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My Blog: http://congnghemayblog.blogspot.com/
http://congnghemay123.blogspot.com/
T畛 kh坦a t狸m ki畉m ti li畛u : Wash jeans garment washing and dyeing, ti li畛u ngnh may, purpose of washing, definition of garment washing, ti li畛u c畉t may, s董 mi nam n畛, thi畉t k畉 叩o s董 mi nam, thi畉t k畉 qu畉n 但u, thi畉t k畉 veston nam n畛, thi畉t k畉 叩o di, ch但n v叩y 畉m li畛n th但n, zipper, d但y k辿o trong ngnh may, ti li畛u ngnh may, kh坦a k辿o rng c動a, tri畛n khai s畉n xu畉t, jacket nam, ph但n lo畉i kh坦a k辿o, tin h畛c ngnh may, bi gi畉ng Accumark, Gerber Accumarkt, cad/cam ngnh may, ti li畛u ngnh may, b畛 ti li畛u k畛 thu畉t ngnh may d畉ng 畉y 畛, v畉t li畛u may, ti li畛u ngnh may, ti li畛u v畛 s畛i, nguy棚n li畛u d畛t, ki畛u d畛t v畉i d畛t thoi, ki畛u d畛t v畉i d畛t kim, ch畛 may, v畉t li畛u d畛ng, b畛 ti li畛u k畛 thu畉t ngnh may d畉ng 畉y 畛, ti棚u chu畉n k畛 thu畉t 叩o s董 mi nam, ti li畛u k畛 thu畉t ngnh may, ti li畛u ngnh may, ngu畛n g畛c v畉i denim, l畛ch s畛 ra 畛i v ph叩t tri畛n qu畉n jean, Levi's, Jeans, Levi Straus, Jacob Davis v Levis Strauss, CH畉T LI畛U DENIM, c畉t may qu畉n t但y nam, quy tr狸nh may 叩o s董 mi cn b畉n, qu畉n nam kh担ng ply, thi畉t k畉 叩o s董 mi nam, thi畉t k畉 叩o s董 mi nam theo ti li畛u k畛 thu畉t, ti li畛u c畉t may,l畛ch s畛 ra 畛i v ph叩t tri畛n qu畉n jean, v畉i denim, Levis strauss cha 畉 c畛a qu畉n jeans. Jeans skinny, street style 叩o s董 mi nam, t鱈nh v畉i may 叩o qu畉n, s董 mi nam n畛, c畉t may cn b畉n, thi畉t k畉 qu畉n 叩o, ti li畛u ngnh may,m叩y 2 kim, m叩y may c担ng nghi畛p, two needle sewing machine, ti li畛u ngnh may, thi畉t b畛 ngnh may, m叩y m坦c ngnh may,Ti畉ng anh ngnh may, english for gamrment technology, anh vn chuy棚n ngnh may, may m畉c th畛i trang, english, picture, Nh畉n bi畉t v ph但n bi畛t c叩c lo畉i v畉i, cotton, chiffon, silk, woolCCH MAY QUY CCH L畉P RP QUY CCH NH S畛TI LI畛U K畛 THU畉T NGNH MAY TIU CHU畉N K畛 THU畉T QUY CCH NH S畛 - QUY CCH L畉P RP QUY CCH MAY QUY TRNH MAY G畉P X畉P NG GI
The 11th International Multidisciplinary Research Conference in Education, Tourism, Environmental Science and Technology with theme: Leveraging Sustainable climate Through Education
Digital Transformation and Governance - SSS2024Loc Nguyen
油
Digital transformation is very important in modern society and government, which connects infrastructure and superstructure. Vietnam Government is doing the best job.
Tutorial on deep transformer (presentation slides)Loc Nguyen
油
Development of transformer is a far progressive step in the long journeys of both generative artificial intelligence (GenAI) and statistical translation machine (STM) with support of deep neural network (DNN), in which STM can be known as interesting result of GenAI because of encoder-decoder mechanism for sequence generation built in transformer. But why is transformer being preeminent in GenAI and STM? Firstly, transformer has a so-called self-attention mechanism that discovers contextual meaning of every token in sequence, which contributes to reduce ambiguousness. Secondly, transformer does not concern ordering of tokens in sequence, which allows to train transformer from many parts of sequences in parallel. Thirdly, the third reason which is result of the two previous reasons is that transformer can be trained from large corpus with high accuracy as well as highly computational performance. Moreover, transformer is implemented by DNN which is one of important and effective approaches in artificial intelligence (AI) in recent time. Although transformer is preeminent because of its good consistency, it is not easily understandable. Therefore, this technical report aims to describe transformer with explanations which are as easily understandable as possible.
Tutorial on deep generative model (slides)Loc Nguyen
油
Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. This technical report focuses on deep generative model (DGM) for digital content generation, which is a short summary of approaches to implement DGMs. Researchers can read this work as an introduction to DGM with easily understandable explanations.
Inspirational message: Artificial general intelligenceLoc Nguyen
油
Artificial general intelligence
International Interdisciplinary Research Conference (IIRC) 2024
EduHeart Book Publishing & Training and Development Services, 4th August 2024, Philippines
Adversarial Variational Autoencoders to extend and improve generative model -...Loc Nguyen
油
Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, I try to unify VAE and GAN into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is a good data generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.
Conditional mixture model and its application for regression modelLoc Nguyen
油
Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regression model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept adaptive of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In orther words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.
Keywords: expectation maximization (EM) algorithm, finite mixture model, conditional mixture model, regression model, adaptive regression model (ARM).
A Novel Collaborative Filtering Algorithm by Bit Mining Frequent ItemsetsLoc Nguyen
油
Collaborative filtering (CF) is a popular technique in recommendation study. Concretely, items which are recommended to user are determined by surveying her/his communities. There are two main CF approaches, which are memory-based and model-based. I propose a new CF model-based algorithm by mining frequent itemsets from rating database. Hence items which belong to frequent itemsets are recommended to user. My CF algorithm gives immediate response because the mining task is performed at offline process-mode. I also propose another so-called Roller algorithm for improving the process of mining frequent itemsets. Roller algorithm is implemented by heuristic assumption The larger the support of an item is, the higher its likely that this item will occur in some frequent itemset. It models upon doing white-wash task, which rolls a roller on a wall in such a way that is capable of picking frequent itemsets. Moreover I provide enhanced techniques such as bit representation, bit matching and bit mining in order to speed up recommendation process. These techniques take advantages of bitwise operations (AND, NOT) so as to reduce storage space and make algorithms run faster.
Simple image deconvolution based on reverse image convolution and backpropaga...Loc Nguyen
油
Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.
Technological Accessibility: Learning Platform Among Senior High School StudentsLoc Nguyen
油
The document discusses technological accessibility among senior high school students. It covers several topics:
- Returning to nature through technology and ensuring technology accessibility is a life skill strengthened by advantages while avoiding problems for youth.
- Solutions to the dilemma of technology accessibility include letting youth develop independently, using security controls, and encouraging compassion.
- Researching career is optional but following passion and balancing positive and negative emotions can contribute to success.
The document discusses how engineering and technology can be used for social impact. It makes three key points:
1. Technology stems from knowledge and science, but assessing the value of knowledge is difficult. However, the importance of technology is clear through its fruits. Education fosters gaining knowledge and leads to technological development.
2. While technology increases wealth, it also widens inequality gaps. However, technology can indirectly address inequality through education by providing universal access to learning and bringing universities to people everywhere.
3. Diversity among countries should be embraced, and late innovations building on existing technologies can be particularly impactful, as was the strategy of a racing champion who overtook opponents in the final round through close observation and
Harnessing Technology for Research EducationLoc Nguyen
油
The document discusses harnessing technology for research and education. It notes some paradoxes of technology, such as how it can both increase inequality but also help solve it through education. It discusses the future of education being supported by distance learning tools, AI, and virtual/augmented reality. Open universities are seen as an intermediate step towards "home universities" and as a place where students can become lifelong learners and teachers. Understanding, emotion, and compassion are discussed as being more important than just remembering facts. The document ends by discussing connecting different subjects and technologies like a jigsaw puzzle.
Future of education with support of technologyLoc Nguyen
油
The document discusses the future of education with the support of technology. It notes that while technology deepens inequality by benefiting the rich, it can indirectly address inequality through education by allowing anyone to access knowledge and study from anywhere. Emerging forms of educational support include distance learning using tools like Zoom, artificial intelligence assistants, and virtual/augmented reality. Blended teaching combining different methods is emphasized. Understanding is seen as more important than memorization, and education should foster understanding, emotional development, and compassion through nature-based activities. The document argues technology can help education promote love by connecting various topics through both fusion and by assembling them like a jigsaw puzzle.
The document discusses generative artificial intelligence (AI) and its applications, using the metaphor of a dragon's flight. It introduces digital generative AI models that can generate images, sounds, and motions. As an example, it describes a model that could generate possible orbits or movements for a dragon and tiger in an image along with a bamboo background. The document then discusses how generative AI could be applied creatively in education to generate new learning materials and methods. It argues that while technology and societies are changing rapidly, climate change is a unifying challenge that nations must work together to overcome.
Adversarial Variational Autoencoders to extend and improve generative modelLoc Nguyen
油
Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, I try to unify VAE and GAN into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.
Learning dyadic data and predicting unaccomplished co-occurrent values by mix...Loc Nguyen
油
Dyadic data which is also called co-occurrence data (COD) contains co-occurrences of objects. Searching for statistical models to represent dyadic data is necessary. Fortunately, finite mixture model is a solid statistical model to learn and make inference on dyadic data because mixture model is built smoothly and reliably by expectation maximization (EM) algorithm which is suitable to inherent spareness of dyadic data. This research summarizes mixture models for dyadic data. When each co-occurrence in dyadic data is associated with a value, there are many unaccomplished values because a lot of co-occurrences are inexistent. In this research, these unaccomplished values are estimated as mean (expectation) of random variable given partial probabilistic distributions inside dyadic mixture model.
Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.
Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on theory of optimization which is the base of support vector machine.
The 11th International Multidisciplinary Research Conference in Education, Tourism, Environmental Science and Technology with theme: Leveraging Sustainable climate Through Education
Digital Transformation and Governance - SSS2024Loc Nguyen
油
Digital transformation is very important in modern society and government, which connects infrastructure and superstructure. Vietnam Government is doing the best job.
Tutorial on deep transformer (presentation slides)Loc Nguyen
油
Development of transformer is a far progressive step in the long journeys of both generative artificial intelligence (GenAI) and statistical translation machine (STM) with support of deep neural network (DNN), in which STM can be known as interesting result of GenAI because of encoder-decoder mechanism for sequence generation built in transformer. But why is transformer being preeminent in GenAI and STM? Firstly, transformer has a so-called self-attention mechanism that discovers contextual meaning of every token in sequence, which contributes to reduce ambiguousness. Secondly, transformer does not concern ordering of tokens in sequence, which allows to train transformer from many parts of sequences in parallel. Thirdly, the third reason which is result of the two previous reasons is that transformer can be trained from large corpus with high accuracy as well as highly computational performance. Moreover, transformer is implemented by DNN which is one of important and effective approaches in artificial intelligence (AI) in recent time. Although transformer is preeminent because of its good consistency, it is not easily understandable. Therefore, this technical report aims to describe transformer with explanations which are as easily understandable as possible.
Tutorial on deep generative model (slides)Loc Nguyen
油
Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. This technical report focuses on deep generative model (DGM) for digital content generation, which is a short summary of approaches to implement DGMs. Researchers can read this work as an introduction to DGM with easily understandable explanations.
Inspirational message: Artificial general intelligenceLoc Nguyen
油
Artificial general intelligence
International Interdisciplinary Research Conference (IIRC) 2024
EduHeart Book Publishing & Training and Development Services, 4th August 2024, Philippines
Adversarial Variational Autoencoders to extend and improve generative model -...Loc Nguyen
油
Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, I try to unify VAE and GAN into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is a good data generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.
Conditional mixture model and its application for regression modelLoc Nguyen
油
Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regression model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept adaptive of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In orther words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.
Keywords: expectation maximization (EM) algorithm, finite mixture model, conditional mixture model, regression model, adaptive regression model (ARM).
A Novel Collaborative Filtering Algorithm by Bit Mining Frequent ItemsetsLoc Nguyen
油
Collaborative filtering (CF) is a popular technique in recommendation study. Concretely, items which are recommended to user are determined by surveying her/his communities. There are two main CF approaches, which are memory-based and model-based. I propose a new CF model-based algorithm by mining frequent itemsets from rating database. Hence items which belong to frequent itemsets are recommended to user. My CF algorithm gives immediate response because the mining task is performed at offline process-mode. I also propose another so-called Roller algorithm for improving the process of mining frequent itemsets. Roller algorithm is implemented by heuristic assumption The larger the support of an item is, the higher its likely that this item will occur in some frequent itemset. It models upon doing white-wash task, which rolls a roller on a wall in such a way that is capable of picking frequent itemsets. Moreover I provide enhanced techniques such as bit representation, bit matching and bit mining in order to speed up recommendation process. These techniques take advantages of bitwise operations (AND, NOT) so as to reduce storage space and make algorithms run faster.
Simple image deconvolution based on reverse image convolution and backpropaga...Loc Nguyen
油
Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.
Technological Accessibility: Learning Platform Among Senior High School StudentsLoc Nguyen
油
The document discusses technological accessibility among senior high school students. It covers several topics:
- Returning to nature through technology and ensuring technology accessibility is a life skill strengthened by advantages while avoiding problems for youth.
- Solutions to the dilemma of technology accessibility include letting youth develop independently, using security controls, and encouraging compassion.
- Researching career is optional but following passion and balancing positive and negative emotions can contribute to success.
The document discusses how engineering and technology can be used for social impact. It makes three key points:
1. Technology stems from knowledge and science, but assessing the value of knowledge is difficult. However, the importance of technology is clear through its fruits. Education fosters gaining knowledge and leads to technological development.
2. While technology increases wealth, it also widens inequality gaps. However, technology can indirectly address inequality through education by providing universal access to learning and bringing universities to people everywhere.
3. Diversity among countries should be embraced, and late innovations building on existing technologies can be particularly impactful, as was the strategy of a racing champion who overtook opponents in the final round through close observation and
Harnessing Technology for Research EducationLoc Nguyen
油
The document discusses harnessing technology for research and education. It notes some paradoxes of technology, such as how it can both increase inequality but also help solve it through education. It discusses the future of education being supported by distance learning tools, AI, and virtual/augmented reality. Open universities are seen as an intermediate step towards "home universities" and as a place where students can become lifelong learners and teachers. Understanding, emotion, and compassion are discussed as being more important than just remembering facts. The document ends by discussing connecting different subjects and technologies like a jigsaw puzzle.
Future of education with support of technologyLoc Nguyen
油
The document discusses the future of education with the support of technology. It notes that while technology deepens inequality by benefiting the rich, it can indirectly address inequality through education by allowing anyone to access knowledge and study from anywhere. Emerging forms of educational support include distance learning using tools like Zoom, artificial intelligence assistants, and virtual/augmented reality. Blended teaching combining different methods is emphasized. Understanding is seen as more important than memorization, and education should foster understanding, emotional development, and compassion through nature-based activities. The document argues technology can help education promote love by connecting various topics through both fusion and by assembling them like a jigsaw puzzle.
The document discusses generative artificial intelligence (AI) and its applications, using the metaphor of a dragon's flight. It introduces digital generative AI models that can generate images, sounds, and motions. As an example, it describes a model that could generate possible orbits or movements for a dragon and tiger in an image along with a bamboo background. The document then discusses how generative AI could be applied creatively in education to generate new learning materials and methods. It argues that while technology and societies are changing rapidly, climate change is a unifying challenge that nations must work together to overcome.
Adversarial Variational Autoencoders to extend and improve generative modelLoc Nguyen
油
Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, I try to unify VAE and GAN into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.
Learning dyadic data and predicting unaccomplished co-occurrent values by mix...Loc Nguyen
油
Dyadic data which is also called co-occurrence data (COD) contains co-occurrences of objects. Searching for statistical models to represent dyadic data is necessary. Fortunately, finite mixture model is a solid statistical model to learn and make inference on dyadic data because mixture model is built smoothly and reliably by expectation maximization (EM) algorithm which is suitable to inherent spareness of dyadic data. This research summarizes mixture models for dyadic data. When each co-occurrence in dyadic data is associated with a value, there are many unaccomplished values because a lot of co-occurrences are inexistent. In this research, these unaccomplished values are estimated as mean (expectation) of random variable given partial probabilistic distributions inside dyadic mixture model.
Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.
Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on theory of optimization which is the base of support vector machine.
1. ASEAN relations
Prof. Dr. Loc Nguyen, PhD, Postdoc
Loc Nguyens Academic Network, Vietnam
Email: ng_phloc@yahoo.com
Homepage: www.locnguyen.net
ASEAN relations - Loc Nguyen
2/15/2024 1
2. ASEAN relations
C畉m 董n 達 cho ph辿p t担i n棚u suy ngh挑 c畛a m狸nh v畛 l挑nh v畛c quan h畛 qu畛c t畉
n董i m t担i ang nghi棚n c畛u v h畛c t畉p. Trong th畛i gian ng畉n ng畛i c滴ng nh動
s畛 gi畛i h畉n hi畛u bi畉t, t担i ch畛 n棚u m畛t vi suy ngh挑 c畛a m狸nh v畛 quan h畛 qu畛c
t畉 gi畛a c叩c n動畛c ASEAN. Tr動畛c ti棚n t担i cho r畉ng ASEAN v畛i nh畛ng kh叩c
bi畛t v畛 vn h坦a v ch鱈nh tr畛 nh動ng c湛ng g畉n chung m畛t s畛 l畛i 鱈ch v畛 kinh t畉
v 畛a ch鱈nh tr畛 n棚n tuy ch動a th畛 h狸nh thnh m畛t li棚n minh nh動ng h畉n nhi棚n
s畉 tr畛 thnh m畛t c畛ng 畛ng a s畉c v畛i nh畛ng li棚n k畉t tuy l畛ng l畉o nh動ng hy
v畛ng kh叩 b畛n ch畉t. i畛u ny ng畛 箪 ASEAN c畉n linh 畛ng nh動 d畉i l畛a trong
m畛i t動董ng h畛 v畛i 担ng B畉c v Nam , trong 坦 Nh畉t v c l hai 畛i t叩c
r畉t quan tr畛ng 畛nh h狸nh quan h畛 c滴ng nh動 l畛i 鱈ch, v畛i l動u 箪 r畉ng ASEAN l
c畛a ng探 then ci gi畛a Th叩i B狸nh D動董ng v 畉n 畛 D動董ng.
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ASEAN relations - Loc Nguyen
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3. ASEAN relations
Thank you very much for giving me a big opportunity to raising my thoughts
about international relations about which I am studying and researching. In a
short time as well as my limit of knowledge, I only share some thoughts
about ASEAN. Firstly, I think that although ASEAN being with differences of
culture and politics but sharing some economical benefits and geopolitical
benefits is not able to establish an alliance yet, ASEAN will become a
colorful community associated with flexible but relatively enduring links.
This implies that ASEAN needs a flexible corporation in mutual relationship
with Northeastern Asia and Southern Asia, in which Japan and Australia are
two very important partners who keep relationships and profits in good
condition, with note that ASEAN is door and latch of Pacific Ocean and
Indian Ocean.
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ASEAN relations - Loc Nguyen
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4. ASEAN relations
ASEAN c畉n nhi畛u 畛i tho畉i 畛 c畛ng c畛 l嘆ng tin c滴ng nh動 s畛 hi畛u bi畉t v nh畛ng
ph動董ng th畛c h畛p t叩c c畉n 畛i m畛i v n棚n t叩ch nh畛ng quan h畛 song ph動董ng v狸 h畛p
t叩c song ph動董ng s畉 i vo l畛i 鱈ch thi畉t th畛c nh動 vi畛c b畛n d但y th畛ng c畉n ph畉i b畛n t畛
nh畛ng s畛i nh畛 h董n. V狸 v畉y h畛p t叩c ph畉i kh畛i ngu畛n t畛 quan h畛 kinh t畉 v畛i nh畛ng
hi畛p 畛nh t畛 do th動董ng m畉i m畛 hng ro thu畉 quan c滴ng nh動 tng c動畛ng m畉ng l動畛i
giao th担ng v畉n t畉i. M畛t l挑nh v畛c h畛p t叩c 畉c d畛ng kh叩c l vn h坦a v gi叩o d畛c, s畛
hi畛u bi畉t vn h坦a g但y h畛ng th炭 c滴ng nh動 g但y d畛ng l嘆ng tin v h畛p t叩c gi叩o d畛c s畉
m畛 c畛a th畛 tr動畛ng lao 畛ng. V但ng, th畛 tr動畛ng lao 畛ng r畉t quan tr畛ng 畛i v畛i
ASEAN v t担i ngh挑 r畉ng ti畉ng Anh c畉n 動畛c khuy畉n kh鱈ch thnh ng担n ng畛 chung,
theo 坦 t畛ng n動畛c c畉n thi畉t l畉p ti畉ng Anh l ng担n ng畛 ch鱈nh th畛c th畛 hai.
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5. ASEAN relations
ASEAN needs more dialogues to consolidate the mutual trust as well as the
understanding and so, collaboration methods need to be renovated, thus, multilateral
relations should be divided into bilateral relations because bilateral corporations
focus on practical profits like the way to plait a big rope needs to twist many smaller
cords. Therefore, international co-operations inside ASEAN should lean forward
economical collaboration with concluding free trade agreements (FTA) opening
tariff barriers as well as reinforcing transportation networks. Another helpful
cooperation is collaboration in culture and education, where understanding culture
will share common highly enthusiastic interests and consolidate mutual trusts, as
well as educational collaboration will open labor market. Yes, the labor market is
very important to ASEAN and so, I think that English should be encouraged and
accepted to become a common language, thus, every ASEAN nation should assign
English as the second official language.
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6. ASEAN relations
Nguy棚n t畉c kh担ng can thi畛p vo c担ng vi畛c n畛i b畛 l畉n nhau c嘆n ph畉i 動畛c duy tr狸 r畉t
l但u n畛a v狸 ASEAN ch動a ph畉i li棚n minh v b畉t k畛 ch畉 ti kinh t畉 畛u b畉t kh畉 thi
nh動ng lu畉t & quy t畉c t畉o n棚n kho畉ng kh担ng gian dung h嘆a l畛i 鱈ch c叩c b棚n c滴ng nh動
kh担ng gian vn h坦a 畛ng x畛 n董i m ho畉t 畛ng ngo畉i giao v truy畛n th担ng s畉 ph叩t
huy. M畛t khi hi畉n ch動董ng hay lu畉t l畛 tham v畛ng v動畛t m畛c s畉 畛 v畛 nh動 s畛 l畛ng l畉o
qu叩 m畛c, n棚n th鱈ch 畛ng v 畛i m畛i c坦 l畉 l m畉u ch畛t. Li棚n minh EU tr畉i qua giai
o畉n C畛ng 畛ng Kinh t畉 EU n棚n nh畛ng v畉n 畛 li棚n quan 畉n h畛p t叩c an ninh trong
ASEAN ch動a ph畉i qu叩 quan tr畛ng ho畉c bu畛c ph畉i gi畉m nh畉 m畛c 畛 v thnh c担ng
h畛p t叩c an ninh ch畛ng t畛i ph畉m c滴ng l m畛t thnh c担ng 叩ng hoan ngh棚nh.
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7. ASEAN relations
The principle not to interfere in the internal affairs of the state must be maintained
for a very long time because ASEAN is not an alliance yet and any economic
sanctions are impossible but laws and rules will create friendly atmosphere and
space that make trade-off of benefits between member nations. Laws and rules will
also create behavioral environment where activities of diplomacy and
communication media will promote themselves their abilities in acceptable
penalties. If charter or laws are excessively ambitious as well as they are too loose,
there may exist some failures. Therefore, adaptation and renovation should be
concerned. The alliance European Union (EU) is originated from European
Economic Community (EEC), which implies that problems related to security
cooperation in ASEAN is not too important yet or we must alleviate their
importance degree and thus, a successful security cooperation against criminals is a
significant success.
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8. ASEAN relations
ASEAN m畛 r畛ng v l担i k辿o m畛t s畛 c動畛ng qu畛c h狸nh thnh c畛c di畛n c但n
b畉ng l m畛t i畛m nh畉n nh動ng i畛m son l c畛ng c畛 lu畉t l畛 c滴ng nh動 th畛
tr動畛ng lao 畛ng v狸 s畛 t畛 c動畛ng s畉 b畉t tay v畛i t畛 ch畛 chi畉n l動畛c b畉ng
c叩ch no 坦, trong 坦 s畛 c畉nh tranh r畉t c畉n thi畉t ch畉ng h畛 m但u thu畉n v畛i
s畛 linh 畛ng, n董i m ph嘆ng b畛 n動畛c 担i lu担n lu担n kh担ng th畛a.
T坦m l畉i, ch畛 ngh挑a t畛 do v畛i nhi畛u t畛 ch畛c phi ch鱈nh ph畛 ph畛i h畛p v畛i
ch畛 ngh挑a ki畉n t畉o c湛ng chia s畉 nh畛ng gi叩 tr畛 v chu畉n m畛c chung c坦 l畉
s畉 th鱈ch h畛p v畛i ASEAN ti畉n t畛i m畛t c畛ng 畛ng a s畉c v 畛 cao ch畛
ngh挑a a ph動董ng.
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9. ASEAN relations
The event that ASEAN expands and implicates some super-powers in
establishing the state of affairs in balance is a significant strategy but the
most importance is to consolidate laws/rules and labor market because
the self-strengthening will hold the strategic autonomy in some implicit
way, thus, the mutual competition is not contradictory to the
collaborative flexibility, where double hedging is always necessary.
In general the liberalism with many non-governmental organizations
in corporation with the constructivism with shared values and standards
may be appropriate to ASEAN so as to approach a colorful community
and heighten multilateralism.
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10. Thank you for listening
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ASEAN relations - Loc Nguyen
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