Search results for: gaze metrics

Authors: Eirini Konstanta, Cedric Gouedard, Aggeliki Delimitsou, Stefania Patera, Samuel Murray

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Introduction: Quality reference DNA standards (QRS) for molecular testing by next-generation sequencing (NGS) are essential for accurate quantitation of copy number variations (CNV) for germline and variant allelic frequencies (VAF) for somatic analyses. Objectives: Presently, several molecular analytics for oncology patients are reliant upon quantitative metrics. Test validation and standardisation are also reliant upon the availability of surrogate control materials allowing for understanding test LOD (limit of detection), sensitivity, specificity. We have developed a dual calibration platform allowing for QRS pairs to be included in analysed DNA samples, allowing for accurate quantitation of CNV and VAF metrics within and between patient samples. Methods: QRS™ blocks up to 500nt were designed for common NGS panel targets incorporating ≥ 2 identification tags (IDTDNA.com). These were analysed upon spiking into gDNA, somatic, and ctDNA using a proprietary CalSuite™ platform adaptable to common LIMS. Results: We demonstrate QRS™ calibration reproducibility spiked to 5–25% at ± 2.5% in gDNA and ctDNA. Furthermore, we demonstrate CNV and VAF within and between samples (gDNA and ctDNA) with the same reproducibility (± 2.5%) in a clinical sample of lung cancer and HBOC (EGFR and BRCA1, respectively). CNV analytics was performed with similar accuracy using a single pair of QRS calibrators when using multiple single targeted sequencing controls. Conclusion: Dual paired QRS™ calibrators allow for accurate and reproducible quantitative analyses of CNV, VAF, intrinsic sample allele measurement, inter and intra-sample measure not only simplifying NGS analytics but allowing for monitoring clinically relevant biomarker VAF across patient ctDNA samples with improved accuracy.

Keywords: calibrator, CNV, gene copy number, VAF

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Authors: Lina Psarra, Manogj Sundaresan, Purjeet Sutar

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In response to the global imperative for sustainable solutions, this article proposes the development of a comprehensive circularity index applicable to a wide range of products across various industries. The absence of a consensus on using a universal metric to assess circularity performance presents a significant challenge in prioritizing and effectively managing sustainable initiatives. This circularity index serves as a quantitative measure to evaluate the adherence of products, processes, and systems to the principles of a circular economy. Unlike traditional distinct metrics such as recycling rates or material efficiency, this index considers the entire lifecycle of a product in one single metric, also incorporating additional factors such as reusability, scarcity of materials, reparability, and recyclability. Through a systematic approach and by reviewing existing metrics and past methodologies, this work aims to address this gap by formulating a circularity index that can be applied to diverse product portfolio and assist in comparing the circularity of products on a scale of 0%-100%. Project objectives include developing a formula, designing and implementing a pilot tool based on the developed Product Circularity Index (PCI), evaluating the effectiveness of the formula and tool using real product data, and assessing the feasibility of integration into various sustainability initiatives. The research methodology involves an iterative process of comprehensive research, analysis, and refinement where key steps include defining circularity parameters, collecting relevant product data, applying the developed formula, and testing the tool in a pilot phase to gather insights and make necessary adjustments. Major findings of the study indicate that the PCI provides a robust framework for evaluating product circularity across various dimensions. The Excel-based pilot tool demonstrated high accuracy and reliability in measuring circularity, and the database proved instrumental in supporting comprehensive assessments. The PCI facilitated the identification of key areas for improvement, enabling more informed decision-making towards circularity and benchmarking across different products, essentially assisting towards better resource management. In conclusion, the development of the Product Circularity Index represents a significant advancement in global sustainability efforts. By providing a standardized metric, the PCI empowers companies and stakeholders to systematically assess product circularity, track progress, identify improvement areas, and make informed decisions about resource management. This project contributes to the broader discourse on sustainable development by offering a practical approach to enhance circularity within industrial systems, thus paving the way towards a more resilient and sustainable future.

Keywords: circular economy, circular metrics, circularity assessment, circularity tool, sustainable product design, product circularity index

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Authors: Sergey V. Vinogradov

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Coastal communities are presently facing increased vulnerabilities due to rising sea levels and shifts in global climate patterns, a trend expected to escalate in the long run. To address the needs of government entities, the public sector, and private enterprises, there is an urgent need to thoroughly investigate, assess, and manage the present and projected risks associated with coastal flooding, including storm surges, sea level rise, and nuisance flooding. In response to these challenges, a practical approach to evaluating storm surge inundation risks has been developed. This methodology offers an integrated assessment of potential flood risk in targeted coastal areas. The physical modeling framework involves simulating synthetic storms and utilizing hydrodynamic models that align with projected future climate and ocean conditions. Both publicly available and site-specific data form the basis for a risk assessment methodology designed to translate inundation model outputs into statistically significant projections of expected financial and operational consequences. This integrated approach produces measurable indicators of impacts stemming from floods, encompassing economic and other dimensions. By establishing connections between the frequency of modeled flood events and their consequences across a spectrum of potential future climate conditions, our methodology generates probabilistic risk assessments. These assessments not only account for future uncertainty but also yield comparable metrics, such as expected annual losses for each inundation event. These metrics furnish stakeholders with a dependable dataset to guide strategic planning and inform investments in mitigation. Importantly, the model's adaptability ensures its relevance across diverse coastal environments, even in instances where site-specific data for analysis may be limited.

Keywords: climate, coastal, surge, risk

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Virginie Hergault, Siham Chebbah, Bertrand Frere

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Following in-house objectives, Central laboratory of Paris police Prefecture conducted a general review on models and Computational Fluid Dynamics (CFD) codes used to simulate pollutant dispersion in the atmosphere. Starting from that review and considering main features of Large Eddy Simulation, Central Laboratory Of Paris Police Prefecture (LCPP) postulates that the Fire Dynamics Simulator (FDS) model, from National Institute of Standards and Technology (NIST), should be well suited for air pollutant dispersion modeling. This paper focuses on the implementation and the evaluation of FDS in the frame of the European COST ES1006 Action. This action aimed at quantifying the performance of modeling approaches. In this paper, the CUTE dataset carried out in the city of Hamburg, and its mock-up has been used. We have performed a comparison of FDS results with wind tunnel measurements from CUTE trials on the one hand, and, on the other, with the models results involved in the COST Action. The most time-consuming part of creating input data for simulations is the transfer of obstacle geometry information to the format required by SDS. Thus, we have developed Python codes to convert automatically building and topographic data to the FDS input file. In order to evaluate the predictions of FDS with observations, statistical performance measures have been used. These metrics include the fractional bias (FB), the normalized mean square error (NMSE) and the fraction of predictions within a factor of two of observations (FAC2). As well as the CFD models tested in the COST Action, FDS results demonstrate a good agreement with measured concentrations. Furthermore, the metrics assessment indicate that FB and NMSE meet the tolerance acceptable.

Keywords: numerical simulations, atmospheric dispersion, cost ES1006 action, CFD model, cute experiments, wind tunnel data, numerical results

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Authors: Berk Ecer, Ebru Akcapinar Sezer

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Traditional management models of intersections, such as no-light intersections or signalized intersection, are not the most effective way of passing the intersections if the vehicles are intelligent. To this end, Dresner and Stone proposed a new intersection control model called Autonomous Intersection Management (AIM). In the AIM simulation, they were examining the problem from a multi-agent perspective, demonstrating that intelligent intersection control can be made more efficient than existing control mechanisms. In this study, autonomous intersection management has been investigated. We extended their works and added a potential-based lane organization layer. In order to distribute vehicles evenly to each lane, this layer triggers vehicles to analyze near lanes, and they change their lane if other lanes have an advantage. We can observe this behavior in real life, such as drivers, change their lane by considering their intuitions. Basic intuition on selecting the correct lane for traffic is selecting a less crowded lane in order to reduce delay. We model that behavior without any change in the AIM workflow. Experiment results show us that intersection performance is directly connected with the vehicle distribution in lanes of roads of intersections. We see the advantage of handling lane management with a potential approach in performance metrics such as average delay of intersection and average travel time. Therefore, lane management and intersection management are problems that need to be handled together. This study shows us that the lane through which vehicles enter the intersection is an effective parameter for intersection management. Our study draws attention to this parameter and suggested a solution for it. We observed that the regulation of AIM inputs, which are vehicles in lanes, was as effective as contributing to aim intersection management. PLO-AIM model outperforms AIM in evaluation metrics such as average delay of intersection and average travel time for reasonable traffic rates, which is in between 600 vehicle/hour per lane to 1300 vehicle/hour per lane. The proposed model reduced the average travel time reduced in between %0.2 - %17.3 and reduced the average delay of intersection in between %1.6 - %17.1 for 4-lane and 6-lane scenarios.

Keywords: AIM project, autonomous intersection management, lane organization, potential-based approach

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Authors: Zhen Sun

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The study adopts the method of visual social semiotics to analyze a sample of fashion photos that were recently published in Chinese fashion magazines that target towards both male and female readers. It identifies a new visual trend in fashion photography, which is characterized by two features. First, the photos represent young, confident, and stylish female models with lower-class sloppy old men. The visual inharmony between the sexually desirable women and the aged men has suggested an impossibly accomplished sexuality and eroticism. Though the women are still under the male gaze, they are depicted as unreachable objects of voyeurism other than sexual objects subordinated to men. Second, the represented people are usually put in the backdrop of tasteless or vulgar Chinese town life, which is congruent with the images of men but makes the modern city girls out of place. The photographers intentionally contrast the images of women with that of men and with the background, which implies an imaginary binary division of modern Orientalism and the photographers’ self-orientalization strategy. Under the theoretical umbrella of neoliberal postfeminism, this study defines a new kind of gynocentric stereotype in Chinese fashion photography, which challenges the previous observations on gender portrayals in fashion magazines.

Keywords: fashion photography, gynocentrism, neoliberal postfeminism, self-orientalization

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Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto

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Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT

Keywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start

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Authors: Jolie M. Martin

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Running experiments while logging detailed user actions has become the standard way of testing product features at Pinterest, as at many other Internet companies. While this technique offers plenty of statistical power to assess the effects of product changes on behavioral metrics, it does not often give us much insight into why users respond the way they do. By combining at-scale experiments with smaller surveys of users in each experimental condition, we have developed a unique approach for measuring the impact of our product and communication treatments on user sentiment, attitudes, and comprehension.

Keywords: experiments, methodology, surveys, user experience

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Authors: M. K. Benhaoua, A. E. H. Benyamina, T. Djeradi, P. Boulet

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In this paper, we propose adaptive routing that considers the routing of communications in order to optimize the overall performance. The routing technique uses a newly proposed Algorithm to route communications between the tasks. The routing we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed routing approach provides significant performance improvements when compared to those using static routing.

Keywords: multi-processor systems-on-chip (mpsocs), network-on-chip (noc), heterogeneous architectures, adaptive routin

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Authors: Invernizzi Agostina

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From the 1930s, it is possible to find a phenomenon that persists through to the sixties in the national filmographies of different southern latitudes (Spain, Mexico, Argentina): the proliferation of ensemble films of groups of women who serve base to elaborate broader social conflicts and to construct imaginaries of the nation and of genders. This paper will address the modes of figuration of some affective imaginaries among women where the forms of sociability and the bonds of sisterhood are determined by the spaces in which the women are grouped. In these films, there are forms of affectivity that dispute the meanings of the patriarchal order of the time. One of the hypotheses is that these films formulate communities of women that carry out a reconfiguration of affective and transnational spaces. This research presents a multidisciplinary approach that simultaneously combines film and audiovisual studies, gender studies, decolonial feminist theories, and affects theories. The study of this phenomenon will provide us with keys for articulating with current problematics, such as the genealogies of women's movements, of which the cinema offers echoes and is a privileged medium for reflection and social change, as well as the international contact flows between these three geographical points, their migratory processes and cultural exchanges, transnationalism and integration.

Keywords: affects, feminisms, film studies, gender

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: M. K. Benhaoua, A. K. Singh, A. E. H. Benyamina

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In this paper, we propose heuristic for dynamic communications mapping that considers the placement of communications in order to optimize the overall performance. The mapping technique uses a newly proposed Algorithm to place communications between the tasks. The placement we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed mapping approach provides significant performance improvements when compared to those using static routing.

Keywords: Multi-Processor Systems-on-Chip (MPSoCs), Network-on-Chip (NoC), heterogeneous architectures, dynamic mapping heuristics

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Authors: Lakshay Kharbanda, Aabhas Chauhan

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In this paper, an application of Normalized Compression Distance (NCD) to detect notable scene alterations occurring in videos is presented. Several research groups have been developing methods to perform image classification using NCD, a computable approximation to Normalized Information Distance (NID) by studying the degree of similarity in images. The timeframes where significant aberrations between the frames of a video have occurred have been identified by obtaining a threshold NCD value, using two compressors: LZMA and BZIP2 and defining scene alterations using Pixel Difference Percentage metrics.

Keywords: image compression, Kolmogorov complexity, normalized compression distance, root mean square error

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Authors: Uriel Martinez-Hernandez, Luke W. Boorman, Hamideh Kerdegari, Tony J. Prescott

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In this paper, we present architecture for the study of telepresence, immersion and human-robot interaction. The architecture is built around a wearable interface, developed here, that provides the human with visual, audio and tactile feedback from a remote location. We have chosen to interface the system with the iCub humanoid robot, as it mimics many human sensory modalities, such as vision, with gaze control and tactile feedback. This allows for a straightforward integration of multiple sensory modalities, but also offers a more complete immersion experience for the human. These systems are integrated, controlled and synchronised by an architecture developed for telepresence and human-robot interaction. Our wearable interface allows human participants to observe and explore a remote location, while also being able to communicate verbally with humans located in the remote environment. Our approach has been tested from local, domestic and business venues, using wired, wireless and Internet based connections. This has involved the implementation of data compression to maintain data quality to improve the immersion experience. Initial testing has shown the wearable interface to be robust. The system will endow humans with the ability to explore and interact with other humans at remote locations using multiple sensing modalities.

Keywords: telepresence, telerobotics, human-robot interaction, virtual reality

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Authors: Ozge Nilay Yalcin

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Dialogue has been proposed to be the natural basis for the human-computer interaction, which is behaviorally rich and includes different modalities such as gestures, posture changes, gaze, para-linguistic parameters and linguistic context. However, equipping the system with these capabilities might have consequences on the usability of the system. One issue is to be able to find a good balance between rich behavior and fluent behavior, as planning and generating these behaviors is computationally expensive. In this work, we propose a multi-modal dialogue management system that automates the conversational flow from text-based dialogue examples and uses synchronized verbal and non-verbal conversational cues to achieve a fluent interaction. Our system is integrated with Smartbody behavior realizer to provide real-time interaction with embodied agent. The nonverbal behaviors are used according to turn-taking behavior, emotions, and personality of the user and linguistic analysis of the dialogue. The verbal behaviors are responsive to the emotional value of the utterance and the feedback from the user. Our system is aimed for online planning of these affective multi-modal components, in order to achieve enhanced user experience with richer and more natural interaction.

Keywords: affect, embodied conversational agents, human-agent interaction, multimodal interaction, natural interfaces

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Authors: Meenal Badki

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We address the issue of active feature determination, where the objective is to determine the set of examples on which additional data (such as lab tests) needs to be gathered, given a large number of examples with some features (such as demographics) and some examples with all the features (such as the complete Electronic Health Record). We note that certain features may be more costly, unique, or laborious to gather. Our proposal is a general active learning approach that is independent of classifiers and similarity metrics. It allows us to identify examples that differ from the full data set and obtain all the features for the examples that match. Our comprehensive evaluation shows the efficacy of this approach, which is driven by four authentic clinical tasks.

Keywords: feature determination, classification, active learning, sample-efficiency

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Authors: H. K. Bonsu-Owu

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Media portrayal of women in traditional stereotypical roles such as mothers, or seductress has been the norm for years. However, the changing socioeconomic and political environment and advancement of women in today’s society have given rise to questions on the appropriate portrayal of women in the media today. The purpose of the study is to analyze the portrayal of women in Ghanaian newspapers and find women’s perception on the issue. The study uses a three step approach in gathering data for analysis. Using the stratified sampling method, it analyzes front page images of women from 210 issues of the selected newspapers. Further, it administers questionnaires to 100 female students to find out how they relate to the images of women in the selected newspapers. Finally, editors of the newspapers are interviewed to find their rational for portraying women as seen on their front pages. The findings suggest that the newspapers portray women for varied reasons such as promoting sales and influencing the public agenda. Further, the female students claim that in spite of women’s vast contribution to the growth of society, the media continue to marginalize them. They add that such portrayals promote and reinforce social construct, however, refuse to see themselves through the male gaze concept. The study concludes that the stereotyped portrayal of women is likely to continue if the government, regulatory bodies, the media and society do not make a conscious effort to address this problem.

Keywords: women, newspaper, portrayal, social construct

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Authors: Xiaoyu Shen, Fang Fang, Chujun Qiu

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Herewith we present a Fourier method for credit risk quantification and allocation in the factor-copula model framework. The key insight is that, compared to directly computing the cumulative distribution function of the portfolio loss via Monte Carlo simulation, it is, in fact, more efficient to calculate the transformation of the distribution function in the Fourier domain instead and inverting back to the real domain can be done in just one step and semi-analytically, thanks to the popular COS method (with some adjustments). We also show that the Euler risk allocation problem can be solved in the same way since it can be transformed into the problem of evaluating a conditional cumulative distribution function. Once the conditional or unconditional cumulative distribution function is known, one can easily calculate various risk metrics. The proposed method not only fills the niche in literature, to the best of our knowledge, of accurate numerical methods for risk allocation but may also serve as a much faster alternative to the Monte Carlo simulation method for risk quantification in general. It can cope with various factor-copula model choices, which we demonstrate via examples of a two-factor Gaussian copula and a two-factor Gaussian-t hybrid copula. The fast error convergence is proved mathematically and then verified by numerical experiments, in which Value-at-Risk, Expected Shortfall, and conditional Expected Shortfall are taken as examples of commonly used risk metrics. The calculation speed and accuracy are tested to be significantly superior to the MC simulation for real-sized portfolios. The computational complexity is, by design, primarily driven by the number of factors instead of the number of obligors, as in the case of Monte Carlo simulation. The limitation of this method lies in the "curse of dimension" that is intrinsic to multi-dimensional numerical integration, which, however, can be relaxed with the help of dimension reduction techniques and/or parallel computing, as we will demonstrate in a separate paper. The potential application of this method has a wide range: from credit derivatives pricing to economic capital calculation of the banking book, default risk charge and incremental risk charge computation of the trading book, and even to other risk types than credit risk.

Keywords: credit portfolio, risk allocation, factor copula model, the COS method, Fourier method

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

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Authors: Klára Gajdošíková

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The aim of this study was to examine the quiet eye method and its components using the mobile eye tracking device. Quiet eye training was proven to be beneficial for different sports, including golf. The main idea of this method is to prolong your fixations on a specific place in order to improve your performance. The shot examined in this study is called putt. Its importance is based on its role on a golf course because, many times, it is the last putt that decides whether you win or lose. Quiet eye training helps players be more focused under pressure, control their attention and overall improve their putting success. Six highly skilled golfers with a handicap ranging from - 4 to + 4, aged 23 to 26, participated in a pilot study with the usage of an eye-tracking device. The study took place in an indoor training area at the golf club Hostivař. Crosstabs showed significant differences between individuals' laterality and their gaze into AOI - areas of interest (middle part of the ball, top of the ball, bottom of the ball, back side of the ball). Statistically significant differences were also discovered between the mean fixation duration of participants with AOI on the middle part of the ball and all other AOIs. The results of this study helped us understand the examined phenomena and showed us the next aim in future quiet eye research. Future research should focus on examining a quiet eye on the golf course. Applying a quiet eye and therefore changing the way we concentrate might be beneficial for coaches and players themselves.

Keywords: eye tracking, golf, laterality, quiet eye

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Fatemeh Deldarabdolmaleki, Mohamad Fakri Zaky Bin Ja'afar

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This paper presents fenestration analysis to study the balance between utilizing daylight and eliminating the disturbing parameters in a private office room with interior venetian blinds taking into account different slat angles. Mean luminance of the scene and window, luminance ratio of the workplane and window, work plane illumination and daylight glare probability(DGP) were calculated as a function of venetian blind design properties. Recently developed software, analyzing High Dynamic Range Images (HDRI captured by CCD camera), such as radiance based evalglare and hdrscope help to investigate luminance-based metrics. A total of Eight-day measurement experiment was conducted to investigate the impact of different venetian blind angles in an office environment under daylight condition in Serdang, Malaysia. Detailed result for the selected case study showed that artificial lighting is necessary during the morning session for Malaysian buildings with southwest windows regardless of the venetian blind’s slat angle. However, in some conditions of afternoon session the workplane illuminance level exceeds the maximum illuminance of 2000 lx such as 10° and 40° slat angles. Generally, a rising trend is discovered toward mean window luminance level during the day. All the conditions have less than 10% of the pixels exceeding 2000 cd/m² before 1:00 P.M. However, 40% of the selected hours have more than 10% of the scene pixels higher than 2000 cd/m² after 1:00 P.M. Surprisingly in no blind condition, there is no extreme case of window/task ratio, However, the extreme cases happen for 20°, 30°, 40° and 50° slat angles. As expected mean window luminance level is higher than 2000 cd/m² after 2:00 P.M for most cases except 60° slat angle condition. Studying the daylight glare probability, there is not any DGP value higher than 0.35 in this experiment, due to the window’s direction, location of the building and studied workplane. Specifically, this paper reviews different blind angle’s response to the suggested metrics by the previous standards, and finally conclusions and knowledge gaps are summarized and suggested next steps for research are provided. Addressing these gaps is critical for the continued progress of the energy efficiency movement.

Keywords: daylighting, office environment, energy simulation, venetian blind

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Authors: K. M. Anusha, R. Krishnaveni

Abstract:

Crossing a decade of the 21st century, the paradigm of human resources can be seen evolving with the strategic gene induced into it. There seems to be a radical shift descending as the corporate sector calls on its HR team to become strategic rather than administrative. This transferal eventually requires the metrics employed by these HR teams not to be just operationally reactive but to be aligned to an evidence-based strategic thinking. Realizing the growing need for a prescriptive metric model for effective HR analytics, this study has designed a conceptual framework for a basic metric model that can assist IT-HRM professionals to transition to a practice of evidence-based decision-making to enhance organizational performance.

Keywords: metric model, evidence based HR, HR analytics, strategic HR practices, IT sector

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Authors: Rosy Saikia, Krishna Barua

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The evolution of visual art can be traced back from “pre-historic” humans, from the age of Paleolithic, Neolithic, and Metal Age. Mesopotamians and the Egyptians were the pioneers of art, in the first period of history. But the field of art first flourished in the west during the Renaissance. Since then, art represents a continuous yet varied tradition till present day. Until the early 19th century art focused chiefly on representational, religious and classical motifs, but gradually art became more abstract and conceptual approaches gained popularity. Rainer Maria Rilke (1875-1926) was one of the leading poets cum art critic of European modernism. This paper addresses the relationship between Rilke's poetry and visual art, which involves an intimate transference of aesthetic means and definitions of form in the creative process of writing. Rilke’s connection with Auguste Rodin made him learn that a person who can “see” things could realize the beauty of a “thing” and could subsequently write. To “see” the “thing” or “object” rather than representing emotion was considered as more important by Rodin and that was the way he cracked the old aesthetic mould. Rilke himself agreed that his correspondence with the artists made him acquainted nothing but a new way of seeing. Rilke admitted to the constant reference to the Bible, the books of the Danish poet Jens Peter Jacobsen and Auguste Rodin, who all had given him the experience of the essence of creativity, its depths and eternity. Rilke’s association with philosophers such as Nietzhche and artists, starting from Worpweders and Rodin to Cezanne’s paintings, made him almost an apprentice in visual art.

Keywords: seeing, gaze, aesthetic, beauty, visual art, Rilke

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Authors: Rit M., Girard R., Villot J., Thorel M.

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In a scenario of extensive residential housing renovation, stakeholders need models that support decision-making through a deep understanding of the existing building stock and accurate energy demand simulations. To address this need, we have modified an optimization model using open data that enables the study of renovation strategies at both territorial and national scales. This approach provides (1) a definition of a strategy to simplify decision trees from theoretical combinations, (2) input to decision makers on real-world renovation constraints, (3) more reliable identification of energy-saving measures (changes in technology or behaviour), and (4) discrepancies between currently planned and actually achieved strategies. The main contribution of the studies described in this document is the geographic scale: all residential buildings in the areas of interest were modeled and simulated using national data (geometries and attributes). These buildings were then renovated, when necessary, in accordance with the environmental objectives, taking into account the constraints applicable to each territory (number of renovations per year) or at the national level (renovation of thermal deficiencies (Energy Performance Certificates F&G)). This differs from traditional approaches that focus only on a few buildings or archetypes. This model can also be used to analyze the evolution of a building stock as a whole, as it can take into account both the construction of new buildings and their demolition or sale. Using specific case studies of French territories, this paper highlights a significant discrepancy between the strategies currently advocated by decision-makers and those proposed by our optimization model. This discrepancy is particularly evident in critical metrics such as the relationship between the number of renovations per year and achievable climate targets or the financial support currently available to households and the remaining costs. In addition, users are free to seek optimizations for their building stock across a range of different metrics (e.g., financial, energy, environmental, or life cycle analysis). These results are a clear call to re-evaluate existing renovation strategies and take a more nuanced and customized approach. As the climate crisis moves inexorably forward, harnessing the potential of advanced technologies and data-driven methodologies is imperative.

Keywords: residential housing renovation, MILP, energy demand simulations, data-driven methodology

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Authors: William Luke Hamel

Abstract:

Existing conceptions of urban flooding caused by tidal fluctuations and sea-level rise have been inadequately conceptualized by metrics of resilience and methods of flow modeling. While a great deal of research has been devoted to the effects of urbanization on pluvial flooding, the kind of tidal flooding experienced by locations like Hampton Roads, Virginia, has not been adequately conceptualized as being a result of human factors such as urbanization and gray infrastructure. Resilience from sea level rise and its associated flooding has been pioneered in the region with the 2015 Norfolk Resilience Plan from 100 Resilient Cities as well as the 2016 Norfolk Vision 2100 plan, which envisions different patterns of land use for the city. Urban resilience still conceptualizes the city as having the ability to maintain an equilibrium in the face of disruptions. This economic and social equilibrium relies on the Elizabeth River, narrowly conceptualized. Intentionally or accidentally, the river was made to be a piece of infrastructure. Its development was meant to serve the docks, shipyards, naval yards, and port infrastructure that gives the region so much of its economic life. Inasmuch as it functions to permit the movement of cargo; the raising and lowering of ships to be repaired, commissioned, or decommissioned; or the provisioning of military vessels, the river as infrastructure is functioning properly. The idea that the infrastructure is malfunctioning when high tides and sea-level rise create flooding is predicated on the idea that the infrastructure is truly a human creation and can be controlled. The natural flooding cycles of an urban river, combined with the action of climate change and sea-level rise, are only abnormal so much as they encroach on the development that first encroached on the river. The urban political ecology of water provides the ability to view the river as an infrastructural extension of urban networks while also calling for its emancipation from stationarity and human control. Understanding the river and city as a hydrosocial territory or as a socio-natural system liberates both actors from the duality of the natural and the social while repositioning river flooding as a normal part of coexistence on a floodplain. This paper argues for the adoption of an urban political ecology lens in the analysis and governance of urban rivers like the Elizabeth River as a departure from the equilibrium-seeking and stability metrics of urban resilience.

Keywords: urban flooding, political ecology, Elizabeth river, Hampton roads

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

Procedia PDF Downloads 499

Authors: Oluwaseun Adeleke, Samuel O. Ikani, Fidelis Edet, Anthony Nwala, Mopelola Raji, Simeon Christian Chukwu

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Introduction: Digital innovations have been useful in supporting a client’s contraceptive user journey from awareness to method initiation. The concept of contraceptive self-care is being promoted globally as a means for achieving universal access to quality contraceptive care; however, information about this approach is limited. An important determinant of the scale of awareness is the message construct, choice of information channel, and an understanding of the socio-epidemiological dynamics within the target audience. Significant gains have been made recently in expanding the awareness base of DMPA-SC -a relatively new entrant into the family planning method mix. The cornerstone of this success is a multichannel promotion campaign themed Discover your Power (DYP). The DYP campaign combines content marketing across select social media platforms, chatbots, Cyber-IPC, Interactive Voice Response (IVR), and radio campaigns. Methodology: During implementation, the project monitored predefined metrics of awareness and intent, such as the number of persons reached with the messages, the number of impressions, and meaningful engagement (link-clicks). Metrics/indicators are extracted through native insight/analytics tools across the various platforms. The project also enlists community mobilizers (CMs) who go door-to-door and engage WRA to advertise DISC’s online presence and support them to engage with IVR, digital companion (chatbot), Facebook page, and DiscoverYourPower website. Results: The result showed that the digital platforms recorded 242 million impressions and reached 82 million users with key DMPA-SC self-injection messaging in the three countries. As many as 3.4 million persons engaged (liked, clicked, shared, or reposted) digital posts -an indication of intention. Conclusion: Digital solutions and innovations are gradually becoming the archetype for the advancement of the self-care agenda. Digital innovations can also be used to increase awareness and normalize contraceptive self-care behavior amongst women of reproductive age if they are made an integral part of reproductive health programming.

Keywords: digital transformation, health systems, DMPA-SC, family planning, self-care

Procedia PDF Downloads 71