107 research outputs found
Simulation Modelling of Cloud Mini and Mega Data Centers Using Cloud Analyst
Cloud Computing has now become a base technology for various other technologies including Internet of Things, Big Data Technologies and many other technologies, the responsibility of Cloud become critical in case of real time applications where the cloud services are required in real time. Delay in the response from Cloud may lead to serious consequences even loss of lives where the processes data from cloud must reach within predefined time interval. The performance of Cloud has experienced delays with the current infrastructure due to multiple issues in Traditional Cloud Network Model. The Paper suggests a proposed architecture Cloud Mini Data Centers simulated using Cloud Analyst to minimize the delays of Cloud Service delivery. The paper also simulate traditional cloud Network model using Cloud Analyst and provides a comparative study of both models
An optimal scheduling method in iot-fog-cloud network using combination of aquila optimizer and african vultures optimization
Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks. © 2023 by the authors
Application of pre-training and fine-tuning AI models to machine translation: a case study of multilingual text classification in Baidu
With the development of international information technology, we are producing
a huge amount of information all the time. The processing ability of information in
various languages is gradually replacing information and becoming a rarer resource.
How to obtain the most effective information in such a large and complex amount of
multilingual textual information is a major goal of multilingual information
processing.
Multilingual text classification helps users to break the language barrier and
accurately locate the required information and triage information. At the same time,
the rapid development of the Internet has accelerated the communication among users
of various languages, giving rise to a large number of multilingual texts, such as book
and movie reviews, online chats, product introductions and other forms, which
contain a large amount of valuable implicit information and urgently need automated
tools to categorize and process those multilingual texts.
This work describes the Natural Language Process (NLP) sub-task known as
Multilingual Text Classification (MTC) performed within the context of Baidu, a
Chinese leading AI company with a strong Internet base, whose NLP division led the
industry in deep learning technology to go online in Machine Translation (MT) and
search. Multilingual text classification is an important module in NLP machine
translation and a basic module in NLP tasks. It can be applied to many fields, such as
Fake Reviews Detection, News Headlines Categories Classification, Analysis of
positive and negative reviews and so on.
In the following work, we will first define the AI model paradigm of
'pre-training and fine-tuning' in deep learning in the Baidu NLP department. Then
investigated the application scenarios of multilingual text classification. Most of the
text classification systems currently available in the Chinese market are designed for a
single language, such as Alibaba's text classification system. If users need to classify
texts of the same category in multiple languages, they need to train multiple single
text classification systems and then classify them one by one.
However, many internationalized products do not have a single text language,
such as AliExpress cross-border e-commerce business, Airbnb B&B business, etc.
Industry needs to understand and classify users’ reviews in various languages, and
have conducted in-depth statistics and marketing strategy development, and
multilingual text classification is particularly important in this scenario.
Therefore, we focus on interpreting the methodology of multilingual text
classification model of machine translation in Baidu NLP department, and capture
sets of multilingual data of reviews, news headlines and other data for manual
classification and labeling, use the labeling results for fine-tuning of multilingual text
classification model, and output the quality evaluation data of Baidu multilingual text
classification model after fine-tuning. We will discuss if the pre-training and
fine-tuning of the large model can substantially improve the quality and performance
of multilingual text classification.
Finally, based on the machine translation-multilingual text classification model,
we derive the application method of pre-training and fine-tuning paradigm in the
current cutting-edge deep learning AI model under the NLP system and verify the
generality and cutting-edge of the pre-training and fine-tuning paradigm in the deep
learning-intelligent search field.Com o desenvolvimento da tecnologia de informação internacional, estamos
sempre a produzir uma enorme quantidade de informação e o recurso mais escasso já
não é a informação, mas a capacidade de processar informação em cada língua. A
maior parte da informação multilingue é expressa sob a forma de texto. Como obter a
informação mais eficaz numa quantidade tão considerável e complexa de informação
textual multilingue é um dos principais objetivos do processamento de informação
multilingue.
A classificação de texto multilingue ajuda os utilizadores a quebrar a barreira
linguística e a localizar com precisão a informação necessária e a classificá-la. Ao
mesmo tempo, o rápido desenvolvimento da Internet acelerou a comunicação entre
utilizadores de várias línguas, dando origem a um grande número de textos
multilingues, tais como críticas de livros e filmes, chats, introduções de produtos e
outros distintos textos, que contêm uma grande quantidade de informação implícita
valiosa e necessitam urgentemente de ferramentas automatizadas para categorizar e
processar esses textos multilingues.
Este trabalho descreve a subtarefa do Processamento de Linguagem Natural
(PNL) conhecida como Classificação de Texto Multilingue (MTC), realizada no
contexto da Baidu, uma empresa chinesa líder em IA, cuja equipa de PNL levou a
indústria em tecnologia baseada em aprendizagem neuronal a destacar-se em
Tradução Automática (MT) e pesquisa científica. A classificação multilingue de
textos é um módulo importante na tradução automática de PNL e um módulo básico
em tarefas de PNL. A MTC pode ser aplicada a muitos campos, tais como análise de
sentimentos multilingues, categorização de notícias, filtragem de conteúdos
indesejados (do inglês spam), entre outros.
Neste trabalho, iremos primeiro definir o paradigma do modelo AI de 'pré-treino
e afinação' em aprendizagem profunda no departamento de PNL da Baidu. Em
seguida, realizaremos a pesquisa sobre outros produtos no mercado com capacidade
de classificação de texto — a classificação de texto levada a cabo pela Alibaba. Após
a pesquisa, verificamos que a maioria dos sistemas de classificação de texto
atualmente disponíveis no mercado chinês são concebidos para uma única língua, tal como o sistema de classificação de texto Alibaba. Se os utilizadores precisarem de
classificar textos da mesma categoria em várias línguas, precisam de aplicar vários
sistemas de classificação de texto para cada língua e depois classificá-los um a um.
No entanto, muitos produtos internacionalizados não têm uma única língua de
texto, tais como AliExpress comércio eletrónico transfronteiriço, Airbnb B&B
business, etc. A indústria precisa compreender e classificar as revisões dos
utilizadores em várias línguas. Esta necessidade conduziu a um desenvolvimento
aprofundado de estatísticas e estratégias de marketing, e a classificação de textos
multilingues é particularmente importante neste cenário.
Desta forma, concentrar-nos-emos na interpretação da metodologia do modelo
de classificação de texto multilingue da tradução automática no departamento de PNL
Baidu. Colhemos para o efeito conjuntos de dados multilingues de comentários e
críticas, manchetes de notícias e outros dados para classificação manual, utilizamos os
resultados dessa classificação para o aperfeiçoamento do modelo de classificação de
texto multilingue e produzimos os dados de avaliação da qualidade do modelo de
classificação de texto multilingue da Baidu. Discutiremos se o pré-treino e o
aperfeiçoamento do modelo podem melhorar substancialmente a qualidade e o
desempenho da classificação de texto multilingue. Finalmente, com base no modelo
de classificação de texto multilingue de tradução automática, derivamos o método de
aplicação do paradigma de pré-formação e afinação no atual modelo de IA de
aprendizagem profunda de ponta sob o sistema de PNL, e verificamos a robustez e os
resultados positivos do paradigma de pré-treino e afinação no campo de pesquisa de
aprendizagem profunda
La morfología de las urban village en la era de la datificación. El ejemplo de Xiasha Village the Shenzhen
Actualmente, los procesos acelerados de urbanización y ocupación del suelo se han convertido en un fenómeno mundial. El crecimiento de las ciudades ha promovido el progreso de la sociedad, pero también ha producido numerosos problemas. En China, la revitalización de las aldeas urbanas (urban village) consecuencia de los procesos de urbanización necesitan encontrar soluciones urgentemente. En la era de Internet, la revolución de los datos nos proporciona información básica, objetiva, instantánea y dinámica. Toda ella puede ser de gran ayuda para estudiar este problema. En este trabajo primero se aborda una introducción y contextualización a la definición de los urban village chinos, y se estudia la historia de las urban village de Shenzhen. Después, se aborda un análisis a escala macroscópico, mesoscópico y microscópico, tratando de sacar partido a los datos disponibles que caracterizan el funcionamiento de dichas villages para hacer una investigación sobre la forma urbana espacial de las aldeas urbanas de Shenzhen. Finalmente, según los resultados del estudio, se proporciona la estrategia de revitalización de los urban village.Currently, urbanization has become a global phenomenon. It pushes the human society to go forward, but meanwhile has produced numerous social problems. In China, the urban village revitalization coming with urbanization needs to be solved urgently. In the internet era, the generous data provides all-sided, objective, instant and dynamic basic data. They can help us to study this problem. This paper firstly explains the definition of Chinese urban villages, and studies the history of Shenzhen¿s urban villages. And then, from the macroscopic, mesoscopic and microscopic levels, using different tools and other new data to make a research on urban spatial form (morphology) of Shenzhen¿s urban villages. Finally, according to the results of the study, the revitalization strategy of urban villages is provided.Chen, W. (2017). La morfología de las urban village en la era de la datificación. El ejemplo de Xiasha Village the Shenzhen. http://hdl.handle.net/10251/113663TFG
Dynamics of Information Diffusion and Social Sensing
Statistical inference using social sensors is an area that has witnessed
remarkable progress and is relevant in applications including localizing events
for targeted advertising, marketing, localization of natural disasters and
predicting sentiment of investors in financial markets. This chapter presents a
tutorial description of four important aspects of sensing-based information
diffusion in social networks from a communications/signal processing
perspective. First, diffusion models for information exchange in large scale
social networks together with social sensing via social media networks such as
Twitter is considered. Second, Bayesian social learning models and risk averse
social learning is considered with applications in finance and online
reputation systems. Third, the principle of revealed preferences arising in
micro-economics theory is used to parse datasets to determine if social sensors
are utility maximizers and then determine their utility functions. Finally, the
interaction of social sensors with YouTube channel owners is studied using time
series analysis methods. All four topics are explained in the context of actual
experimental datasets from health networks, social media and psychological
experiments. Also, algorithms are given that exploit the above models to infer
underlying events based on social sensing. The overview, insights, models and
algorithms presented in this chapter stem from recent developments in network
science, economics and signal processing. At a deeper level, this chapter
considers mean field dynamics of networks, risk averse Bayesian social learning
filtering and quickest change detection, data incest in decision making over a
directed acyclic graph of social sensors, inverse optimization problems for
utility function estimation (revealed preferences) and statistical modeling of
interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112
The avian dawn chorus across Great Britain: using new technology to study breeding bird song
The avian dawn chorus is a period of high song output performed daily around sunrise during the breeding season. Singing at dawn is of such significance to birds that they remain motivated to do so amid the noise of numerous others. Yet, we still do not fully understand why the dawn chorus exists. Technological advances in recording equipment, data storage and sound analysis tools now enable collection and scrutiny of large acoustic datasets, encouraging research on sound-producing organisms and promoting ‘the soundscape’ as an indicator of ecosystem health. Using an unrivalled dataset of dawn chorus recordings collected during this thesis, I explore the chorus throughout Great Britain with the prospect of furthering our understanding and appreciation of this daily event. I first evaluate the performance of four automated signal recognition tools (‘recognisers’) when identifying the singing events of target species during the dawn chorus, and devise a new ensemble approach that improves detection of singing events significantly over each of the recognisers in isolation. I then examine daily variation in the timing and peak of the chorus across the country in response to minimum overnight temperature. I conclude that cooler temperatures result in later chorus onset and peak the following dawn, but that the magnitude of this effect is greater at higher latitude sites with cooler and less variable overnight temperature regimes. Next, I present evidence of competition for acoustic space during the dawn chorus between migratory and resident species possessing similar song traits, and infer that this may lead either to fine-scale temporal partitioning of song, such that each competitor maintains optimal output, or to one competitor yielding. Finally, I investigate day-to-day attenuation of song during the leaf-out period from budburst through to full-leaf in woodland trees, and establish the potential for climate-driven advances in leaf-out phenology to attenuate song if seasonal singing activity in birds has not advanced to the same degree. I find that gradual attenuation of sound through the leaf-out process is dependent on the height of the receiver, and surmise that current advances in leaf-out phenology are unlikely to have undue effect on song propagation. This project illustrates the advantage of applying new technology to ecological studies of complex acoustic environments, and highlights areas in need of improvement, which is essential if we are to comprehend and preserve our natural soundscapes
Driving Disruption: Assessing Uber\u27s Corporate Identity in the Battle over Driver Classification
This research analyzes the ongoing effort by Uber’s executives to prevent the reclassification of the company’s drivers from independent contractors to employees. Through rhetorical appeals made to customers, regulatory bodies, and drivers themselves, Uber’s executives are attempting to cultivate a corporate identity that portrays the company’s labor practices in a way that adheres to California’s labor laws, namely the “ABC” test for worker classification codified in Assembly Bill 5, while maintaining the company’s ill-gotten reputation as a bastion of Silicon Valley innovation. The success of this posturing hinges on attempts to conflate Uber’s labor practices with equitable social outcomes, publicize narratives that overemphasize and mischaracterize the benefits of flexible work schedules, and co-opt consumerist terminology in its description of drivers’ relation to the company. This piece embarks upon a critical analysis of these strategies, comparing the claims made in public-facing corporate rhetoric with the actual power dynamics that exist between the company and its drivers. If these strategies ultimately prove successful, they may provide a blueprint for future anti-reclassification campaigns waged by Uber throughout the United States. Regardless of whether AB5’s ABC test finds Uber’s drivers to be employees or independent contractors, the decision will be reached with incomplete knowledge of the algorithms that govern driver workflows, which are shielded from the public and regulators alike by intellectual property law. To remedy this uncertainty, I argue for the empowerment of municipal governments to regulate Uber’s operations within their jurisdictions and for regulatory oversight over algorithms that administer systems of labor
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Exploring the use of Artificial Intelligent Systems in STEM Classrooms
Human beings by nature have a predisposition towards learning and the exploration of the natural world. We are intrinsically intellectual and social beings knitted with adaptive cognitive architectures. As Foot (2014) succinctly sums it up: “humans act collectively, learn by doing, and communicate in and via their actions” and they “… make, employ, and adapt tools of all kinds to learn and communicate” and “community is central to the process of making and interpreting meaning—and thus to all forms of learning, communicating, and acting” (p.3). Education remains pivotal in the transmission of social values including language, knowledge, science, technology, and an avalanche of others. Indeed, Science, Technology, Engineering, and Mathematics (STEM) have been significant to the advancement of social cultures transcending every epoch to contemporary times. As Jasanoff (2004) poignantly observed, “the ways in which we know and represent the world (both nature and society) are inseparable from the ways in which we choose to live in it. […] Scientific knowledge [..] both embeds and is embedded in social practices, identities, norms, conventions, discourses, instruments, and institutions” (p.2-3). In essence, science remains both a tacit and an explicit cultural activity through which human beings explore their own world, discover nature, create knowledge and technology towards their progress and existence. This has been possible through the interaction and applications of artifacts, tools, and technologies within the purviews of their environments. The applications of technologies are found across almost every luster of organizational learning especially teacher education, STEM, architecture, manufacturing, and a flurry of others. Thus, human evolution and development are inexplicably linked with education either formally or informally. The 21st century has however seen a surge in the use of artificial intelligence (AI) and digital technologies in education. The proliferation of artificial intelligence and associated technologies are creating new overtures of digital multiculturalism with distinct worldviews of significance to education. For example, learners are demonstrating digital literacy skills and are knowledgeable about AI technologies across every specter of their lives (Bennett et al., 2008). It is also opening new artesian well-springs of educational opportunities and pedagogical applications. This includes mapping new methodological pathways, content creation and curriculum design, career preparations and indeed a seemingly new paradigm shift in teaching STEM.
There is growing scholarly evidence about the use and diffusion of these technologies in K-12 and higher education (Bonk & Graham, 2012; Hew & Brush, 2007; Langer, 2018; Mishra & Koehler, 2006). Some of these include the Sphero robots, Micro Bit, Jill Watson, BrickPi3 Classroom kit, Engino STEM Mechanic, Lego Education WeDo Core Set and Spike. Both educators and learners are using these in STEM programs as well as other education related activities. Just as human activities and interactions with artifacts and tools shaped and redefined the scientific-technological feat of previous generations, so the contemporary digital technological era seems to be on a similar trajectory. However, there is sparsity of empirical scholarship on the pedagogical prospects and effectiveness of artificial intelligence in STEM classrooms. Also, it should be noted that scholarship on how AI impacts pedagogical content knowledge of STEM educators and how learners perceive these technologies are just emerging. In addition, the recent COVID-19 pandemic (Ghandhi et al., 2020; Rasmussen et al., 2020) has unexpectedly created a renewed synergy towards the applications of digital technologies in teaching STEM. In the context of this force majeure (COVID-19), the traditional brick and mortar educational spaces metamorphosed into digital spaces with the applications of many artificial intelligent technologies and resources in the arena of education. This doctoral dissertation study examined these enigmas including how educators use these technologies in STEM classrooms. The study is informed by activity theory or cultural-historical activity theory (Engeström et al., 2007; Hasan et al., 2014; Krinski & Barker, 2009; Oers, 2010; Vygotsky,1987). The study participants will be selected from educators currently integrating artificial intelligent systems and digital technologies in their respective STEM classrooms. Pre-data survey inquiry has shown that many educators were incorporating some forms of AIS into their STEM classrooms.
In view of these, I have explored Sphero educational robots to interrogate the research topic. The Sphero Edu described as a “…STEAM-based toolset that weaves hardware, software, and community engagement to promote 21st century skills. While these skills are absolutely crucial, our edu program goes beyond code by nurturing students’ creativity and ingenuity like no other education program can” (Sphero, April 2020). The Sphero robots also have features and applications for designing and teaching STEM topics such as nature, space science, geometry, and other activities of pedagogical significance. Users could also design and write advanced engineering programs in JavaScript during STEM educational activities formally and outside of the classrooms. In essence, educators and students can learn designing, programming, engineering, mathematics, computational thinking, and hands-on skills reflective of the 21st century.
In brief, the dissertation study research has explored artificial intelligence and emerging technologies and how these could transform and advance teaching and learning of STEM hence the research topic: Exploring the use of Artificial Intelligent Systems in STEM Classrooms. Methodologically, this is a qualitative study through the theoretical frameworks of activity theory as applicable to STEM education. The main research questions are:
1) Given that artificial intelligent systems and digital technologies have been applied in STEM educational domains (content, pedagogy, student learning, assessment). How does the application of AIS and digital technologies impact pedagogy in STEM educational activities?
2) Given that digital technology is transforming contemporary society in every facet. How/What does AIS tell us about how digital technology impacts STEM pedagogy?
Data was collected from the study participants, archival sources, and others for analyses. It is hoped that the findings will inform and address theories of learning and teaching, policy and praxis in science education, teacher preparatory and professional development programs as it relates to STEM classroom
Data and Design: Advancing Theory for Complex Adaptive Systems
Complex adaptive systems exhibit certain types of behaviour that are difficult to predict or understand using reductionist approaches, such as linearization or assuming conditions of optimality. This research focuses on the complex adaptive systems associated with public health. These are noted for being driven by many latent forces, shaped centrally by human behaviour.
Dynamic simulation techniques, including agent-based models (ABMs) and system dynamics (SD) models, have been used to study the behaviour of complex adaptive systems, including in public health. While much has been learned, such work is still hampered by important limitations. Models of complex systems themselves can be quite complex, increasing the difficulty in explaining unexpected model behaviour, whether that behaviour comes from model code errors or is due to new learning. Model complexity also leads to model designs that are hard to adapt to growing knowledge about the subject area, further reducing model-generated insights.
In the current literature of dynamic simulations of human public health behaviour, few focus on capturing explicit psychological theories of human behaviour. Given that human behaviour, especially health and risk behaviour, is so central to understanding of processes in public health, this work explores several methods to improve the utility and flexibility of dynamic models in public health. This work is undertaken in three projects.
The first uses a machine learning algorithm, the particle filter, to augment a simple ABM in the presence of continuous disease prevalence data from the modelled system. It is shown that, while using the particle filter improves the accuracy of the ABM, when compared with previous work using SD with a particle filter, the ABM has some limitations, which are discussed.
The second presents a model design pattern that focuses on scalability and modularity to improve the development time, testability, and flexibility of a dynamic simulation for tobacco smoking. This method also supports a general pattern of constructing hybrid models --- those that contain elements of multiple methods, such as agent-based or system dynamics. This method is demonstrated with a stylized example of tobacco smoking in a human population.
The final line of work implements this modular design pattern, with differing mechanisms of addiction dynamics, within a rich behavioural model of tobacco purchasing and consumption. It integrates the results from a discrete choice experiment, which is a widely used economic method for study human preferences. It compares and contrasts four independent addiction modules under different population assumptions. A number of important insights are discussed: no single module was universally more accurate across all human subpopulations, demonstrating the benefit of exploring a diversity of approaches; increasing the number of parameters does not necessarily improve a module's predictions, since the overall least accurate module had the second highest number of parameters; and slight changes in module structure can lead to drastic improvements, implying the need to be able to iteratively learn from model behaviour
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