Search results for: Collective Intelligence

Authors: Uche Lucy Onyekwelu, Uche B. Ugwuanyi

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Suffice to say that socio-economic disruptions of any form is likely to affect the wellbeing of the citizenry. The upsurge of social disequilibrium caused by the incessant disruptive tendencies exhibited by youths and some others in Nigeria are not helping matters. In Nigeria the social unrest has caused different forms of draw backs in Socio Economic Development. This study has empirically evaluated the impact of insecurity and insurgency on the Economic Development of Nigeria. The paper noted that the different forms of insecurity in Nigeria are namely: Insurgency and Banditry as witnessed in Northern Nigeria; Militancy: Niger Delta area and self-determination groups pursuing various forms of agenda such as Sit –at- Home Syndrome in the South Eastern Nigeria and other secessionist movements. All these have in one way or the other hampered Economic development in Nigeria. Data for this study were collected through primary and secondary sources using questionnaire and some existing documentations. Cost of investment in different aspects of security outfits in Nigeria represents the independent variable while the differentials in the Gross Domestic Product(GDP) and Human Development Index(HDI) are the measures of the dependent variable. Descriptive statistics and Simple Linear Regression analytical tool were employed in the data analysis. The result revealed that Insurgency/Insecurity negatively affect the economic development of the different parts of Nigeria. Following the findings, a model to analyse the effect of insecurity and insurgency was developed, named INSECUREDEVNIG. It implies that the economic development of Nigeria will continue to deteriorate if insurgency and insecurity continue. The study therefore recommends that the government should do all it could to nurture its human capital, adequately fund the state security apparatus and employ individuals of high integrity to manage the various security outfits in Nigeria. The government should also as a matter of urgency train the security personnel in intelligence cum Information and Communications Technology to enable them ensure the effectiveness of implementation of security policies needed to sustain Gross Domestic Product and Human Capital Index of Nigeria.

Keywords: insecurity, insurgency, gross domestic product, human development index, Nigeria

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Authors: Laverne Seales

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When most people think of Panama, they immediately think of the Canal; however, the construction and the people who made it possible are often omitted and seldom acknowledged. The reality is that the construction of this waterway was achieved through forced migration and discriminatory practices toward people of African descent, specifically black people from the Caribbean. From the colonial period to the opening and subsequent operation of the Panama Canal by the United States, this paper goes through the rich layers of Panamanian history to examine the life of Afro-Caribbeans and their descendants in Panama. It also considers the role of the United States in Panama; it explores how the United States in Panama forged a racially complex country that made the integration of Afro-Caribbeans and their descendants difficult. After laying a historical foundation, the exploration of Afro-Caribbean people and Panamanians of Afro-Caribbean descent are analyzed through Afro-Panamanian writer Carlos Guillermo ‘Cubena’ Wilson's novels, short stories, and poetry. This study focuses on how Cubena addresses racism, discrimination, inequality, and social justice issues towards Afro-Caribbeans and their descendants who traveled to Panama to construct the Canal. Content analysis methodology can yield several significant contributions, and analyzing Carlos Guillermo Wilson's literature under this framework allows us to consider social commentary and critique of Panamanian society. It identifies the social issues and concerns of Afro-Caribbeans and people of Afro-Caribbean descent, such as inequality, corruption, racism, political oppression, and cultural identity. Analysis methodology allows us to explore how Cubena's literature engages with questions of cultural identity and belonging in Panamanian society. By examining themes related to race, ethnicity, language, and heritage, this research uncovers the complexities of Panamanian cultural identity, allowing us to interrogate power dynamics and social hierarchies in Panamanian society. Analyzing the portrayal of different social groups, institutions, and power structures helps uncover how power is wielded, contested, and resisted; Cubena's fictional world allows us to see how it functions in Panama. Content analysis methodology also provides for critiquing political systems and governance in Panama. By examining the representation and presentation of political figures, institutions, and events in Cubena's literature, we uncover his commentary on corruption, authoritarianism, governance, and the role of the United States in Panama. Content analysis highlights how Wilson's literature amplifies the voices and experiences of marginalized individuals and communities in Panamanian society. By centering the narratives of Afro-Panamanians and other marginalized groups, this researcher uncovers Cubena's commitment to social justice and inclusion in his writing and helps the reader engage with historical narratives and collective memory in Panama. Overall, analyzing Carlos Guillermo ‘Cubena’ Wilson's literature as a platform for social commentary and critique of Panamanian society using content analysis methodology provides valuable insights into the cultural, social, and political dimensions of Afro-Panamanians during and after the construction of the Panama Canal.

Keywords: Afro-Caribbean, Panama Canal, race, Afro-Panamanian, identity, history

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Samar Swaid

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Today, corporates are moving toward the adoption of Design-Thinking techniques to develop products and services, putting their consumer as the heart of the development process. One of the leading companies in Design-Thinking, IDEO (Innovation, Design, Engineering Organization), defines Design-Thinking as an approach to problem-solving that relies on a set of multi-layered skills, processes, and mindsets that help people generate novel solutions to problems. Design thinking may result in new ideas, narratives, objects or systems. It is about redesigning systems, organizations, infrastructures, processes, and solutions in an innovative fashion based on the users' feedback. Tim Brown, president and CEO of IDEO, sees design thinking as a human-centered approach that draws from the designer's toolkit to integrate people's needs, innovative technologies, and business requirements. The application of design thinking has been witnessed to be the road to developing innovative applications, interactive systems, scientific software, healthcare application, and even to utilizing Design-Thinking to re-think business operations, as in the case of Airbnb. Recently, there has been a movement to apply design thinking to machine learning and artificial intelligence to ensure creating the "wow" effect on consumers. The Association of Computing Machinery task force on Data Science program states that" Data scientists should be able to implement and understand algorithms for data collection and analysis. They should understand the time and space considerations of algorithms. They should follow good design principles developing software, understanding the importance of those principles for testability and maintainability" However, this definition hides the user behind the machine who works on data preparation, algorithm selection and model interpretation. Thus, the Data Science program includes design thinking to ensure meeting the user demands, generating more usable machine learning tools, and developing ways of framing computational thinking. Here, describe the fundamentals of Design-Thinking and teaching modules for data science programs.

Keywords: data science, design thinking, AI, currculum, transformation

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Authors: Matthias Haenisch, Marc Godau, Julia Barreiro, Dominik Maxelon

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In times of ubiquitous digitalization and the increasing entanglement of humans and technologies in musical practices in the 21st century, it is to be asked, how popular musicians learn in the (post)digital Age. Against the backdrop of the increasing interest in transferring informal learning practices into formal settings of music education the interdisciplinary research association »MusCoDA – Musical Communities in the (Post)Digital Age« (University of Erfurt/University of Applied Sciences Clara Hoffbauer Potsdam, funded by the German Ministry of Education and Research, pursues the goal to derive an empirical model of collective songwriting practices from the study of informal lelearningf songwriters and bands that can be translated into pedagogical concepts for music education in schools. Drawing on concepts from Community of Musical Practice and Actor Network Theory, lelearnings considered not only as social practice and as participation in online and offline communities, but also as an effect of heterogeneous networks composed of human and non-human actors. Learning is not seen as an individual, cognitive process, but as the formation and transformation of actor networks, i.e., as a practice of assembling and mediating humans and technologies. Based on video stimulated recall interviews and videography of online and offline activities, songwriting practices are followed from the initial idea to different forms of performance and distribution. The data evaluation combines coding and mapping methods of Grounded Theory Methodology and Situational Analysis. This results in network maps in which both the temporality of creative practices and the material and spatial relations of human and technological actors are reconstructed. In addition, positional analyses document the power relations between the participants that structure the learning process of the field. In the area of online informal lelearninginitial key research findings reveal a transformation of the learning subject through the specific technological affordances of TikTok and Instagram and the accompanying changes in the learning practices of the corresponding online communities. Learning is explicitly shaped by the material agency of online tools and features and the social practices entangled with these technologies. Thus, any human online community member can be invited to directly intervene in creative decisions that contribute to the further compositional and structural development of songs. At the same time, participants can provide each other with intimate insights into songwriting processes in progress and have the opportunity to perform together with strangers and idols. Online Lelearnings characterized by an increase in social proximity, distribution of creative agency and informational exchange between participants. While it seems obvious that traditional notions not only of lelearningut also of the learning subject cannot be maintained, the question arises, how exactly the observed informal learning practices and the subject that emerges from the use of social media as online learning technologies can be transferred into contexts of formal learning

Keywords: informal learning, postdigitality, songwriting, actor-network theory, community of musical practice, social media, TikTok, Instagram, apps

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Authors: Xu Qian

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This abstract examines the concept of resilience in adolescents from both adolescent medicine and child psychology perspectives. It discusses the role of healthcare providers in fostering resilience among adolescents, encompassing physical, psychological, and social aspects. The paper highlights evidence-based interventions and practical strategies for promoting resilience in this population. Introduction: Resilience plays a crucial role in the healthy development of adolescents, enabling them to navigate through the challenges of this transitional period. This abstract explores the concept of resilience from the perspectives of adolescent medicine and child psychology, shedding light on the collective efforts of healthcare providers in fostering resilience. By integrating the principles and practices of these two disciplines, this abstract emphasizes the multidimensional nature of resilience and its significance in the overall well-being of adolescents. Methods: A comprehensive literature review was conducted, encompassing research articles, empirical studies, and expert opinions from both adolescent medicine and child psychology fields. The search included databases such as PubMed, PsycINFO, and Google Scholar, focusing on publications from the past decade. The review aimed to identify evidence-based interventions and practical strategies employed by healthcare providers to promote resilience among adolescents. Results: The review revealed several key findings regarding the promotion of resilience in adolescents. Firstly, resilience is a dynamic process influenced by individual characteristics, environmental factors, and the interaction between the two. Secondly, healthcare providers play a critical role in fostering resilience by addressing the physical, psychological, and social needs of adolescents. This entails comprehensive healthcare services that integrate medical care, mental health support, and social interventions. Thirdly, evidence-based interventions such as cognitive-behavioral therapy, social skills training, and positive youth development programs have shown promising outcomes in enhancing resilience. Discussion: The integration of adolescent medicine and child psychology perspectives provides a comprehensive framework for promoting resilience in adolescents. By acknowledging the interplay between physical health, psychological well-being, and social functioning, healthcare providers can tailor interventions to address the specific needs and challenges faced by adolescents. Collaborative efforts between medical professionals, psychologists, educators, and families are vital in creating a supportive environment that fosters resilience. Additionally, the findings highlight the importance of early identification and intervention, emphasizing the need for routine screening and assessment to identify adolescents at risk and provide timely support. Conclusion: Promoting resilience in adolescents requires a holistic approach that integrates adolescent medicine and child psychology perspectives. By recognizing the multifaceted nature of resilience, healthcare providers can implement evidence-based interventions and practical strategies to enhance the well-being of adolescents. The collaboration between healthcare professionals from different disciplines, alongside the involvement of families and communities, is crucial for creating a resilient support system. By investing in the promotion of resilience during adolescence, we can empower young individuals to overcome adversity and thrive in their journey toward adulthood.

Keywords: psychology, clinical psychology, child psychology, adolescent psychology, adolescent

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Authors: Lufungula Osembe

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The field of education has witnessed advances in technology and digital transformation. The methods of teaching have undergone significant changes in recent years, resulting in effects on various areas such as pedagogies, curriculum design, personalized teaching, gamification, data analytics, cloud-based learning applications, artificial intelligence tools, advanced plug-ins in LMS, and the emergence of multimedia creation and design. The field of education has not been immune to the changes brought about by digital innovation in recent years, similar to other fields such as engineering, health, science, and technology. There is a need to look at the variables/elements that digital innovation brings to education and develop a framework for higher institutions of learning to assess their readiness to create a viable environment for digital innovation to be successfully adopted. Given the potential benefits of digital innovation in education, it is essential to develop a framework that can assist academics and policymakers in higher institutions of learning to evaluate the effectiveness of adopting and adapting to the evolving landscape of digital innovation in education. The primary research question addressed in this study is to establish the preparedness of higher institutions of learning to adopt and adapt to the evolving landscape of digital innovation. This study follows a Design Science Research (DSR) paradigm to develop a framework for academics and policymakers in higher institutions of learning to evaluate the readiness of their institutions to adopt digital innovation in education. The Design Science Research paradigm is proposed to aid in developing a readiness framework for digital innovation in education. This study intends to follow the Design Science Research (DSR) methodology, which includes problem awareness, suggestion, development, evaluation, and conclusion. One of the major contributions of this study will be the development of the framework for digital innovation in education. Given the various opportunities offered by digital innovation in recent years, the need to create a readiness framework for digital innovation will play a crucial role in guiding academics and policymakers in their quest to align with emerging technologies facilitated by digital innovation in education.

Keywords: digital innovation, DSR, education, opportunities, research

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Md. Nurul Alom Siddikqe

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The purpose of this intervention was to describe how the marginalized people protect their rights and increase their self-dignity and self-esteem among brothel-based sex workers in 6 cities which are the victim of trafficked who came from different periphery areas Bangladesh. Eventually the sex workers are tortured by the pimp, clients, Msahi (so called guardian of bonded sex workers), Babu (So called husband) highly discriminated, vulnerable and stigmatized due to their occupation, movement, behavior and activities, which has got social disapproval. However, stigma, discrimination and violation of human rights not only bar them to access legal services, education of their kids, health, movement of outside of brothel, deprived of funeral after death, but also make them inaccessible due to their invisibility. Conducted an assessment among brothel-based sex workers setup to know their knowledge on human rights and find out their harassment and violence in their community. Inspired them to think about to be united and also assisted them to formation of self help group (SHG). Developed capacity of the SHG and developed leadership of its members through different trainings like administrative, financial management, public speaking and resource mobilization. Developed strategy to enhance the capacity of SHG so that they can collectively claim their rights and develop strategic partnership and network with the relevant service provider’s for restoring all sorts of rights. Conducted meeting with stakeholder including duty bearers, civil society organizations, media people and local government initiatives. Developed Networking with human rights commission, local elite, religious leaders and form human right watch committees at community level. Organized rally and observed national and international days along with government counterparts. By utilizing the project resources the members of SHG became capable to raise their collective voices against violence, discrimination and stigma as well as protected them from insecurity. The members of SHG have been participating in social program/event the SHG got membership of district level NGO coordination meeting through invitation from Deputy Commissioner, Civil Surgeon and Social welfare office of Government of Bangladesh. The Law Enforcement Agency is ensuring safety and security and the education department of government enrolled their children in primary level education. The Government provided land for grave yard after death for the Muslim sex workers and same for the other religious group. The SHGs are registered with government respective authorities. The SHGs are working with support from different development partners and implementing different projects sometime as consortium leaders. Opportunity created to take the vocational training from the government reputed department. The harassment by the clients reduced remarkably, babu, Mashi and other counterparts recognized the sex workers rights and ensure security with government counterpart access increased in legal, health and education. Indications are that the brothel based sex workers understood about their rights and became capable of ensuring their security through working under the self-help groups meaningfully.

Keywords: brothel, discrimination, harassment, stigma

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Authors: Mahdi Allaoua Seklab, Süleyman BaşTürk

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In the quest to enhance coastal environmental health, this study introduces a fully autonomous beach cleaning machine, a breakthrough in leveraging green energy and advanced artificial intelligence for ecological preservation. Designed to operate independently, the machine is propelled by a solar-powered system, underscoring a commitment to sustainability and the use of renewable energy in autonomous robotics. The vehicle's autonomous navigation is achieved through a sophisticated integration of LIDAR and a camera system, utilizing an SSD MobileNet V2 object detection model for accurate and real-time trash identification. The SSD framework, renowned for its efficiency in detecting objects in various scenarios, is coupled with the lightweight and precise highly MobileNet V2 architecture, making it particularly suited for the computational constraints of on-board processing in mobile robotics. Training of the SSD MobileNet V2 model was conducted on Google Colab, harnessing cloud-based GPU resources to facilitate a rapid and cost-effective learning process. The model was refined with an extensive dataset of annotated beach debris, optimizing the parameters using the Adam optimizer and a cross-entropy loss function to achieve high-precision trash detection. This capability allows the machine to intelligently categorize and target waste, leading to more effective cleaning operations. This paper details the design and functionality of the beach cleaning machine, emphasizing its autonomous operational capabilities and the novel application of AI in environmental robotics. The results showcase the potential of such technology to fill existing gaps in beach maintenance, offering a scalable and eco-friendly solution to the growing problem of coastal pollution. The deployment of this machine represents a significant advancement in the field, setting a new standard for the integration of autonomous systems in the service of environmental stewardship.

Keywords: autonomous beach cleaning machine, renewable energy systems, coastal management, environmental robotics

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Orit Wolf

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Classical music confronts a crucial challenge: updating cherished concert traditions for the digital age. This paper is a journey, and a quest to make classical concerts resonate with a new generation. It's not just about asking questions; it's about exploring the future of classical concerts and their potential to captivate and connect with today's audience in an era defined by change. The younger generation, known for their love of diversity, interactive experiences, and multi-sensory immersion, cannot be overlooked. This paper explores innovative strategies that forge deep connections with audiences whose relationship with classical music differs from the past. The urgency of this challenge drives the transformation of classical concerts. Examining classical concerts is necessary to understand how they can harmonize with contemporary sensibilities. New dimensions in audiovisual experiences that enchant the emerging generation are sought. Classical music must embrace the technological era while staying open to fusion and cross-cultural collaboration possibilities. The role of technology and Artificial Intelligence (AI) in reshaping classical concerts is under research. The fusion of classical music with digital experiences and dynamic interdisciplinary collaborations breathes new life into the concert experience. It aligns classical music with the expectations of modern audiences, making it more relevant and engaging. Exploration extends to the structure of classical concerts. Conventions are challenged, and ways to make classical concerts more accessible and captivating are sought. Inspired by innovative artistic collaborations, musical genres and styles are redefined, transforming the relationship between performers and the audience. This paper, therefore, aims to be a catalyst for dialogue and a beacon of innovation. A set of critical inquiries integral to reshaping classical concerts for the digital age is presented. As the world embraces digital transformation, classical music seeks resonance with contemporary audiences, redefining the concert experience while remaining true to its roots and embracing revolutions in the digital age.

Keywords: new concert formats, reception of classical music, interdiscplinary concerts, innovation in the new musical era, mash-up, cross culture, innovative concerts, engaging musical performances

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Authors: I. Martinez-Conesa, E. Garcia-Meca, M. Barradas-Quiroz

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The objective of this study is to assess the impact on earnings management of latest two Hofstede cultural dimensions: long-term orientation and indulgence. Long-term orientation represents the alignment of a society towards the future and indulgence expresses the extent to which a society exhibits willingness, or restrain, to realise their impulses. Additionally, this paper tests if there are relevant differences by testing the moderating role of the legal tradition, Continental versus Anglo-Saxon. Our sample comprises 15 countries: Belgium, Canada, Germany, Spain, France, Great Britain, Hong Kong, India, Japan, Korea, Netherlands, Philippines, Portugal, Sweden, and Thailand, with a total of 12,936 observations from 2003 to 2013. Our results show that managers in countries with high levels of long-term orientation reduce their levels of discretionary accruals. The findings do not confirm the effect of indulgence on earnings management. In addition, our results confirm previous literature regarding the effect of individualism, noting that firms in countries with high levels of collectivism might be more inclined to use earnings discretion to protect the welfare of the collective group of firm stakeholders. Uncertainty avoidance results in downwards earnings management as well as high disclosure, suggesting that less manipulation takes place when transparency is higher. Indulgence is the cultural dimension that confronts wellbeing versus survival; dimension is formulated including happiness, the perception of live control and the importance of leisure. Indulgence shows a weak negative correlation with power distance indicating a slight tendency for more hierarchical societies to be less indulgent. Anglo-Saxon countries are a positive effect of individualism and a negative effect of masculinity, uncertainty avoidance, and disclosure. With respect to continental countries, we can see a significant and positive effect of individualism and a significant and negative effect of masculinity, long-term orientation, and indulgence. Therefore, we observe the negative effect on earnings management provoked by higher disclosure and uncertainty avoidance only happens in Anglo-Saxon countries. Meanwhile, the improvement in reporting quality motivated by higher long-term orientation and higher indulgence is dominant in Continental countries. Our results confirm that there is a moderating effect of the legal system in the association between culture and earnings management. This effect is especially relevant in the dimensions related to uncertainty avoidance, long term orientation, indulgence, and disclosure. The negative effect of long-term orientation on earnings management only happens in those countries set in continental legal systems because of the Anglo-Saxon legal systems is supported by the decisions of the courts and the traditions, so it already has long-term orientation. That does not occur in continental systems, depending mainly of contend of the law. Sensitivity analysis used with Jones modified CP model, Jones Standard model and Jones Standard CP model confirm the robustness of these results. This paper collaborates towards a better understanding on how earnings management, culture and legal systems relate to each other, and contribute to previous literature by examining the influence of the two latest Hofstede’s dimensions not previously studied in papers.

Keywords: Hofstede, long-term-orientation, earnings management, indulgence

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Shohoni Mahabub

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Health Information Systems (HIS) are essential to contemporary healthcare; nonetheless, they frequently encounter challenges such as data fragmentation, inefficiencies, and an absence of real-time analytics. The advent of machine learning (ML) and artificial intelligence (AI) provides a revolutionary potential to address these difficulties via AI-driven data fusion ecosystems. These ecosystems integrate many health data sources, including electronic health records (EHRs), wearable devices, and genetic data, with sophisticated machine learning techniques such as natural language processing (NLP) and predictive analytics to produce actionable insights. Through the integration of strong data intake layers, secure interoperability protocols, and privacy-preserving models, these ecosystems provide individualized treatment, early illness diagnosis, and enhanced operational efficiency. This paradigm change enhances clinical decision-making and rectifies systemic inefficiencies in healthcare delivery. Nonetheless, adoption presents problems such as data privacy concerns, ethical considerations, and scalability constraints. The study examines options such as federated learning for safe, decentralized data sharing, explainable AI for transparency, and cloud-based infrastructure for scalability to address these issues. These ecosystems aim to address health equity disparities, particularly in resource-limited environments, and improve public health surveillance, notably in pandemic response initiatives. This article emphasizes the revolutionary potential of AI-driven data fusion ecosystems in redefining Health Information Systems by providing an implementation roadmap and showcasing successful deployment case studies. The suggested method promotes a cooperative initiative among legislators, healthcare professionals, and technology to establish a cohesive, efficient, and patient-centric healthcare model.

Keywords: AI-powered healthcare systems, data fusion ecosystem, predictive analytics, digital health interoperability

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Authors: Naima Mohammadi

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The present paper critically reports on the emergency of Iranian international students in a large public university in Italy. Although the most sizeable diaspora of Iranians dates back to the 1979 revolution, a huge wave of Iranian female students travelled abroad after the Iranian Green Movement (2009) due to the intensification of gender discrimination and Islamization. To explore the experience of Iranian female students at an Italian public university, two complementary methods were adopted: a focus group and individual interviews. Focus groups yield detailed collective conversations and provide researchers with an opportunity to observe the interaction between participants, rather than between participant and researcher, which generates data. Semi-structured interviews allow participants to share their stories in their own words and speak about personal experiences and opinions. Research participants were invited to participate through a public call in a Telegram group of Iranian students. Theoretical and purposive sampling was applied to select participants. All participants were assured that full anonymity would be ensured and they consented to take part in the research. A two-hour focus group was held in English with participants in the presence and some online. They were asked to share their motivations for studying in Italy and talk about their experiences both within and outside the university context. Each of these interviews lasted from 45 to 60 minutes and was mostly carried out online and in Farsi. The focus group consisted of 8 Iranian female post-graduate students. In analyzing the data a blended approach was adopted, with a combination of deductive and inductive coding. According to research findings, although 9/11 was the beginning of the West’s challenges against Muslims, the nuclear threats of Islamic regimes promoted the toughest international sanctions against Iranians as a nation across the world. Accordingly, carrying an Iranian identity contributes to social, political, and economic exclusion. Research findings show that geopolitical factors such as international sanctions and Islamophobia, and a lack of reciprocity in terms of recognition, have created a sense of stigmatization for veiled and unveiled Iranian female students who are the largest groups of ‘non-European Muslim international students’ enrolled in Italian universities. Participants addressed how their nationality has devalued their public image and negatively impacted their self-confidence and self-realization in academia. They highlighted the experience of an unwelcoming atmosphere by different groups of people and institutes, such as receiving marked students’ badges, rejected bank account requests, failed visa processes, secondary security screening selection, and hyper-visibility of veiled students. This study corroborates the need for institutions to pay attention to geopolitical factors and religious diversity in student recruitment and provide support mechanisms and access to basic rights. Accordingly, it is suggested that Higher Education Institutions (HEIs) have a social and moral responsibility towards the discrimination and both social and academic exclusion of Iranian students.

Keywords: Iranian diaspora, female students, recognition theory, inclusive university

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Authors: Mariana Damova, Stanko Stankov, Emil Stoyanov, Hristo Hristov, Hermand Pessek, Plamen Chernev

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We will present one of the first use cases on the DestinE platform, a joint initiative of the European Commission, European Space Agency and EUMETSAT, providing access to global earth observation, meteorological and statistical data, and emphasize the good practice of intergovernmental agencies acting in concert. Further, we will discuss the importance of space-bound disruptive solutions for improving the balance between the ever-increasing water-related disasters coming from climate change and minimizing their economic and societal impact. The use case focuses on forecasting floods and estimating the impact of flood events on the urban environment and the ecosystems in the affected areas with the purpose of helping municipal decision-makers to analyze and plan resource needs and to forge human-environment relationships by providing farmers with insightful information for improving their agricultural productivity. For the forecast, we will adopt an EO4AI method of our platform ISME-HYDRO, in which we employ a pipeline of neural networks applied to in-situ measurements and satellite data of meteorological factors influencing the hydrological and hydrodynamic status of rivers and dams, such as precipitations, soil moisture, vegetation index, snow cover to model flood events and their span. ISME-HYDRO platform is an e-infrastructure for water resources management based on linked data, extended with further intelligence that generates forecasts with the method described above, throws alerts, formulates queries, provides superior interactivity and drives communication with the users. It provides synchronized visualization of table views, graphviews and interactive maps. It will be federated with the DestinE platform.

Keywords: flood simulation, AI, Earth observation, e-Infrastructure, flood forecasting, flood areas localization, response planning, resource estimation

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Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

Abstract:

Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

Procedia PDF Downloads 187

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

Abstract:

Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

Procedia PDF Downloads 361

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 71

Authors: Joy Cao, Min Zhou

Abstract:

Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 90

Authors: Miria Gambardella

Abstract:

The influence of solidarity demonstrations towards the Zapatista National Liberation Army has been constantly present over the years, both locally and internationally, guaranteeing visibility to the cause, shaping the movement’s choices, and influencing its hopes of impact worldwide. Most of the coffee produced by the autonomous cooperatives from Chiapas is exported, therefore making coffee trade the main income from international solidarity networks. The question arises about the implications of the relations established between the communities in resistance in Southeastern Mexico and international solidarity movements, specifically on the strategies adopted to conciliate army's demands for autonomy and economic asymmetries between Zapatista cooperatives producing coffee and European collectives who hold purchasing power. In order to deepen the inquiry on those topics, a year-long multi-site investigation was carried out. The first six months of fieldwork were based in Barcelona, where Zapatista coffee was first traded in Spain and where one of the historical and most important European solidarity groups can be found. The last six months of fieldwork were carried out directly in Chiapas, in contact with coffee producers, Zapatista political authorities, international activists as well as vendors, and the rest of the network implicated in coffee production, roasting, and sale. The investigation was based on qualitative research methods, including participatory observation, focus groups, and semi-structured interviews. The analysis did not only focus on retracing the steps of the market chain as if it could be considered a linear and unilateral process, but it rather aimed at exploring actors’ reciprocal perceptions, roles, and dynamics of power. Demonstrations of solidarity and the money circulation they imply aim at changing the system in place and building alternatives, among other things, on the economic level. This work analyzes the formulation of discourse and the organization of solidarity activities that aim at building opportunities for action within a highly politicized economic sphere to which access must be regularly legitimized. The meaning conveyed by coffee is constructed on a symbolic level by the attribution of moral criteria to transactions. The latter participate in the construction of imaginaries that circulate through solidarity movements with the Zapatista rebellion. Commercial exchanges linked to solidarity networks turned out to represent much more than monetary transactions. The social, cultural, and political spheres are invested by ethics, which penetrates all aspects of militant action. It is at this level that the boundaries of different collective actors connect, contaminating each other: merely following the money flow would have been limiting in order to account for a reality within which imaginary is one of the main currencies. The notions of “trust”, “dignity” and “reciprocity” are repeatedly mobilized to negotiate discontinuous and multidirectional flows in the attempt to balance and justify commercial relations in a politicized context that characterizes its own identity through demonizing “market economy” and its dehumanizing powers.

Keywords: coffee trade, economic anthropology, international cooperation, Zapatista National Liberation Army

Procedia PDF Downloads 88

Authors: Michael N. O'Sullivan, Con Sheahan

Abstract:

Mass customization is the idea of offering custom products or services to satisfy the needs of each individual customer while maintaining the efficiency of mass production. Technologies like 3D printing and artificial intelligence have many start-ups hoping to capitalize on this dream of creating personalized products at an affordable price, and well established companies scrambling to innovate and maintain their market share. However, the majority of them are failing as they struggle to understand one key question – where does customization make sense? Customization and personalization only make sense where the value of the perceived benefit outweighs the cost to implement it. In other words, will people pay for it? Looking at the Kano Model makes it clear that it depends on the product. In products where customization is an inherent need, like prosthetics, mass customization technologies can be highly beneficial. However, for products that already sell as a standard, like headphones, offering customization is likely only an added bonus, and so the product development team must figure out if the customers’ perception of the added value of this feature will outweigh its premium price tag. This can be done through the use of a ‘serious game,’ whereby potential customers are given a limited budget to collaboratively buy and bid on potential features of the product before it is developed. If the group choose to buy customization over other features, then the product development team should implement it into their design. If not, the team should prioritize the features on which the customers have spent their budget. The level of customization purchased can also be translated to an appropriate production method, for example, the most expensive type of customization would likely be free-form design and could be achieved through digital fabrication, while a lower level could be achieved through short batch production. Twenty-five teams of final year students from design, engineering, construction and technology tested this methodology when bringing a product from concept through to production specification, and found that it allowed them to confidently decide what level of customization, if any, would be worth offering for their product, and what would be the best method of producing it. They also found that the discussion and negotiations between players during the game led to invaluable insights, and often decided to play a second game where they offered customers the option to buy the various customization ideas that had been discussed during the first game.

Keywords: Kano model, mass customization, new product development, serious game

Procedia PDF Downloads 135

Authors: Mina Shokry Hanna Saleh Tadros

Abstract:

The centrality of the Petroleum Industry in the world energy is undoubted. The world economy almost runs and depends on petroleum. Petroleum industry is a multi-trillion industry; it turns otherwise poor and underdeveloped countries into wealthy nations and thrusts them at the center of international diplomacy. Although these developing nations lack the necessary technology to explore and exploit petroleum resources they are not without help as developed nations, represented by their multinational corporations are ready and willing to provide both the technical and managerial expertise necessary for the development of this natural resource. However, the exploration of these petroleum resources comes with, sometimes, grave, concomitant consequences. These consequences are especially pronounced with respect to the environment. From the British Petroleum Oil rig explosion and the resultant oil spillage and pollution in New Mexico, United States to the Mobil Oil spillage along Egyptian coast, the story and consequence is virtually the same. Egypt’s delta Region produces Nigeria’s petroleum which accounts for more than ninety-five percent of Nigeria’s foreign exchange earnings. Between 1999 and 2007, Egypt earned more than $400 billion from petroleum exports. Nevertheless, petroleum exploration and exploitation has devastated the Delta environment. From oil spillage which pollutes the rivers, farms and wetlands to gas flaring by the multi-national corporations; the consequences is similar-a region that has been devastated by petroleum exploitation. This paper thus seeks to examine the consequences and impact of petroleum pollution in the Egypt Delta with particular reference on the right of the people of Niger Delta to a healthy environment. The paper further seeks to examine the relevant international, regional instrument and Nigeria’s municipal laws that are meant to protect the result of the people of the Egypt Delta and their enforcement by the Nigerian State. It is quite worrisome that the Egypt Delta Region and its people have suffered and are still suffering grave violations of their right to a healthy environment as a result of petroleum exploitation in their region. The Egypt effort at best is half-hearted in its protection of the people’s right.

Keywords: crude oil, fire, floating roof tank, lightning protection systemenvironment, exploration, petroleum, pollutionDuvernay petroleum system, oil generation, oil-source correlation, Re-Os

Procedia PDF Downloads 81

Authors: Carolina Cuéllar, Paz González

Abstract:

Research has shown that school leadership has a significant indirect effect on students’ achievements. In Chile, evidence has also revealed that this impact is stronger in vulnerable schools. With the aim of strengthening school leadership, public policy has taken up the challenge of enhancing capabilities of novice principals through the implementation of induction programs, which include a mentoring component, entrusting the task of delivering these programs to universities. The importance of using mentoring or coaching models in the preparation of novice school leaders has been emphasized in the international literature. Thus, it can be affirmed that building leadership capacity through partnership is crucial to facilitate cognitive and affective support required in the initial phase of the principal career, gain role clarification and socialization in context, stimulate reflective leadership practice, among others. In Chile, mentoring is a recent phenomenon in the field of school leadership and it is even more new in the preparation of new principals who work in public schools. This study, funded by the Chilean Ministry of Education, sought to explore the challenges and lessons arising from the design and implementation of mentoring processes which are part of the induction programs, according to the perception of the different actors involved: ministerial agents, university coordinators, mentors and novice principals. The investigation used a qualitative design, based on a study of three cases (three induction programs). The sources of information were 46 semi-structured interviews, applied in two moments (at the beginning and end of mentoring). Content analysis technique was employed. Data focused on the uniqueness of each case and the commonalities within the cases. Five main challenges and lessons emerged in the design and implementation of mentoring within the induction programs for new principals from Chilean public schools. They comprised the need of (i) developing a shared conceptual framework on mentoring among the institutions and actors involved, which helps align the expectations for the mentoring component within the induction programs, along with assisting in establishing a theory of action of mentoring that is relevant to the public school context; (ii) recognizing trough actions and decisions at different levels that the role of a mentor differs from the role of a principal, which challenge the idea that an effective principal will always be an effective mentor; iii) improving mentors’ selection and preparation processes trough the definition of common guiding criteria to ensure that a mentor takes responsibility for developing critical judgment of novice principals, which implies not limiting the mentor’s actions to assist in the compliance of prescriptive practices and standards; (iv) generating common evaluative models with goals, instruments and indicators consistent with the characteristics of mentoring processes, which helps to assess expected results and impact; and (v) including the design of a mentoring structure as an outcome of the induction programs, which helps sustain mentoring within schools as a collective professional development practice. Results showcased interwoven elements that entail continuous negotiations at different levels. Taking action will contribute to policy efforts aimed at professionalizing the leadership role in public schools.

Keywords: induction programs, mentoring, novice principals, school leadership preparation

Procedia PDF Downloads 127

Authors: Cristian Păuna

Abstract:

In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

Procedia PDF Downloads 131

Authors: Swadha Singh

Abstract:

International Solar Alliance is the foremost treaty-based global organization concerned with tapping the potential of sun-abundant nations between the Tropics of Cancer and Capricorn and enables co-operation among them. As a founding member of the International Solar Alliance, India exhibits its positioning as an upcoming leader in clean energy. India has set ambitious goals and targets to expand the share of solar in its energy mix and is playing a proactive role both at the regional and global levels. ISA aims to serve multiple goals- bring about scale commercialization of solar power, boost domestic manufacturing, and leverage solar diplomacy in African countries, amongst others. Against this backdrop, this paper attempts to examine the ways in which ISA as an intergovernmental organization under Indian leadership can leverage the cause of clean energy (solar) diplomacy and effectively shape partnerships and collaborations with other developing countries in terms of sharing solar technology, capacity building, risk mitigation, mobilizing financial investment and providing an aggregate market. A more specific focus of ISA is on the developing countries, which in the absence of a collective, are constrained by technology and capital scarcity, despite being naturally endowed with solar resources. Solar rich but finance-constrained economies face political risk, foreign exchange risk, and off-taker risk. Scholars argue that aligning India’s climate change discourse and growth prospects in its engagements, collaborations, and partnerships at the bilateral, multilateral and regional level can help promote trade, attract investments, and promote resilient energy transition both in India and in partner countries. For developing countries, coming together in an action-oriented way on issues of climate and clean energy is particularly important since it is developing and underdeveloped countries that face multiple and coalescing challenges such as the adverse impact of climate change, uneven and low access to reliable energy, and pressing employment needs. Investing in green recovery is agreed to be an assured way to create resilient value chains, create sustainable livelihoods, and help mitigate climate threats. If India is able to ‘green its growth’ process, it holds the potential to emerge as a climate leader internationally. It can use its experience in the renewable sector to guide other developing countries in balancing multiple similar objectives of development, energy security, and sustainability. The challenges underlying solar expansion in India have lessons to offer other developing countries, giving India an opportunity to assume a leadership role in solar diplomacy and expand its geopolitical influence through inter-governmental organizations such as ISA. It is noted that India has limited capacity to directly provide financial funds and support and is not a leading manufacturer of cheap solar equipment, as does China; however, India can nonetheless leverage its large domestic market to scale up the commercialization of solar power and offer insights and learnings to similarly placed abundant solar countries. The paper examines the potential of and limits placed on India’s solar diplomacy.

Keywords: climate diplomacy, energy security, solar diplomacy, renewable energy

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Authors: Keroles Akram Saed Ghatas

Abstract:

The near future will bring significant changes in modern organizations and management due to the growing role of intangible assets and knowledge workers. The area of copyright, intellectual property, digital (intangible) assets and media redistribution appears to be one of the greatest challenges facing business and society in general and management sciences and organizations in particular. The proposed article examines the views and perceptions of fairness in digital media sharing among Harvard Law School's LL.M.s. Students, based on 50 qualitative interviews and 100 surveys. The researcher took an ethnographic approach to her research and entered the Harvard LL.M. in 2016. at, a Face book group that allows people to connect naturally and attend in-person and private events more easily. After listening to numerous students, the researcher conducted a quantitative survey among 100 respondents to assess respondents' perceptions of fairness in digital file sharing in various contexts (based on media price, its availability, regional licenses, copyright holder status, etc.). to understand better . .). Based on the survey results, the researcher conducted long-term, open-ended and loosely structured ethnographic interviews (50 interviews) to further deepen the understanding of the results. The most important finding of the study is that Harvard lawyers generally support digital piracy in certain contexts, despite having the best possible legal and professional knowledge. Interestingly, they are also more accepting of working for the government than the private sector. The results of this study provide a better understanding of how “fairness” is perceived by the younger generation of lawyers and pave the way for a more rational application of licensing laws.

Keywords: cognitive impairments, communication disorders, death penalty, executive function communication disorders, cognitive disorders, capital murder, executive function death penalty, egyptian law absence, justice, political cases piracy, digital sharing, perception of fairness, legal profession

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Authors: Jessica BrodeFrank, Beka Bryer, Lacey Wilson, Sierra Van Ryck deGroot

Abstract:

Museums are enjoying quite the moment in pop culture. From discussions of labor in Bob’s Burger to introducing cultural repatriation in The Black Panther, discussions of various museum issues are making their way to popular media. “Talking Back to Hollywood” analyzes the impact museums have on movies and television. The paper will highlight a series of cultural cameos and discuss what each reveals about critical themes in museums: repatriation, labor, obfuscated histories, institutional legacies, artificial intelligence, and holograms. Using a mixed methods approach to include surveys, descriptive research, thematic analysis, and context analysis, the authors of this paper will explore how we, as the museum staff, might begin to cite museums and movies together as texts. Drawing from their experience working in museums and public history, this contingent of mid-career professionals will highlight the impact museums have had on movies and television and the didactic lessons these portrayals can provide back to cultural heritage professionals. From tackling critical themes in museums such as repatriation, labor conditions/inequities, obfuscated histories, curatorial choice and control, institutional legacies, and more, this paper is grounded in the cultural zeitgeist of the 2000s and the message these media portrayals send to the public and the cultural heritage sector. In particular, the paper will examine how portrayals of AI, holograms, and more technology can be used as entry points for necessary discussions with the public on mistrust, misinformation, and emerging technologies. This paper will not only expose the legacy and cultural understanding of the museum field within popular culture but also will discuss actionable ways that public historians can use these portrayals as an entry point for discussions with the public, citing literature reviews and quantitative and qualitative analysis of survey results. As Hollywood is talking about museums, museums can use that to better connect to the audiences who feel comfortable at the cinema but are excluded from the museum.

Keywords: museums, public memory, representation, popular culture

Procedia PDF Downloads 86