1,582 research outputs found

    Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

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    Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ā€˜panopticonā€™. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each studentā€™s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parentsā€™ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ā€˜data practicesā€™ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ā€˜cookedā€™. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering studentsā€™ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    Desired sensory branding strategies in-store versus online: the skincare industry

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    Modern shoppers are inundated with purchasing options in every product category, with thousands of brands competing for their patronage. It has therefore become increasingly important for organisations to differentiate product offerings in the market if they want to be competitive. It has further been highlighted that an individualā€™s experience of a brand is of paramount importance, as it is directly linked to brand loyalty. A vehicle for creating memorable brand experiences is the utilisation of multi-sensory experiences or sensory branding. Within the context of traditional or in-store shopping, sensory branding encompasses the use of visual, auditory, olfactory, tactile and gustatory stimuli to adjust consumer purchasing behaviour. However, more and more consumers are opting for online shopping, spurred on by the effects of the global COVID-19 pandemic, and are no less demanding of brands online than they would be in-store. The cosmetics and personal care industry is one of the more predominant gainers from e-commerce. The skincare industry exhibited one of the largest growth rates from 2019 ā€“ 2025 and had an estimated market value of 155.8billionin2022.WhenconsideringtheSouthAfricanskincareindustryinisolation,thereisnoexception,categorisedbyhighaveragegrowthratesandmanycompetitiveplayersinthemarket.ThisisapparentwhenconsideringthattheskincareindustrywithinSouthAfricaisexpctedtogrowannuallyby5.48155.8 billion in 2022. When considering the South African skincare industry in isolation, there is no exception, categorised by high average growth rates and many competitive players in the market. This is apparent when considering that the skincare industry within South Africa is expcted to grow annually by 5.48% from 2023 to 2027, translating to an industry value of 788.4 million by 2027 (Statista 2023). With reference to in-store shopping for skincare products, sensory marketing strategies have been known to be heavily relied on. Therefore, with consumers moving towards online shopping, it is essential for skincare businesses to consider how to deliver sensory experiences online as well as in-store. Whilst the importance of the use of sensory branding and marketing in the skincare industry is notable, both in-store and online, it was established that while there is research available on sensory branding, there is very limited academic research on digital sensory branding and the sensory branding of v skincare products. Moreover, to the researcherā€™s knowledge, no academic literature specifically investigates the digital sensory branding of skincare brands. Therefore, this study will contribute not only by adding academic research to the topic being investigated but also through rreccomendations made based on the outcomes of this study to skincare brands in South Africa. From the comprehensive literature review, a conceptual model was constructed to investigate the relationship between traditional and digital sensory branding strategies (independent variables) and brand loyalty (dependent variable). Two sets of hypotheses were formulated relating to the identified variables of this study and the empirical research conducted was utilised to deduce whether these hypotheses should be rejected or supported. To conduct the empirical research needed for this study, certain research methodology was employed. This study made use of a positivistic paradigm and a quantitative approach. The target population of this study constituted consumers who had purchased skincare products in-store as well as online and, as no true sample frame existed, respondents were selected through the use of non-probability sampling, more specifically, convenience sampling. To collect the data, an online survey was used, with the specific data collection instrument being a web-based self-administered questionnaire, which was distributed via social media platforms, such as Facebook and LinkedIn, as well as via email. Section A of the questionnaire focused on the demographic details of the respondents, while Section B ā€“ Section F related to the variables of the study. A total of 372 potential respondents started the questionnaire, however only 321 questionnaires were deemed usable after the data had been coded and cleaned, indicating a response rate of 86.3%. This study made use of both descriptive (measures of central tendency as well as standard deviation and skewness) and inferential (SEM Models, Primary Models, Pearsonā€™s correlation coefficients, Chi-Square test of Association, ANOVAs and Welch Robust test, Tukey test and Games Howell Test as well as Cohenā€™s d) statistics to interpret the data, which was graphically illustrated. vi The empirical investigation conducted in this study between the variables and sub-variables revealed that significant relationships exist between traditional sensory branding strategies (traditional olfactory and tactile stimuli) and digital sensory branding strategies (digital visual, olfactory and tactile stimuli) and brand loyalty, with refence to the skincare industry. It was further notable that, with specific reference to the skincare industry, the sense of sight, smell and touch are key factors for sensory branding, whereas auditory stimuli were found to only be useful when used in unison with the other senses. Moreover, with reference to in-store shopping, it was deduced that consumers shop for skincare mostly via retail outlets, which could lead to sensory overload. Furthermore, the results of this study suggest that younger consumers are price sensitive. Based on the pertinent empirical results, and corresponding literature findings, of this study, recommendations were provided to businesses operating in the skincare industry. With reference to in-store trading, it was recommended that because skincare is mostly sold via retail outlets, the brand itself does not have control over all sensory stimuli to which the consumer is exposed. As a result, consumers may be subject to sensory overload and skincare brands should keep their sensory branding in-store simple. Moreover, skincare brands could make use of an in-store aesthetician or beautician, which would facilitate consumer-product interaction. With regards to online trading, a recommendation for skincare brands would be to use moving images or GIFs, which will allow the consumer to more easily imagine the feel of the product. Moreover, skincare brands can make use of brand ambassadors to create ā€œunboxingā€ videos, which will convey more clearly the sensory information of the product and instil confidence in consumers. Reccomendations were also made with reference to the financial state of consumers, as the financial position of the respondents could influence their decision making. The limitations of this study comprised the availability of reliable existing sources to support the study as the concept of digital sensory branding is still relatively new and, due to the study being focused on the skincare industry, taste stimuli were excluded as they were found to have no relevance. Finally, vii based on all the literature findings and empirical results, recommendations for future areas of study were made. This study provides evidence that both traditional and digital sensory branding strategies have an influence on, or relationship with, brand loyalty. Through this study, the importance of sensory branding, with specific reference to the skincare industry, is brought to light. Furthermore, skincare brands can utilise the information provided to improve the experience of their consumers when shopping in-store, as well as online, thereby increasing their base of brand loyal consumers.Thesis (PhD) -- Faculty of Business and Economic Sciences, 202

    Innovation in Energy Security and Long-Term Energy Efficiency ā…”

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    The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency

    A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

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    People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

    Handbook Transdisciplinary Learning

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    What is transdisciplinarity - and what are its methods? How does a living lab work? What is the purpose of citizen science, student-organized teaching and cooperative education? This handbook unpacks key terms and concepts to describe the range of transdisciplinary learning in the context of academic education. Transdisciplinary learning turns out to be a comprehensive innovation process in response to the major global challenges such as climate change, urbanization or migration. A reference work for students, lecturers, scientists, and anyone wanting to understand the profound changes in higher education

    Data ethics : building trust : how digital technologies can serve humanity

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    Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume ā€œCyber Ethics 4.0ā€ published in 2018 by the same editors
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