The results of this study bring clues to the adequacy of communication with the child in the context of health and illness and discriminately in a future health pandemic crisis.

Keywords: childen, health beliefs, pediatrics, covid19, vaccines

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Authors: Mousouris Spiridon, Kavakli Evangelia

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This paper focuses on Digital Libraries (DLs) that contain and geovisualise cultural data, highlighting the need to define them as a separate category termed Cultural Mapping Libraries, based on their inherent connection of culture with geographic location and their design requirements in support of visual representation of cultural data on the map. An exploratory analysis of DLs that conform to the above definition brought forward the observation that existing Cultural Mapping Libraries fail to geovisualise the entirety of cultural data per point of interest thus resulting in a Cultural Snapshot phenomenon. The existence of this phenomenon was reinforced by the results of a systematic bibliographic research. In order to address the Cultural Snapshot, this paper proposes the use of the Semantic Web principles to efficiently interconnect spatial cultural data through time, per geographic location. In this way points of interest are transformed into scenery where culture evolves over time. This evolution is expressed as occurrences taking place chronologically, in an event oriented approach, a conceptualization also endorsed by the CIDOC Conceptual Reference Model (CIDOC CRM). In particular, we posit the use of CIDOC CRM as the baseline for defining the logic of Cultural Mapping Libraries as part of the Culture Domain in accordance with the Digital Library Reference Model, in order to define the rules of cultural data management by the system. Our future goal is to transform this conceptual definition in to inferencing rules that resolve the Cultural Snapshot and lead to a more complete geovisualisation of cultural data.

Keywords: digital libraries, semantic web, geovisualization, CIDOC-CRM

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

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Authors: Alexander G. Yushchenko

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From a multi-science point of view, we analyze threats to security resulting from globalization of international information space and information and communication aggression of Russia. A definition of Ruschism is formulated as an ideology supporting aggressive actions of modern Russia against the Euro-Atlantic community. Stages of the hybrid war Russia is leading against Ukraine are described, including the elements of subversive activity of the special services, the activation of the military phase and the gradual shift of the focus of confrontation to the realm of information and communication technologies. We reveal an emergence of a threat for democratic states resulting from the destabilizing impact of a target state’s mass media and social networks being exploited by Russian secret services under freedom-of-speech disguise. Thus, we underline the vulnerability of cyber- and information security of the network society in regard of hybrid war. We propose to define the latter a synergetic war. Our analysis is supported with a long-term qualitative monitoring of representation of top state officials on popular TV channels and Facebook. From the memetics point of view, we have detected a destructive psycho-information technology used by the Kremlin, a kind of information catastrophe, the essence of which is explained in detail. In the conclusion, a comprehensive plan for information protection of the public consciousness and mentality of Euro-Atlantic citizens from the aggression of the enemy is proposed.

Keywords: cyber and information security, hybrid war, psycho-information technology, synergetic war, Ruschism

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Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Ashley Hobson, Ashley Efaw

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Professionalism is an essential skill for clinicians, particularly for Occupational Therapy Providers (OTPs). The World Federation of Occupational Therapy (WFOT) Guiding Principles for Ethical Occupational Therapy and American Occupational Therapy Association (AOTA) Code of Ethics establishes expectations for professionalism among OTPs, emphasizing its importance in the field. However, the teaching and assessment of professionalism vary across OTP programs. The flexibility provided by the country standards allows programs to determine their own approaches to meeting these standards, resulting in inconsistency. Educators in both academic and fieldwork settings face challenges in objectively assessing and providing feedback on student professionalism. Although they observe instances of unprofessional behavior, there is no standardized assessment measure to evaluate professionalism in OTP students. While most students are committed to learning and applying professionalism skills, they enter OTP programs with varying levels of proficiency in this area. Consequently, they lack a uniform understanding of professionalism and lack an objective means to self-assess their current skills and identify areas for growth. It is crucial to explicitly teach professionalism, have students to self-assess their professionalism skills, and have OTP educators assess student professionalism. This approach is necessary for fostering students' professionalism journeys. Traditionally, there has been no objective way for students to self-assess their professionalism or for educators to provide objective assessments and feedback. To establish a uniform approach to professionalism, the authors incorporated professionalism content into our curriculum. Utilizing an operational definition of professionalism, the authors integrated professionalism into didactic, fieldwork, and capstone courses. The complexity of the content and the professionalism skills expected of students increase each year to ensure students graduate with the skills to practice in accordance with the WFOT Guiding Principles for Ethical Occupational Therapy Practice and AOTA Code of Ethics. Two professionalism assessments were developed based on the expectations outlined in the both documents. The Professionalism Self-Assessment allows students to evaluate their professionalism, reflect on their performance, and set goals. The Professionalism Assessment for Educators is a modified version of the same tool designed for educators. The purpose of this workshop is to provide educators with a framework and tools for assessing student professionalism. The authors discuss how to integrate professionalism content into OTP curriculum and utilize professionalism assessments to provide constructive feedback and equitable learning opportunities for OTP students in academic, fieldwork, and capstone settings. By adopting these strategies, educators can enhance the development of professionalism among OTP students, ensuring they are well-prepared to meet the demands of the profession.

Keywords: professionalism, assessments, student learning, student preparedness, ethical practice

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Authors: Fadila Hadjeris

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The study aims at investigating the functions of English-Arabic code switching in English as a foreign language classroom oral discourse and the extent to which they can contribute to the flow of classroom interaction. It also seeks to understand the views, beliefs, and perceptions of teachers and learners towards this practice. We hypothesized that code switching is a communicative strategy which facilitates classroom interaction. Due to this fact, both teachers and learners support its use. The study draws on a key body of literature in bilingualism, second language acquisition, and classroom discourse in an attempt to provide a framework for considering the research questions. It employs a combination of qualitative and quantitative research methods which include classroom observations and questionnaires. The analysis of the recordings shows that teachers’ code switching to Arabic is not only used for academic and classroom management reasons. Rather, the data display instances in which code switching is used for social reasons. The analysis of the questionnaires indicates that teachers and pupils have different attitudes towards this phenomenon. Teachers reported their deliberate switching during EFL teaching, yet the majority was against this practice. According to them, the use of the mother has detrimental effects on the acquisition and the practice of the target language. In contrast, pupils showed their preference to their teachers’ code switching because it enhances and facilitates their understanding. These findings support the fact that the shift to pupils’ mother tongue is a strategy which aids and facilitates the teaching and the learning of the target language. This, in turn, necessitates recommendations which are suggested to teachers and course designers.

Keywords: bilingualism, codeswitching, classroom interaction, classroom discourse, EFL learning/ teaching, SLA

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Authors: Nursel Bektas

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The purpose of this study is to investigate the roles of teaching assistants (TAs) working in reception classes through practitioners’ perspectives. The study has two major purposes; firstly to explore the general roles of TAs, and secondly to identify their roles in supporting children for mathematics. A small-scale case study approach was adopted for this study. The research was carried out in two reception classes within a primary school in London. The qualitative data were gathered through observations and semi-structured interviews with four reception class practitioners, comprising two teachers and two TAs. The results show that TAs consider their role to be more like a teacher, whereas classroom teachers do not corroborate this and they generally believe that the role of TAs depends on their personal characteristics and skills. In regard to the general role of TAs, the study suggests that reception class TAs are deployed both at the classroom level to provide academic support for children’s learning and development, and at the school level they are deployed as support staff such as Midday Meal Supervisor or assistants. In terms of the pedagogical roles of TAs, it was found that TAs have a strong teaching role in literacy development, with notable autonomy if conducting their own phonics sessions without teacher direction, but a negligible influence in numeracy/ math’s. In addition, the results show that the TA role is perceived to be quite limited in planning and assessment processes. Linked to their limited roles in such processes, all participants agree that all the responsibility regarding the children’s learning and development, planning and assessment lies with the teacher. Therefore, data suggest that TAs’ roles in these areas depend on TAs’ their own initiatives.

Keywords: early years education, reception classes, roles, teaching assistants

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Authors: Chansheng He, Zhongfu Wang, Xiao Bai, Jie Tian, Xin Jin

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Heterogeneity of soil hydraulic properties directly affects hydrological processes at different scales. Understanding heterogeneity of soil hydraulic properties such as soil moisture is therefore essential for modeling watershed ecohydrological processes, particularly in hard to access, topographically complex mountainous watersheds. This study maps spatial variations of soil moisture by in situ observation network that consists of sampling points, zones, and tributaries, and monitors corresponding hydrological variables of air and soil temperatures, evapotranspiration, infiltration, and runoff in the Upper Reach of the Heihe River Watershed, a second largest inland river (terminal lake) with a drainage area of over 128,000 km² in Northwest China. Subsequently, the study uses a hydrological model, SWAT (Soil and Water Assessment Tool) to simulate the effects of heterogeneity of soil moisture on watershed hydrological processes. The spatial clustering method, Full-Order-CLK was employed to derive five soil heterogeneous zones (Configuration 97, 80, 65, 40, and 20) for soil input to SWAT. Results show the simulations by the SWAT model with the spatially clustered soil hydraulic information from the field sampling data had much better representation of the soil heterogeneity and more accurate performance than the model using the average soil property values for each soil type derived from the coarse soil datasets. Thus, incorporating detailed field sampling soil heterogeneity data greatly improves performance in hydrologic modeling.

Keywords: heterogeneity, soil moisture, SWAT, up-scaling

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Xuan Zheng

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China is home to one of the world’s largest Deaf and Hard of Hearing (DHH) populations. In the 1980s, the concept of inclusive education was introduced, giving rise to a unique “learning in regular class (随班就读)” model tailored to local contexts. China’s inclusive education for DHH students is diversifying with innovative models like special education classes at regular schools, regular classes at regular schools, resource classrooms, satellite classes, and bilingual-bimodal projects. The scope extends to preschool and higher education programs. However, the inclusive development of DHH students faces challenges. The prevailing pathological viewpoint on disabilities persists, emphasizing the necessity for favorable auditory and speech rehabilitation outcomes before DHH students can integrate into regular classes. In addition, inadequate support systems in inclusive schools result in poor academic performance and increased psychological disorders among the group, prompting a notable return to special education schools. Looking ahead, China’s inclusive education for DHH students needs a substantial shift from “learning in regular class” to “sharing equal regular education.” Particular attention should be devoted to the effective integration of DHH students who employ sign language into mainstream educational settings. It is crucial to strengthen regulatory frameworks and institutional safeguards, advance the professional development of educators specializing in inclusive education for DHH students, and consistently enhance resources tailored to this demographic. Furthermore, the establishment of a robust, multidimensional, and collaborative support network, engaging both families and educational institutions, is also a pivotal facet.

Keywords: deaf, hard of hearing, inclusive education, China

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Authors: L. Yu, W. K. Li, S. K. Ong, A. Y. C. Nee

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In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.

Keywords: augmented reality framework, server-client model, vision-based tracking, image search

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Lakhoua Najeh

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Aims of the work: After a presentation of the functionality of an example of a mechatronic system which is a paint mixer system, we present the concepts of modeling and safe operation. This paper briefly discusses how to model and protect the functioning of a mechatronic system relying mainly on functional analysis and safe operation techniques. Methods: For the study of an example of a mechatronic system, we use methods for external functional analysis that illustrate the relationships between a mechatronic system and its external environment. Thus, we present the Safe-Structured Analysis Design Technique method (Safe-SADT) which allows the representation of a mechatronic system. A model of operating safety and automation is proposed. This model enables us to use a functional analysis technique of the mechatronic system based on the GRAFCET (Graphe Fonctionnel de Commande des Etapes et Transitions: Step Transition Function Chart) method; study of the safe operation of the mechatronic system based on the Safe-SADT method; automation of the mechatronic system based on a software tool. Results: The expected results are to propose a model and safe operation of a mechatronic system. This methodology enables us to analyze the relevance of the different models based on Safe-SADT and GRAFCET in relation to the control and monitoring functions and to study the means allowing exploiting their synergy. Conclusion: In order to propose a general model of a mechatronic system, a model of analysis, safety operation and automation of a mechatronic system has been developed. This is how we propose to validate this methodology through a case study of a paint mixer system.

Keywords: mechatronic systems, system modeling, safe operation, Safe-SADT

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