Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems and (...) how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health. (shrink)

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with the (...) support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free. (shrink)

The proposed paper presents an argument in favor of a Rawlsian approach to ethics for Internet technology companies (den Hoven & Rooksby, 2008; Hoffman, 2017). Ethics statements from such companies are analyzed and shown to be utilitarian and teleological in nature, and therefore in opposition to Rawls’ theories of justice and fairness. The statements are also shown to have traits in common with Confucian virtue ethics (Ames, 2011; Nylan, 2008).

Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems and (...) how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health. (shrink)

Today’s everyone normal life can include a normal rate of playing computer games or video games; but what about an excessive or compulsive use of video games that impact on our life? Our kids, who usually spend a lot of time in playing video games will likely have a trouble in paying attention to their school lessons. In this paper, we introduce an expert system to help users in getting the correct diagnosis of the health problem of video game addictions (...) that range from (Musculoskeletal issues, Vision problems and Obesity). Moreover, this expert system provides information about the problem and tell us how we can solve it. SL5 Object expert system language was used to design and implement the expert system. (shrink)

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels (...) of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

About a British Academy collection of papers on Bayes' famous theorem.

A fundamental problem in science is how to make logical inferences from scientific data. Mere data does not suffice since additional information is necessary to select a domain of models or hypotheses and thus determine the likelihood of each model or hypothesis. Thomas Bayes’ Theorem relates the data and prior information to posterior probabilities associated with differing models or hypotheses and thus is useful in identifying the roles played by the known data and the assumed prior information when making (...) inferences. Scientists, philosophers, and theologians accumulate knowledge when analyzing different aspects of reality and search for particular hypotheses or models to fit their respective subject matters. Of course, a main goal is then to integrate all kinds of knowledge into an all-encompassing worldview that would describe the whole of reality. A generous description of the whole of reality would span, in the order of complexity, from the purely physical to the supernatural. These two extreme aspects of reality are bridged by a nonphysical realm, which would include elements of life, man, consciousness, rationality, mental and mathematical abstractions, etc. An urgent problem in the theory of knowledge is what science is and what it is not. Albert Einstein’s notion of science in terms of sense perception is refined by defining operationally the data that makes up the subject matter of science. It is shown, for instance, that theological considerations included in the prior information assumed by Isaac Newton is irrelevant in relating the data logically to the model or hypothesis. In addition, the concepts of naturalism, intelligent design, and evolutionary theory are critically analyzed. Finally, Eugene P. Wigner’s suggestions concerning the nature of human consciousness, life, and the success of mathematics in the natural sciences is considered in the context of the creative power endowed in humans by God. (shrink)

Nếu nền kinh tế quốc gia là một cơ thể sống, thì hệ thống tài chính là cơ chế tạo, cung cấp và lưu thông máu tới từng tế bào, bộ phận. Thiếu hay thừa đều phát sinh các vấn đề cần giải quyết. Với quá trình chuyển đổi kinh tế và hội nhập quốc tế mạnh mẽ, liên tục giám sát, kịp thời dự đoán sát thực các dấu hiệu và biến động của thị trường để từ đó xây (...) dựng chính sách điều tiết thị trường tài chính và toàn bộ nền kinh tế một cách hợp lý là việc làm cần thiết bảo đảm tăng trưởng kinh tế bền vững, đúng định hướng. Đối với nền tài chính của Việt Nam, hiện đang tồn tại bảy dấu hiệu cảnh báo cần được các nhà nghiên cứu và hoạch định chính sách quan tâm. (shrink)

The problem addressed in this paper is “the main epistemic problem concerning science”, viz. “the explication of how we compare and evaluate theories [...] in the light of the available evidence” (van Fraassen, BC, 1983, Theory comparison and relevant Evidence. In J. Earman (Ed.), Testing scientific theories (pp. 27–42). Minneapolis: University of Minnesota Press). Sections 1– 3 contain the general plausibility-informativeness theory of theory assessment. In a nutshell, the message is (1) that there are two values a theory should exhibit: (...) truth and informativeness—measured respectively by a truth indicator and a strength indicator; (2) that these two values are conflicting in the sense that the former is a decreasing and the latter an increasing function of the logical strength of the theory to be assessed; and (3) that in assessing a given theory by the available data one should weigh between these two conflicting aspects in such a way that any surplus in informativeness succeeds, if the shortfall in plausibility is small enough. Particular accounts of this general theory arise by inserting particular strength indicators and truth indicators. In Section 4 the theory is spelt out for the Bayesian paradigm of subjective probabilities. It is then compared to incremental Bayesian confirmation theory. Section 4 closes by asking whether it is likely to be lovely. Section 5 discusses a few problems of confirmation theory in the light of the present approach. In particular, it is briefly indicated how the present account gives rise to a new analysis of Hempel’s conditions of adequacy for any relation of confirmation (Hempel, CG, 1945, Studies in the logic of comfirmation. Mind, 54, 1–26, 97–121.), differing from the one Carnap gave in § 87 of his Logical foundations of probability (1962, Chicago: University of Chicago Press). Section 6 adresses the question of justification any theory of theory assessment has to face: why should one stick to theories given high assessment values rather than to any other theories? The answer given by the Bayesian version of the account presented in section 4 is that one should accept theories given high assessment values, because, in the medium run, theory assessment almost surely takes one to the most informative among all true theories when presented separating data. The concluding section 7 continues the comparison between the present account and incremental Bayesian confirmation theory. (shrink)

The college hopes that every semester students are able to pay tuition properly and smoothly. The hope is that the institution will be able to maintain monthly cash flow so that its operational and maintenance costs can be met. Therefore, this study was conducted to predict and fulfill the institution's cash-in from the method of paying tuition fees either by cash, installments, or sometimes late payments every semester. In predicting the method of paying tuition fees, using student profile data (name, (...) name, study program) and achievement index every semester for 5 semesters passed and the method of payment (cash, installments, and late--cash or installments). Using the Naive Bayes (NB) method, Support Vector Machine (SVM), and Deep Learning, this study aims to forecast tuition costs. The Classification Prediction Model with Naive Bayes, SVM, and Deep Learning produces Confusion Matrix Performance NB with an Accuracy of 91.49%, Confusion Matrix Performance SVM with an Accuracy of 85.11%, and Confusion Matrix Performance Deep Learning with an Accuracy of 89.36%, according to the research findings. (shrink)

We present a general framework for representing belief-revision rules and use it to characterize Bayes's rule as a classical example and Jeffrey's rule as a non-classical one. In Jeffrey's rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayes's rule, but a new assignment of probabilities to some events. Despite their differences, Bayes's and Jeffrey's rules can be characterized in terms of the same axioms: "responsiveness", which requires (...) that revised beliefs incorporate what has been learnt, and "conservativeness", which requires that beliefs on which the learnt input is "silent" do not change. To illustrate the use of non-Bayesian belief revision in economic theory, we sketch a simple decision-theoretic application. (shrink)

Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...) messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naïve Bayes (MNB) and optimized linear Support Vector Machine(SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...) performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

According to [Bayesian] models” in cognitive neuroscience, says a recent textbook, “the human mind behaves like a capable data scientist”. Do they? That is to say, do such model show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, do not and indeed cannot, show that we are Bayesian-rational. The key reason is that such models appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two responses, seen in recent cognitive neuroscience. (...) One says that the mind can be seen as approximately Bayes-rational, while the other reconceives norms of rationality. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...) performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. -/- We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features. (shrink)

Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...) messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naïve Bayes (MNB) and optimized linear Support Vector Machine(SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action. (shrink)

Ở quy mô toàn cầu, giao thông vận tải là hoạt động tạo ra lượng khí thải nhà kính lớn thứ nhì trong tất cả các lĩnh vực, chiếm 16,52% tổng lượng khí thải nhà kính toàn cầu vào năm 2019, chỉ sau các hoạt động sản xuất điện và nhiệt, chiếm khoảng 31%.

Trong thảo luận ngắn này, xin giới thiệu lại một bài điều tra tổng hợp về trường hợp cơ quan quản lý Scotland đã thống nhất với ý kiến của các đại diện công nghiệp nuôi trồng-chế biến cá hồi nhằm nới lỏng kiểm soát hóa chất diệt ký sinh, vốn được biết là độc hại (gọi chung là pesticide), có tên emamectin. Cụ thể các giới hạn sử dụng được nới lỏng để tăng 23 lần, sau các “thương thảo (...) trong bóng tối” với đại diện giới chủ. Bài báo đăng trên Ferret, ngày 21-5-2023, ký tên tác giả R. Edwards. (shrink)

Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.

Medical diagnosis has been traditionally recognized as a privileged field of application for so called probabilistic induction. Consequently, the Bayesian theorem, which mathematically formalizes this form of inference, has been seen as the most adequate tool for quantifying the uncertainty surrounding the diagnosis by providing probabilities of different diagnostic hypotheses, given symptomatic or laboratory data. On the other side, it has also been remarked that differential diagnosis rather works by exclusion, e.g. by modus tollens, i.e. deductively. By drawing on a (...) case history, this paper aims at clarifying some points on the issue. Namely: 1) Medical diagnosis does not represent, strictly speaking, a form of induction, but a type, of what in Peircean terms should be called ‘abduction’ (identifying a case as the token of a specific type); 2) in performing the single diagnostic steps, however, different inferential methods are used for both inductive and deductive nature: modus tollens, hypothetical-deductive method, abduction; 3) Bayes’ theorem is a probabilized form of abduction which uses mathematics in order to justify the degree of confidence which can be entertained on a hypothesis given the available evidence; 4) although theoretically irreconcilable, in practice, both the hypothetical- deductive method and the Bayesian one, are used in the same diagnosis with no serious compromise for its correctness; 5) Medical diagnosis, especially differential diagnosis, also uses a kind of “probabilistic modus tollens”, in that, signs (symptoms or laboratory data) are taken as strong evidence for a given hypothesis not to be true: the focus is not on hypothesis confirmation, but instead on its refutation [Pr (¬ H/E1, E2, …, En)]. Especially at the beginning of a complicated case, odds are between the hypothesis that is potentially being excluded and a vague “other”. This procedure has the advantage of providing a clue of what evidence to look for and to eventually reduce the set of candidate hypotheses if conclusive negative evidence is found. 6) Bayes’ theorem in the hypothesis-confirmation form can more faithfully, although idealistically, represent the medical diagnosis when the diagnostic itinerary has come to a reduced set of plausible hypotheses after a process of progressive elimination of candidate hypotheses; 7) Bayes’ theorem is however indispensable in the case of litigation in order to assess doctor’s responsibility for medical error by taking into account the weight of the evidence at his disposal. (shrink)

Supra-Bayesianism is the Bayesian response to learning the opinions of others. Probability pooling constitutes an alternative response. One natural question is whether there are cases where probability pooling gives the supra-Bayesian result. This has been called the problem of Bayes-compatibility for pooling functions. It is known that in a common prior setting, under standard assumptions, linear pooling cannot be nontrivially Bayes-compatible. We show by contrast that geometric pooling can be nontrivially Bayes-compatible. Indeed, we show that, under certain (...) assumptions, geometric and Bayes-compatible pooling are equivalent. Granting supra-Bayesianism its usual normative status, one upshot of our study is thus that, in a certain class of epistemic contexts, geometric pooling enjoys a normative advantage over linear pooling as a social learning mechanism. We discuss the philosophical ramifications of this advantage, which we show to be robust to variations in our statement of the Bayes-compatibility problem. (shrink)

ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide (...) additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program, and thresholding based on Bayes Factors, to scholars in the field of moral education. (shrink)

I argue that when we use ‘probability’ language in epistemic contexts—e.g., when we ask how probable some hypothesis is, given the evidence available to us—we are talking about degrees of support, rather than degrees of belief. The epistemic probability of A given B is the mind-independent degree to which B supports A, not the degree to which someone with B as their evidence believes A, or the degree to which someone would or should believe A if they had B as (...) their evidence. My central argument is that the degree-of-support interpretation lets us better model good reasoning in certain cases involving old evidence. Degree-of-belief interpretations make the wrong predictions not only about whether old evidence confirms new hypotheses, but about the values of the probabilities that enter into Bayes’ Theorem when we calculate the probability of hypotheses conditional on old evidence and new background information. (shrink)

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows its (...) assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has an accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naïve Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution. (shrink)

The paper argues that the two best known formal logical fallacies, namely denying the antecedent (DA) and affirming the consequent (AC) are not just basic and simple errors, which prove human irrationality, but rather informational shortcuts, which may provide a quick and dirty way of extracting useful information from the environment. DA and AC are shown to be degraded versions of Bayes’ theorem, once this is stripped of some of its probabilities. The less the probabilities count, the closer these (...) fallacies become to a reasoning that is not only informationally useful but also logically valid. (shrink)

The paper argues that the two best known formal logical fallacies, namely denying the antecedent (DA) and affirming the consequent (AC) are not just basic and simple errors, which prove human irrationality, but rather informational shortcuts, which may provide a quick and dirty way of extracting useful information from the environment. DA and AC are shown to be degraded versions of Bayes’ theorem, once this is stripped of some of its probabilities. The less the probabilities count, the closer these (...) fallacies become to a reasoning that is not only informationally useful but also logically valid. (shrink)

Bây giờ, cuộc chiến với tổn thất đa dạng sinh học đang lan rộng sang ngành công nghiệp làm đẹp. Công nghiệp thời trang đã từng chịu áp lực trước các ảnh hưởng môi trường tiêu cực, và đang phải điều chỉnh lớn, nhất là với thời trang nhanh. Nhưng món chăm sóc thẩm mỹ dường như đang chịu áp lực gia tăng dần. Ngành hàng thẩm mỹ và chăm sóc cá nhân có doanh số rất lớn và lợi nhuận (...) cao, đặc biệt đối với phân khúc thu nhập cao. Tuy nhiên, lợi thế này cũng tiềm ẩn rủi ro phải đối diện với những quan sát, nghiên cứu, điều tra và cả chỉ trích của các tổ chức xã hội dân sự, các phong trào hoạt động môi trường, và cả người tiêu dùng. Hơn ai hết, ngành công nghiệp này có độ nhạy rất cao với rủi ro của các ý kiến đánh giá tiêu cực xã hội, nhất là hậu quả nặng nề của các phong trào tẩy chay tiêu dùng. Sắc thái mới nhất và sẽ chỉ có thể gia tăng khi mà sức khỏe hệ sinh thái toàn cầu đang suy giảm chính là các sản phẩm có liên quan tới phát thải và tổn thất môi sinh, cụ thể hơn là tổn thất đa dạng sinh học do chặt phá rừng và canh tác nông nghiệp gây tác hại lâu dài. (shrink)

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...) in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational. (shrink)

The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.

A Bayesian measure of evidence for precise hypotheses is presented. The intention is to give a Bayesian alternative to significance tests or, equivalently, to p-values. In fact, a set is defined in the parameter space and the posterior probability, its credibility, is evaluated. This set is the “Highest Posterior Density Region” that is “tangent” to the set that defines the null hypothesis. Our measure of evidence is the complement of the credibility of the “tangent” region.

This paper briefly addresses questions of confirmation and disconfirmation in psychoanalysis. It argues that psychoanalysis enjoys Bayesian support as an interpretive extension of commonsense psychology that provides the best explanation of a large range of empirical data. Suggestion provides no such explanation, and recent work in attachment, developmental psychology, and neuroscience accord with this view.

Bây giờ ở Hà Nội lại có nhiều quán nước vối, nấu cả nụ lẫn lá. Thế là, thức uống ngàn đời của người Việt mà cách đây gần một trăm năm cụ Nguyễn Công Tiễu, một nhà khoa học hàng đầu về lĩnh vực nông nghiệp, đã có một công trình về giá trị dinh dưỡng của nó sau một hồi thời thế xoay vần đã quay trở lại, bình thường như lẽ sống, sự sống. Nước vối khiêm nhường, (...) nhưng đã đủ khỏe khoắn để vượt qua những thách thức của nước ngọt, đồ uống có ga, những thương hiệu lẫy lừng. (Tạp chí Tia Sáng; 7-7-2016). (shrink)

I highlight a metaphysical concern that stands in the way of more widespread adoption of causal modeling techniques such as causal Bayes nets. Researchers in some fields may resist adoption due to concerns that they don't 'really' understand what they are saying about a system when they apply such techniques. Students in these fields are repeated exhorted to be cautious about application of statistical techniques to their data without a clear understanding of the conditions required for those techniques to (...) yield genuine insight into the data. They are acutely aware that anyone can chuck some data into a software package and get what looks like an answer, even though these tests may not be well-defined for the data on which they can apparently be run. This is thus a healthy skepticism for uptake of causal modeling methods, which points directly to the need for a metaphysical understanding of causation in order to successfully use the modeling methods. Without a clear understanding of what the methods are committing to, including what it means to say that there exists a causal relationship, researchers have limited ability to identify potentially bad output, or to independently verify results. (shrink)

In Being You, Anil Seth takes us on a comprehensive tour through the science of consciousness, drawing on the most up-to-date data, lessons and theories in the field. This is a compelling book that will leave readers pondering whether new technologies and smart experimental designs will further deepen our understanding of the mind, writes Manh Tung Ho.

Bài viết trình bày một số triển khai mới trong quá trình phát triển bổ sung các luận điểm sâu hơn cho hệ thống phát triển và quản trị quá trình sản xuất tri thức / phát kiến kỹ thuật-công nghệ của nhóm nghiên cứu AISDL, thường được gọi tắt là hệ thống lý thuyết SM3D.

Bài viết trình bày một số triển khai mới trong quá trình phát triển bổ sung các luận điểm sâu hơn cho hệ thống phát triển và quản trị quá trình sản xuất tri thứcphát kiến kỹ thuật-công nghệ của nhóm nghiên cứu AISDL, thường được gọi tắt là hệ thống lý thuyết SM3D.

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

In a series of papers over the past twenty years, and in a new book, Igor Douven has argued that Bayesians are too quick to reject versions of inference to the best explanation that cannot be accommodated within their framework. In this paper, I survey their worries and attempt to answer them using a series of pragmatic and purely epistemic arguments that I take to show that Bayes’ Rule really is the only rational way to respond to your evidence.

The reduction of grounding to causation, or each to a more general relation of which they are species, has sometimes been justified by the impressive inferential capacity of structural equation modelling, causal Bayes nets, and interventionist causal modelling. Many criticisms of this assimilation focus on how causation is inadequate for grounding. Here, I examine the other direction: how treating grounding in the image of causation makes the resulting view worse for causation. The distinctive features of causal modelling that make (...) this connection appealing are distorted beyond use by forcing them to fit onto grounding. The very inferential strength that makes causation attractive is only possible because of a narrow construal of what counts as a causal relation; as soon as that broadens, the inferential capacity markedly diminishes. Making causation suitable for application to grounding spoils what was appealing about causation for this task in the first place. However, grounding need not appeal to causation: causal modelling does not have exclusive claim to structural equation modeling or other formal techniques of modelling structure. I offer a case in favour of a different kind of metaphysical frugality, which tend towards narrow, more restrictive construals of relations like causation or grounding, because then each relation behaves more homogenously. This more homogenous behavior delivers stronger inferential power per relation even though there may be more relations to which one is committed. (shrink)

TCCS - Việt Nam đang đứng trước ngưỡng cửa thập niên thứ hai, thế kỷ XXI. Tuy vậy, mốc thời gian không quan trọng bằng những đổi thay chóng mặt của quá trình quốc tế hóa đời sống kinh tế, và những biến đổi sâu sắc nó sẽ mang lại. Quá trình ấy, ngay bây giờ, lại được phóng đại qua lăng kính của những biến động toàn cầu do khủng hoảng kinh tế - tài chính 2007 - 2009, điều (...) có thể dẫn tới những biến đổi lớn về cấu trúc kinh tế nhân loại, cũng như diễn tiến địa - chính trị khắp nơi trên thế giới. (shrink)

This article explores some open questions related to the problem of verification of theories in the context of empirical sciences by contrasting three epistemological frameworks. Each of these epistemological frameworks is based on a corresponding central metaphor, namely: (a) Neo-empiricism and the gambling metaphor; (b) Popperian falsificationism and the scientific tribunal metaphor; (c) Cognitive constructivism and the object as eigen-solution metaphor. Each of one of these epistemological frameworks has also historically co-evolved with a certain statistical theory and method for testing (...) scientific hypotheses, respectively: (a) Decision theoretic Bayesian statistics and Bayes factors; (b) Frequentist statistics and p-values; (c) Constructive Bayesian statistics and e-values. This article examines with special care the Zero Probability Paradox (ZPP), related to the verification of sharp or precise hypotheses. Finally, this article makes some remarks on Lakatos’ view of mathematics as a quasi-empirical science. (shrink)

Trust is defined as a belief of a human H (‘the trustor’) about the ability of an agent A (the ‘trustee’) to perform future action(s). We adopt here dispositionalism and internalism about trust: H trusts A iff A has some internal dispositions as competences. The dispositional competences of A are high-level metacognitive requirements, in the line of a naturalized virtue epistemology. (Sosa, Carter) We advance a Bayesian model of two (i) confidence in the decision and (ii) model uncertainty. To trust (...) A, H demands A to be self-assertive about confidence and able to self-correct its own models. In the Bayesian approach trust can be applied not only to humans, but to artificial agents (e.g. Machine Learning algorithms). We explain the advantage the metacognitive trust when compared to mainstream approaches and how it relates to virtue epistemology. The metacognitive ethics of trust is swiftly discussed. (shrink)

Sự tiến bộ nhanh chóng của các nền tảng Công nghệ Thông tin (CNTT) và ngôn ngữ lập trình đã làm thay đổi hình thái vận động và phát triển của xã hội loài người. Không gian mạng và các tiện ích đi kèm ngày càng được mở rộng, dẫn đến sự chuyển dịch dần từ đời sống trong thế giới thực sang đời sống trong thế giới ảo (còn gọi là không gian mạng hay không gian số). Trong bối (...) cảnh các hoạt động kinh tế và xã hội ngày càng được kết nối tốt hơn thông qua IoT, và sắp tới đây là tiềm năng tích hợp AI vào hầu như mọi mặt của đời sống ở cả thế giới thực và thế giới ảo, thì không chỉ cá nhân, mà cả doanh nghiệp và quốc gia cũng sẽ phải đối mặt với các thách thức chưa từng có tiền lệ đối với rủi ro về an ninh thông tin. Sự mở rộng và phát triển của các Mô hình Xử lý Ngôn ngữ Tự nhiên (Natural Language Processing – NLP) và Mô hình Ngôn ngữ Lớn (Large Language Model – LLM) thể hiện những đặc điểm giống con người về lập luận, nhận thức, sự chú ý và sáng tạo đã giúp con người vượt qua rào cản về khả năng vận hành. Đi kèm với các tiềm năng to lớn của AI là những lỗ hổng bảo mật mới và các rủi ro về an ninh thông tin phức tạp hơn. Khi xã hội vẫn đang cố gắng chuyển mình sang giai đoạn mới để có thể thích ứng với sự biến đổi công nghệ, cuộc cách mạng AI lại tiếp tục diễn ra đòi hỏi chúng ta phải suy nghĩ lại quỹ đạo của quá trình chuyển đổi xã hội vì nó có thể làm trầm trọng hơn các rủi ro an ninh thông tin được trình bày ở trên. Cụ thể, xã hội cần tiến hóa như thế nào để có thể bắt kịp với sự biến đổi mang tính đột phá tạo ra bởi làn sóng công nghệ AI hiện nay? Làm sao để quản lý và tận dụng sức mạnh của chúng trong khi vẫn đảm bảo được an ninh thông tin khi không gian và thời gian sống trong thế giới ảo của chúng ta ngày càng tăng? Các câu hỏi này sẽ được trả lời thông qua bài viết. (shrink)

Trong bài viết này chúng tôi trình bày thông tin đại cương về vai trò của nickel trong công nghiệp chế tạo giữa bối cảnh yêu cầu bảo vệ môi sinh trong đại nghị sự mục tiêu phát triển bền vững (SDG) của Liên Hợp Quốc ngày một cấp bách, đối diện với nguy cơ không thể hoàn thành. Các thông tin tóm lược ở đây được dẫn chiếu tới một số nhỏ các tài liệu tham khảo chính và không (...) thể đảm bảo tính trọn vẹn trong khuôn khổ một bài báo nhỏ. Những vấn đề đặt ra có tính tổng quát cho quan hệ môi sinh với việc khai khoáng, tách và tinh chế nickel nói chung trên thế giới. Vì thế, có thể thông tin trong bài không hoàn toàn trùng khớp với tình hình cụ thể của một địa phương trên trái đất, do đặc điểm địa lý, địa chất và kinh tế-công nghệ khác biệt. Bài viết tập trung xoay quanh nguyên lý quan hệ kinh tế-môi trường và ý nghĩa thực tiễn của cụm từ “bền vững” trong bối cảnh cụ thể của công nghiệp nickel. (shrink)