Logical Inference Research Articles

Heart rate variability in patients with insomnia disorder: a systematic review and meta-analysis.

Though logical inference based on small studies suggests that patients with insomnia have a blunted heart rate variability (HRV), this condition not been clearly shown. This meta-analysis aimed to determine whether or notHRV was impaired in patients withinsomnia. We conducted a comprehensive literature search for studies that examined the association of HRV with insomnia disorder. HRV measures were extracted using the SDNN (time-domain measure, i.e., standard deviation of all NN intervals,) or the HF-norm (frequency-domain measure, i.e., high-frequency power normalized using total power). The effect size estimates of R_equivalent obtained from p values and sample size were synthesized. We identified 17 studies with 921 participants. The pooled R_equivalent was 0.19 with 95% CI of 0.12 and 0.25 (p < 0.001), and there was no significant heterogeneity (I2 = 0.0%, p = 0.809). A p value of 0.075 corresponding to the R_equivalent of 0.19 was obtained, suggesting that blunted HRV was not significant in patients with insomnia disorder. The current evidence did not confirm that HRV was reliably impaired in patients with insomnia disorder. Prospective studies using standard HRV measures are needed to clarify the HRV dynamics in different insomnia subtypes.

Ballast Water Dynamic Allocation Optimization for Revolving Floating Cranes Based on a Hybrid Algorithm of Fuzzy-Particle Swarm Optimization with Domain Knowledge

Ballast systems and ballast water dynamic allocation between ballast tanks are very important for ensuring the offshore operation efficiency and safety of the revolving floating crane (RFC). Its modeling and solving have multiple difficulties such as modeling complexity, solving complexity and engineering practicability. Early studies showed that domain knowledge is of great significance for the optimization of the design quality and innovation of such complex engineering issues. By analyzing the coupled operation process characteristics among the floating crane, ship hull and ballast system, a ballast water allocation optimization model based on dynamic programming strategy is established. The domain knowledge of ship ballasting is extracted, and a domain knowledge base of expert experience rules for the ballast water allocation is established. A Fuzzy-Particle Swarm Optimization (FPSO) algorithm is given to obtain the optimal allocation scheme, which uses fuzzy logic inference to process domain knowledge and improve the solving quality. Three different cases are given to illustrate the validity of the proposed model and algorithm by comparing it with other algorithms. The analysis results show that the established optimization method can effectively improve the operation efficiency and reduce the calculation time and the number of ballast tanks involved in allocation, which makes the optimal scheme more suitable for engineering applications.

Between God and Satan: Thomas Thornton, Witch-Hunting, and Religious Mission in the English Atlantic World, 1647–1693

Between God and Satan: Thomas Thornton, Witch-Hunting, and Religious Mission in the English Atlantic World, 1647–1693

Etic and politic implications in 99 Cahaya di Langit Eropa as a travel literature

This study aims to show the hidden agenda in the 99 Cahaya di Langit Eropa as a travel literature. The method used is divided into two, namely data collection methods and data analysis methods. The data in this study are in the form of statements about the content, nature, characteristics, condition of something or symptoms, or statements about the relationships between something and something else. Data analysis methods used in this study are historical methods, comparisons, and logical inference. This research shows that the 99 Cahaya di Langit Eropa has ethical and political implications which are the fundamental agenda in travel stories. The ethical implications of this novel are first, by demeaning Indonesia, this novel follows the ethical violations committed by Western orientalism. Second, by demeaning the West, this novel also commits the same ethical violation, namely demeaning other nations. The political implication of this novel is an attempt to free oneself from the hegemony of colonial orientalism, to elevate the dignity of the colonized nation. The answer to liberation offered to Indonesia is a typical answer given by Indonesian Muslim scholars, namely by making Indonesia an Islamic country that is Western and Islamic West. Indonesia is understood as a nation that still must be westernized and at the same time Muslimized.Keywords: ethical implications, political implications, travel literature, West, Islam

Disciplinary nature of 『the politics of education』 revisited: In the middle of its identity and boundary spanning

본 연구는 새로운 교육 패러다임의 전환이 필요하다는 우려와 기대가 교차하는 시점에서 오늘날 교육정치학이 경험하고 있는 현실에 대한 이해를 바탕으로 학문적 성격 탐구의 확장을 시도하면서 이에 부합하는 미래지향적인 교육정치학의 학문적 성격을 탐색하고자 한다. 코로나19 팬데믹과 디지털 전환은 교육정치학 연구에 새로운 도전으로 등장하고 있다. 이러한 상황에서 교육정치학은 학문적 성격의 검토를 통해 정체성을 점검하고 학문의 경계 확장을 모색해야 할 지점에 서 있다. 이런 점에서 교육과 정의에 관한 탐구와 정치적 합리성으로 대변되는 논리의 탐구는 교육정치학의 성격을 내재적으로 접근하던 전통적인 방식의 확장을 가져다 줄 것이다. 이러한 논구를 통해 미래지향적인 교육정치학의 학문적 성격은 융합학으로서의 교육정치학, 국가의 동학으로서의 교육정치학, 현장기반 미래학으로서의 교육정치학으로 확립될 필요가 있다. 이에 따라 교육정치학의 탐구 영역은 개인적 행위 양식의 차원, 조직의 행위 양식 차원 그리고 제도적 수준에서 국가, 정부, 시도의회, 사법제도, 시장제도, 시민사회와 결사체 등을 대상으로 다양하게 연구할 수 있다. 교육정치학의 연구접근에 있어서는 교육문제와 그 해결 과정에서 분명하고 명료한 언어를 사용하여 사회구조를 분석하고, 행위자와 구조의 상호작용을 이해하는 통합적 추론 논리와 방법을 개발할 필요가 있다.At the time of anxiety and expectation for new paradigm shift are crossing, this study intends to understand the reality which the politics of education faces, attempts to expand inquiry on its disciplinary features, and explores future-oriented nature. Corona 19 pandemic and digital transformation have emerged as new challenges in the politics of education research. Under these current situations, the politics of education stood at the point of examining its identity and seeking its boundary spanning through the revisit to its disciplinary nature. It is expected that inquiries on the relations of both education and social justice, and the logic of political rationality would contribute to expanding discussions about the nature of the politics of education beyond its traditional approach to the nature. This study suggests the future oriented disciplinary natures of the politics of education as interdisciplinary science, the nation’s dynamic academics, and field-based futurology. Therefore, the areas of inquiries would be listed in variety as apparent behaviors and modes at the individual, organizational levels, the nation, government, national assembly, local legislative councils, market system, civil society and interest groups at the institutional level. For the research approach, social structure needs to be analyzed using clear and definitive language in education problem and problem solving processes, and it is required to develop the logic of integrative inferences to understand the interactions between actors, structure, and its practical methods.

Karl Popper and the Problem of Essentialism in Philosophy

In modern philosophy, essentialism is in most cases regarded as an outdated and, in fact, incorrect philosophical trend. And one of the scientists who created such a reputation of essentialism was the famous English philosopher of Austrian origin Karl Popper. The success of his book “The Open Society and its Enemies” led to the fact that in the West essentialism began to be considered not only cognitively untenable, but also suspicious as the theoretical basis of fascism, communism and totalitarianism. In the article, K. Popper’s arguments against essentialism are reviewed all over again, and it is shown that K. Popper’s criticism of essentialism as an anti-scientific and outdated doctrine is not the point of view of the philosophy as a whole, but it is just the position of empirical positivism. Essentialism deals with the reality that lies on the other side of phenomena. And this, according to K. Popper, necessarily leads to “ultimate” definitions. However, in accordance with the doctrine of falsification by K. Popper himself, every scientific conclusion within its expiration date is “ultimate”. The article shows that in reality essentialism did not only play an extraordinary role in the classical metaphysical theory of knowledge, but also continues to do so within the framework of modern ontology. Moreover, in the latter case, he does it with the help of abduction which is a specific form of logical inference generating scientific hypotheses. The existence of abduction in science, which generates new knowledge, suggests that essentialism is not something, at best, tolerable and excusable. This is the main way to development of sciences in general. In all cases, the author of the article considers only logical entities, but not legal, theological or any other.

Determining the Availability of Continuous Systems at Open Pits Applying Fuzzy Logic

This work presents a model for determining the availability of continuous systems at open pits by applying fuzzy logic and fuzzy inference systems. The applied model was formed by the synthesis of independent partial indicators of availability. The model is based on an expert system for assessing the availability of continuous mining systems. The availability of the system, as a complex state parameter, is decomposed into the partial indicators, reliability, and convenience of maintenance, and the fuzzy compositions, used for integration the partial indicators, are the max–min and min–max compositions. The advantage of this model in comparison to the conventional models is that it takes into account the effect of practical indicators of availability, does not require long-term monitoring, and records necessary to determine the time picture of the system state. This model for determining the availability has a role to help responsible persons at open pits in the planning and control of exploitation, and the adoption of an appropriate maintenance strategy, all with the aim of stable production and cost reduction. The presented model can be used as a tool for the quick assessment of system availability, based on expert judgments and assessments.

Real-Time Regulation of Physical Training Intensity Based on Fuzzy Neural Network

In this paper, the fuzzy neural network model is studied, the real-time regulation model of physical training intensity is analyzed and a real-time regulation system based on a fuzzy neural network is designed. The real-time, accurate and effective regulation of the physiological load intensity in the body of the exerciser is consistent with the predetermined goals of the training program. In this paper, we propose an RBF neural network, combined with the plan and demand of physical training operation situation sensing, and considering that most of the biological training operation data is fuzzy, this paper connects a fuzzy logic inference system and a neural network and proposes a network operation situation sensing model based on an RBF neural network structure. The RBF neural network and the traditional fuzzy neural network are compared. The experiments prove that this paper’s fuzzy neural network model has a faster training speed. In this paper, we use time-realistic control equipment to monitor the physical training process of athletes so that we can grasp the training situation of athletes in real-time and ensure that athletes can achieve better training results by changing training methods and changing training loads in time for those athletes who cannot reach their sports goals. In the process of physical fitness training monitoring, an effective monitoring of training, time-accurate regulation monitoring has the advantage of timely feedback on the training situation. This model has a better convergence effect during exercise and a higher accuracy of posture prediction during testing.

Logical intuition is not really about logic.

Recent research suggests that reasoners are able to draw simple logical or probabilistic inferences relatively intuitively and automatically, a capacity that has been termed "logical intuition" (see, e.g., De Neys & Pennycook, 2019). A key finding in support of this interpretation is that conclusion validity consistently interferes with judgments of conclusion believability, suggesting that information about logical validity is available quickly enough to interfere with belief judgments. In this study, we examined whether logical intuitions arise because reasoners are sensitive to the logical features of a problem or another structural feature that just happens to align with logical validity. In three experiments (N = 113, 137, and 254), we presented participants with logical (determinate) and pseudological (indeterminate) arguments and asked them to judge the validity or believability of the conclusion. Logical arguments had determinately valid or invalid conclusions, whereas pseudological arguments were all logically indeterminate, but some were pseudovalid (possible strong arguments) and others pseudoinvalid (possible weak arguments). Experiments 1 and 2 used simple modus ponens and affirming the consequent structures; Experiment 3 used more complex denying the antecedent and modus tollens structures. In all three experiments, we found that pseudovalidity interfered with belief judgments to the same extent as real validity. Altogether, these findings suggest that while people are able to draw inferences intuitively, and these inferences impact belief judgments, they are not logical intuitions. Rather, the intuitive inferences are driven by the processing of more superficial structural features that happen to align with logical validity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Ефективний оптичний підхід до обробки нечітких даних на основі кольорів та світлофільстрів

Ця робота присвячена створенню ефективних оптичних логічних систем на основі використання світлового випромінювання визначеного кольору безпосередньо як нечіткої змінної — носія логічної інформації та основи побудови логічних рішень шляхом перетворення світлового випромінювання відповідними світловими фільтрами. Оптична обробка кольорової інформації, яка відображає різні значення вхідних даних (розглядається на прикладі експертних оцінок), здійснюється запропонованою структурною побудовою нечітких логічних вентилів (логічного колороїда) та значно спрощується, по відношенню до існуючих систем, за рахунок реалізації на властивостях адитивної та субтрактивної обробки кольорів з використанням достатньо простих світлових фільтрів. Сформовано нечітку базу даних на основі визначення кванта інформації, як відповідний колір, та компоненти оптичної логічної системи за допомогою адитивної та субтрактивної обробки світлового випромінювання відповідних кольорів; розроблено основи синтезу систем логічного висновку та прийняття рішень. В роботі синтезовано узагальнену структурну схему оптичного логічного колороїда як основу створення багаторівневої системи прийняття рішень для подальшого застосування в системах штучного інтелекту. Схеми оптичних логічних колороїдів можуть поєднуватися в послідовно-паралельні ієрархічно організовані схеми, колір використовуваних світлофільтрів може також, крім експертних оцінок, відображати тактильну інформацію систем сенсорів про навколишнє середовище, що необхідно для формування відповідних логічних оцінок або рішень. Використання кольору як носія логічної інформації дозволяє створити швидкодіючі технічні пристрої з продуктивністю, в основі розрахунків якої використовується швидкість світла для формування певного масиву логічних рішень.

An ontology oriented service framework for social IoT

Social IoT (S-IoT) is a robust paradigm linked with big data and IoT for the sharing and interaction of various devices and services to obtain the desired goal. In recent applications, it is a challenging task to maintain the trust between various linked devices for the growth and creation of IoT systems. Therefore, this paper presents a separation architecture based on the Quality of Services by managing the trust between the IoT devices. The proposed model is novel based on the selection of the attribute set against the separated class. The training and classification are done by using the ontology architecture. The data architecture is processed by a k-means algorithm for the generation of the class labels into three different groups. The groups have been labeled using a Fuzzy logic inference engine and a Genetic Algorithm has been used for optimization. The performance measure has been evaluated using multiple multiclass classifiers for accuracy, True Positive Rate, (TPR), and False Positive Rate (FPR).The simulation results elucidate that accuracy of the proposed technique is improved by 10.7%, FPR by 6%, and TPR by 11% in comparison to state of art techniques.

Logical inferences according to the Cognitive Development model among Preparatory school students

The research aims to identify the level of logical inferences and its skills among students of the preparatory stage, and to identify the logical inferences &amp; its skills according to the gender variable (male - female) and academic specialization (scientific – literary). The sample consisted of (400) male and female students who were selected by the random stratified proportional method from the three Karkh education directorates. The researcher built a logical inferences test that includes: (6) sub-skills: inference (Proportional, probabilistic, Synthetic, Hypothetical, Syllogistic, &amp; Variables Controlling). The electrochemical properties of the test were verified. The following statistical methods were used: (Chi-square, Correlation Coefficient: Pearson, Biserial, Spearman Brown Correlation), one-sample t-test, two-way analysis of variance). The following results were obtained: 1) Preparatory school students possess a high level of logical inference; 2) Preparatory stage students have more structural &amp; hypothetical inference skill than other skills; 3) The degree of logical inference males is higher than in females; 4) The degree of logical inferences among students of the scientific Branch is higher than that of students of the literary Branch; 5) Males outperform females in the skills of proportional and Synthetic inference;

Driver state detection for driver-automation shared control with fuzzy logic

This paper focuses on driver-automation shared lateral control by considering the variation of driver state which is interfered by multiple-risk abnormal behaviours. First, four abnormal behaviours, i.e. smoking, calling, yawning and drowsiness, under Driver Monitoring System are detected by a computer vision based method which combines face alignment algorithm with Haar–AdaBoost. A novel fuzzy logic system is then designed by analysing the risk of the four behaviours, aiming to identify the risk levels of driver states. Based on the identified results, a fuzzy inference logic is developed to design the driver-automation shared control by using a PID controller. Simulation experiments are conducted to illustrate the effectiveness of the detection method and the designed controller. The comparison between the simulation results shows that the proposed control architecture has comparatively better performance in lane keeping task.

A logical analysis of the debate on Hao River

ABSTRACT The debate between Zhuangzi and Huizi has profound epistemological significance, however, the main body of their debate comprises logical inferences and refutations. Therefore, this paper mainly focuses on the logical aspects of the debate. Some scholars have suggested that Huizi’s argument is self-contradictory; however, in fact, based on such evaluations of Huizi’s argument, we can conclude that Zhuangzi’s logic is also self-contradictory. By utilizing modern logical analysis tools, this study reveals what and how the propositions and inferences contained in the language of the debate are articulated accurately and strictly. Moreover, by elaborating on the implicit premises omitted by Zhuangzi and Huizi, the actual logical process of the debate can be restored, and the logical fallacy of clandestine change of argumentative issue are revealed. This study presents clear modern logic analysis of the debate; it can also provide a more reliable logical basis for the discussions of Zhuangzi and Huizi’s related philosophical thoughts.

Methodology for Conflating Large-Scale Roadway Networks

Transportation agencies are increasingly integrating third-party traffic data into their core business function areas such as system performance monitoring, project programming, traffic incident management, and safety analysis. However, linking private-sector data with agency asset inventory data has been a major challenge because the networks typically have different referencing systems, segmentation schemes, and representations of travel directions. This paper presents an effective conflation algorithm that associates spatial features between large-scale road networks. Instead of breaking lines into smaller pieces, which is a common technique in transportation applications, we use an intersection-based approach that leverages the inherent topological similarities between networks. The underlying uncertainty and imprecision in network geometries and road names are addressed through application of a fuzzy logic inference technique. We then implement an effective mechanism to handle differences in representations of divided roadways and travel directions in the two networks. The algorithm was tested on Kentucky statewide roadway networks and achieved a matching accuracy of over 99%. This approach has been successfully applied by the Kentucky Transportation Cabinet in its project identification and prioritization process.

Risk assessment of dammed lakes in China based on Bayesian network

Scientific risk assessment of dammed lakes is vitally important for emergency response planning. In this study, based on the evolution process of the disaster chain, the logic topology structure of dammed lake risk was developed. Then, a quantitative risk assessment model of dammed lake using Bayesian network is developed, which includes three modules of dammed lake hazard evaluation, outburst flood routing simulation, and loss assessment. In the model, the network nodes of each module were quantified using statistical data, empirical model, logical inference, and Monte Carlo method. The failure probability of a dammed lake, and the losses of life and property were calculated. This can be multiplied to assess the risk a dammed lake imposes after the uniformization of each loss type. Based on the socio-economic development and longevity statistics of dammed lakes, a risk-level classification method for dammed lakes is proposed. The Baige dammed lake, which emerged in China in 2018, was chosen as a case study and a risk assessment was conducted. The obtained results showed that the comprehensive risk index of Baige dammed lake is 0.7339 under the condition without manual intervention, identifying it as the extra-high level according to the classification. These results are in accordance with the actual condition, which corroborates the reasonability of the proposed model. The model can quickly and quantitatively evaluate the overall risk of a dammed lake and provide a reference for decision-making in a rapid emergency response scenario.

A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning

Sentencing prediction is an important direction of artificial intelligence applied to the judicial field. The purpose is to predict the trial sentence for the case based on the description of the case in the adjudication documents. Traditional methods mainly use neural networks exclusively, which are trained on a large amount of data to encode textual information and then directly regress or classify out the sentence. This shows that machine learning methods are effective, but are extremely dependent on the amount of data. We found that there is still external knowledge such as laws and regulations that are not used. Moreover, the prediction of sentences in these methods does not fit well with the trial process. Thus, we propose a sentence prediction method that incorporates trial logic based on abductive learning, called SPITL. The logic of the trial is reflected in two aspects: one is that the process of sentence prediction is more in line with the logic of the trial, and the other is that external knowledge, such as legal texts, is utilized in the process of sentence prediction. Specifically, we establish a legal knowledge base for the characteristics of theft cases, translating relevant laws and legal interpretations into first-order logic. At the same time, we designed the process of sentence prediction according to the trial process by dividing it into key circumstance element identification and sentence calculation. We fused the legal knowledge base as weakly supervised information into a neural network through the combination of logical inference and machine learning. Furthermore, a sentencing calculation method that is more consistent with the sentencing rules is proposed with reference to the Sentencing Guidelines. Under the condition of the same training data, the effect of this model in the experiment of responding to the legal documents of theft cases was improved compared with state-of-the-art models without domain knowledge. The results are not only more accurate as a sentencing aid in the judicial trial process, but also more explanatory.

A Gaze into the Internal Logic of Graph Neural Networks, with Logic

Graph Neural Networks share with Logic Programming several key relational inference mechanisms. The datasets on which they are trained and evaluated can be seen as database facts containing ground terms. This makes possible modeling their inference mechanisms with equivalent logic programs, to better understand not just how they propagate information between the entities involved in the machine learning process but also to infer limits on what can be learned from a given dataset and how well that might generalize to unseen test data. This leads us to the key idea of this paper: modeling with the help of a logic program the information flows involved in learning to infer from the link structure of a graph and the information content of its nodes properties of new nodes, given their known connections to nodes with possibly similar properties. The problem is known as graph node property prediction and our approach will consist in emulating with help of a Prolog program the key information propagation steps of a Graph Neural Network's training and inference stages. We test our a approach on the ogbn-arxiv node property inference benchmark. To infer class labels for nodes representing papers in a citation network, we distill the dependency trees of the text associated to each node into directed acyclic graphs that we encode as ground Prolog terms. Together with the set of their references to other papers, they become facts in a database on which we reason with help of a Prolog program that mimics the information propagation in graph neural networks predicting node properties. In the process, we invent ground term similarity relations that help infer labels in the test set by propagating node properties from similar nodes in the training set and we evaluate their effectiveness in comparison with that of the graph's link structure. Finally, we implement explanation generators that unveil performance upper bounds inherent to the dataset. As a practical outcome, we obtain a logic program, that, when seen as machine learning algorithm, performs close to the state of the art on the node property prediction benchmark.

What powers logical inference?

What powers logical inference?

PROBABILISTIC ENTAILMENT ON FIRST ORDER LANGUAGES AND REASONING WITH INCONSISTENCIES

AbstractWe investigate an approach for drawing logical inference from inconsistent premisses. The main idea in this approach is that the inconsistencies in the premisses should be interpreted as uncertainty of the information. We propose a mechanism, based on Kinght’s [14] study of inconsistency, for revising an inconsistent set of premisses to a minimally uncertain, probabilistically consistent one. We will then generalise the probabilistic entailment relation introduced in [15] for propositional languages to the first order case to draw logical inference from a probabilistic set of premisses. We will show how this combination can allow us to limit the effect of uncertainty introduced by inconsistent premisses to only the reasoning on the part of the premise set that is relevant to the inconsistency.