Logic Of Knowledge Research Articles

О феноменологии логических переживаний в «Логических исследованиях» Э. Гуссерля

In this paper, the author endeavors to clarify the relationship between logic and phenomenology in the context of a detailed examination of E. Husserl’s inquiries into the nature of logical knowledge. This endeavor appears justified not only from a historical-philosophical perspective but also from a purely conceptual standpoint: the present study investigates the role that E. Husserl assigns to ideal structures within the experience of consciousness, interpreted by him as logical objects. Through a sequential analysis, the philosophical approach of Husserl is illustrated by elucidating the essence of his anti-psychologist stance regarding logic, explicating the idea of the intentional character of consciousness, and subsequently synthesizing these two perspectives to reveal the reflexive nature of logic, which is necessitated by consciousness for self-discovery and self-clarification. It is emphasized that Husserl’s examination unfolds in a manner that defines the typological proximity of the considered acts of consciousness. By addressing these dimensions, the study aims to contribute to a deeper understanding of how Husserl intertwines phenomenology and logic, and how this interplay informs the dynamics of logical experiences within the framework of conscious acts.

Simulating judicial trial logic: Dual residual cross-attention learning for predicting legal judgment in long documents

Legal Judgment Prediction (LJP) plays a vital role in judicial assistance systems, aiming to predict judgment outcomes automatically from the fact descriptions in legal cases. A key challenge in LJP lies in effectively capturing decisive information that influences legal judgments — such as criminal events, behaviors, and consequences — especially from lengthy legal documents. Existing approaches, which incorporate domain-specific knowledge, have attempted to improve the ability to capture decisive information but often fail to adequately address the complexities of longer texts and may introduce noise, leading to incorrect predictions. To overcome these limitations, we propose a novel method, JuriSim, for predicting Chinese criminal legal judgments in long documents by incorporating the knowledge of judicial trial logic. Specifically, JuriSim extracts legal events and generates rationales based on fact descriptions to capture the decisive information that influences judgment outcomes. Then, a dual residual cross-attention mechanism is introduced to interactively process facts, events, and rationales for predicting relevant legal statutes, charges, and term of penalties. This mechanism allows the model to reduce the loss of important information and the retention of incorrect information during the aggregation process. Furthermore, we present a constrained cross-entropy loss, utilizing the topological relationship between charges, terms, and applicable law statutes. Experiments conducted on the publicly available CAIL-Long criminal dataset demonstrate the efficiency of the JuriSim framework in predicting legal judgments, especially for cases involving long documents. JuriSim_Lawformer shows a relative improvement of 3.45% in Macro-F1 for charge prediction and 3.05% for term of penalty prediction, compared to Lawformer.

Inferential knowledge and epistemic dimensions

Abstract Knowledge representation is one way to exploit expertise in a given domain by logical means. But, what kind of knowledge does one acquire from an inference (or inference on a query result over a knowledge base)? Such a question may appear awkward since the answer seems so obvious: from an inference, one simply acquires knowledge. This is undoubtedly the case when only one type of knowledge (for instance, expert knowledge) is involved in an inference. What if several types of knowledge are involved? What type of knowledge can one deduce from a plurality of knowledge types? I claim that reasoning with different knowledge concepts requires a fine-grained representation of knowledge in which every knowledge type finds a singular expression in order to avoid some epistemic equivocity associated with a coarse-grained representation of knowledge. In the first part of the paper, I revisit the Muddy Children Puzzle, which usually serves to illustrate common knowledge in dynamic epistemic logic. I try to show that this problem also shows some sort of epistemic equivocity between concepts of knowledge and, consequently, that the problem calls for some epistemological refinements concerning the representation of the types of knowledge at play in an inference. In the second part, I address this issue from a semantic point of view, and I develop a fragment of epistemic logic capable of providing a solution to the problem of epistemic equivocity.

The logic as an element of legal considerations and its application in the process of drawing up legal documents

The article analyses logic as an element of legal reasoning and its application in the process of drafting legal documents. Mastering the art of drafting legal documents is an invaluable skill in the professional legal activity of representatives of various legal professions, which can ensure their comprehensibility and clarity, and accordingly, the proper protection of rights. An integral part of this skill is logic as an element of legal reasoning, the correctness of its application. Representatives of various legal professions, whose work is related to legal norms and the drafting of legal documents, refer to elementary rules of logic. Legal reasoning related to the interpretation of normative legal acts and their application to cases, during the evaluation of evidence and making final decisions, which are reflected in legal documentation, is an important means of achieving the rule of law. Due to the special legal thinking, lawyers get the ability to consider any thing from a concrete and not an abstract point of view. Every successful lawyer must be well versed in the laws of formal and informal logic, the rules of operating concepts, judgments, and inferences. Logic is the science of how we should reason if our goal is to discover truth. Neglecting the laws of formal logic in the process of drafting legal documents (identity, contradiction, excluded third party and sufficient grounds) leads to errors. The lack of necessary skills can manifest itself in the composition of the text of legal documents in the form of errors, which, in turn, can cause serious consequences in the form of violations of human rights and freedoms and, as a result, the principle of the rule of law. Knowledge of formal and informal logic, the peculiarities of its application, will contribute to the development of the skills of correct and effective legal reasoning, which will master the art of drafting legal documents – an invaluable skill in the professional legal activity of representatives of various legal professions, which is the unconditional foundation of the observance of human rights and freedoms in our state, as well as implementation of the rule of law principle.

Template-Based Contrastive Distillation Pretraining for Math Word Problem Solving.

Since math word problem (MWP) solving aims to transform natural language problem description into executable solution equations, an MWP solver needs to not only comprehend the real-world narrative described in the problem text but also identify the relationships among the quantifiers and variables implied in the problem and maps them into a reasonable solution equation logic. Recently, although deep learning models have made great progress in MWPs, they ignore the grounding equation logic implied by the problem text. Besides, as we all know, pretrained language models (PLM) have a wealth of knowledge and high-quality semantic representations, which may help solve MWPs, but they have not been explored in the MWP-solving task. To harvest the equation logic and real-world knowledge, we propose a template-based contrastive distillation pretraining (TCDP) approach based on a PLM-based encoder to incorporate mathematical logic knowledge by multiview contrastive learning while retaining rich real-world knowledge and high-quality semantic representation via knowledge distillation. We named the pretrained PLM-based encoder by our approach as MathEncoder. Specifically, the mathematical logic is first summarized by clustering the symbolic solution templates among MWPs and then injected into the deployed PLM-based encoder by conducting supervised contrastive learning based on the symbolic solution templates, which can represent the underlying solving logic in the problems. Meanwhile, the rich knowledge and high-quality semantic representation are retained by distilling them from a well-trained PLM-based teacher encoder into our MathEncoder. To validate the effectiveness of our pretrained MathEncoder, we construct a new solver named MathSolver by replacing the GRU-based encoder with our pretrained MathEncoder in GTS, which is a state-of-the-art MWP solver. The experimental results demonstrate that our method can carry a solver's understanding ability of MWPs to a new stage by outperforming existing state-of-the-art methods on two widely adopted benchmarks Math23K and CM17K. Code will be available at https://github.com/QinJinghui/tcdp.

KaTaGCN: Knowledge-Augmented and Time-Aware Graph Convolutional Network for efficient traffic forecasting

Dynamic spatio-temporal dependencies and temporal patterns in traffic series are critical factors affecting traffic forecasting accuracy. Due to the intrinsic challenges of incorporating explicit, logical knowledge into the implicit black-box learning process of neural networks, only a few methods effectively use prior knowledge to improve the learning of traffic forecasting. To tackle this problem, we introduce a new approach called Knowledge-augmented and Time-aware Graph Convolutional Network, namely KaTaGCN. At its core, we have created a knowledge-augmented module that boosts the diffusion weights between closely related adjacent nodes in graph learning. This is achieved by implementing a new loss function. Then, to learn the periodic implicit relationship between these timestamps and traffic signals, the weights and biases are chosen adaptively to be trained based on the timestamps of each sample. Finally, a gated spatio-temporal mapping module regresses high-dimensional embedded features from spatial and temporal dimensions. KaTaGCN is structured without any attention mechanisms or recurrent neural networks. Extensive experimental results on six real-world public traffic datasets demonstrate that KaTaGCN achieves an average improvement of 4.29% in forecasting performance compared with suboptimal results.

YUKTI PRAMANA IN MANAGEMENT 0F DISEASE

Ayurveda is a very ancient science with numerous theories for disease management, one of which is the philosophy of Pramana. Pramana is a crucial tool for managing diseases. In Ayurvedic texts, Pramana is also known as Pareeksha i.e. Examination. Yukti Pramana is a part of this philosophy, providing logical knowledge and arrangements of wisdom for effective treatment and achieving desired results. Acharya Charak has told four types of Pramana i.e. Aptopdesha, Pratyaksha, Anumana and Yukti Pramana. The proper arrangement of objects to gain knowledge is a Yukti. This article shows a short review of Yukti Pramana in Disease Management.

Logika, Penalaran, Dan Argumentasi Hukum

Nowdays, the understanding and knowledge in logic, reasoning, and legal argumentation become moreabsolute necessity not only for civitas academica in philosophy and law but most important for legal practitioners as polices, judges, prosecutors, attorneys, lawyers, and even all members of society whosedealing with legal problems everyday. As apart of generally reasoning, legal reasoning, although have some different characteristics, is attached to valid principles of reasoning such as rules of reasoning, rules of sillogism, rules of inductive probability, and informal fallacies. Thus legal reasoning is not a distinct and separate kind of reasoning from logic as science of how to think rightly, appropriately, and valid (as a branch of philosophy) but rather how to applied rules of reasoning from logic principles to legal problems. This article explores the rules of thinking in induction and sillogism. IRAC (Issue, Rule, Argument, dan Conclusion) model as an application of inductive and sillogistic reasoning in law will to end this article.

Advanced Query Functionalities in Natural Logic Knowledge Bases

Natural logics are formal logics characterized by expressions that bear a resemblance to simplified natural language sentences. The inherent readability of natural logics renders them suitable also for large knowledge bases. Within the realm of natural logics, the rules of logical proof are applicable directly to the sentences constructed using this framework. This direct application ensures explainability of query inferences. In the paper we describe a specific natural logic named NaturaLog, suited for ontology-structured knowledge bases. We outline how NaturaLog can be effectively implemented into a database system to facilitate deductive querying. The primary focus of this paper lies in exploring various query functionalities and elucidating methods to achieve these capabilities, also when dealing with large-scale knowledge bases.

Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example.

The rebirth of metaphysics and relevance of dialectics

At the new turn of historical development, it is necessary to turn to the understanding of dialectics and the newest forms of metaphysics, recognizing the objective dialectic of the material world, which is reflected in the subjective dialectic of concepts. For Aristotle's formal logic, there are true judgments and false judgments, and if we take the law of the excluded third, then even stricter: either yes or no. However, the metaphysical way of understanding reaches a limit beyond which it becomes limited and entangled in contradictions, since it deals with objects as unchangeable. Sophistry was revived recently in the third millennium, because capitalism needs it in the context of the digital transformation of the state and the current controversy over the new world War reveals a lot of techniques of sophistic thinking. Formal logic works fine in the conditions of everyday practical relations of people however, knowledge of dialectical logic is required to solve the global problems of our time.

Overview of knowledge reasoning for knowledge graph

Knowledge graphs are large-scale semantic networks that considerably impact knowledge representation. Mining hidden knowledge from existing data, including triplet knowledge reasoning, is a primary objective of knowledge graphs. With the development of Neural Network (NN) and Deep Learning (DL), the interpretability of triplet knowledge reasoning gradually decreases; furthermore, what machines learn is not actual reasoning but digital reasoning shortcuts. To solve this problem, more background knowledge needs to be introduced into knowledge graphs: causal graphs can offer valuable causal logic knowledge for reasoning; temporal quadruples can provide essential temporal distribution details; and commonsense graphs can furnish pertinent commonsense understanding to support reasoning. In recent years, many scholars have incorporated additional background knowledge into knowledge graphs to construct more complex reasoning mechanisms. This paper reviews the basic concepts and definitions of knowledge reasoning and the reasoning methods used for knowledge graphs. Specifically, we dissect the reasoning methods into four categories: triplet reasoning, causal inference, temporal inference, and commonsense reasoning. Finally, we discuss the remaining challenges and research opportunities related to knowledge graph reasoning.

Stable Model Semantics for Description Logic Terminologies

This paper studies a stable model semantics for Description Logic (DL) knowledge bases (KBs) and for (possibly cyclic) terminologies, ultimately showing that terminologies under the proposed semantics can be equipped with effective reasoning algorithms. The semantics is derived using Quantified Equilibrium Logic, and---in contrast to the usual semantics of DLs based on classical logic---supports default negation and allows to combine the open-world and the closed-world assumptions in a natural way. Towards understanding the computational properties of this and related formalisms, we show a strong undecidability result that applies not only to KBs under the stable model semantics, but also to the more basic setting of minimal model reasoning. Specifically, we show that concept satisfiability in minimal models of an ALCIO KB is undecidable. We then turn our attention to (possibly cyclic) DL terminologies, where ontological axioms are limited to definitions of concept names in terms of complex concepts. This restriction still yields a very rich setting. We show that standard reasoning problems, like concept satisfiability and subsumption, are ExpTime-complete for terminologies expressed in ALCI under the stable model semantics.

No College Students Left Behind: Writing Module Incorporating Critical Thinking Skills for the National Postgraduate Entrance Examination in China

In recent years, Chinese universities and scholars have recognised the significance of critical thinking. Despite this growing awareness, integrating critical thinking skills into the curriculum remains a challenge in China. To gain entry to postgraduate programmes, students majoring in management in the country are required to take a National Postgraduate Entrance Examination (NPEE) that emphasises logical knowledge related to critical thinking and writing. Hence, this study aims to develop a writing module that incorporates critical thinking skills for college students in China. To achieve this, a mixed methods approach was employed. Firstly, a prototype writing module was created using the Analyse, Design, Develop, Implement and Evaluate (ADDIE) Model and verified by content experts for content validity. Subsequently, the module was implemented in a traditional classroom setting. For the pilot study, a pilot sample of 50 students was randomly chosen from a training centre in China that assists college students to prepare for the examination. For the next stage, 100 students were selected as the study sample. After implementing the writing module, both quantitative and qualitative analysis were conducted. The results indicated a significant improvement in participants' scores in the pre-test and post-test, demonstrating the effectiveness of the writing module in fostering critical thinking skills. The writing module developed by the researchers will have a profound effect on Chinese teachers' teaching in the future. It will provide them with clear instructional materials to cultivate Chinese students' critical thinking abilities, ensuring that no college students will be left behind.
 
 Received: 27 October 2023 / Accepted: 22 December 2023 / Published: 5 January 2024

Non-Participant Observations in Experience-Based Codesign: An example using a Case Study Research approach to explore Emergency Department Care

Previous reviews of experience-based codesign (EBCD) projects have shown non-participant observations (NPOs) are omitted or unreported in the majority of studies, despite academics’ insistence of their value. We undertook Stage 1 of an EBCD project using a Case Study Research approach to organise the inquiry. NPOs were used as the first object (frame of analysis) of the case subject, exploring and understanding the experience of users and providers of ED care. 162 hours of NPOs were conducted by six research team members across three hospital Emergency Departments (EDs), representing a local nested case. The four principles of EBCD were used to guide the inquiry, providing rich understanding and description of the context, and valuable insights into critical issues. Summarised NPO narratives allowed the research team to familiarise themselves with the different physical environments, workflows, and processes, as well as think more deeply about work and interactions. Conducted within complex healthcare settings, this example demonstrates the value of observing Work-As-Done and has advanced knowledge of institutional logic that will be critical in considering realistic and sustainable change initiatives. Commitment to intentional design and well-reported research methods for exploring and understanding lived experience can satisfy academic audiences of the study integrity and provide comprehensive information for those undertaking accelerated EBCD.

How can population models contribute to contemporary pest management practices?

AbstractPopulation models provide a logical knowledge base before conducting laborious and expensive field experiments. Historically, two types of population models have been developed: highly realistic simulations and simple analytical models. Highly realistic simulations comprise a complicated systems model, whereas simple analytical models comprise various analytical models that focus only on the fundamental structure of the target pest population. Although both approaches have contributed to pest management science, each has limitations, poor predictability, and lacks substantial connections to reality. Assimilation by state-space modeling, in which observation and process models are jointly incorporated, is a good compromise between a simple model and reality in nature. In the big data era, artificial intelligence (AI), specifically aimed at high predictability, has recently become popular. If vital physical and biological records are automatically censored in the field with high precision, AI will produce the most plausible predictions, providing the best practical solution given our current knowledge. AI can be a powerful tool in the contemporary world; however, deductive modeling approaches are still important when considering the behavior of AIs and may also provide important insights to detect deficient information in the data.

Debiasing thinking among non-WEIRD reasoners

Human reasoning has been shown to be biased in a variety of situations. While most studies have focused on samples of WEIRD participants (from Western Educated Industrialized Rich and Democratic societies), the sparse non-WEIRD data on the topic suggest an even stronger propensity for biased reasoning. This could be explained by a competence issue (people lack the ability to integrate logical knowledge into their reasoning) or a performance issue (people possess the logical knowledge but do not know it is relevant). We addressed this question using a debiasing paradigm with the base-rate task on a sample of non-industrialized people, the Himba of Namibia. After a short training, most participants were debiased, lending credence to the performance account. Debiasing was however to some extent boosted by schooling and living environment suggesting that competence also plays a role (in that more acquired knowledge allows for a higher training benefit). Results imply that debias interventions can be successfully employed to boost sound reasoning around the world.

Comparison and analysis of different ECG denoising methods

With the improvement of medical level, electrocardiogram (ECG) is widely used for disease diagnosis. A lot of pathological and physiological information is contained in the ECG, which can be used to record the point activity of normal human heart and diagnose various heart disease. However, the acquired ECG signals are always contaminated with noise which caused by acquisition equipment or other circumstance. Therefore, Efficient denoising method is very important. In this paper, three typical ECG signal denoising methods are listed, including FIR filtering, wavelet filtering and EMD filtering. In this paper, the principles of the three filtering methods are introduced in detail, and their effects are compared. By comparison, it intuitively shows the processing effects of each method on ECG signals. Meanwhile, a simple Butterworth filter is designed to denoise a standard wave, which represents the logic knowledge related to denoising. It is very significant for the medical signal processing field and help to research more effective signal processing methods.

The Role of Strategic Awareness in Developing the Practice of Strategic Foresight in Business Organizations -Epistemological Perspective-

Current research aimed at investigating the role of strategic awareness in developing the practice of strategic foresight in business organizations; in an attempt to cope with an intellectual dilemma summarized in that the practice of strategic foresight is still very limited in business organizations in general and in Arab business organizations in particular, for three reasons, first: its novelty and lacking its awareness; second: anxiety towards the future; third: nature of consideration to the future. Furthermore, to achieve the foregoing, the qualitative approach was used employing the epistemological prospective, because this prospective fathoms on rooting the knowledge for exploring factors, variables, and dimensions affecting the stimulation to use the strategic foresight, and reduce the barriers to practicing this prospective in the lived reality thru stimulating out-of-the-box thinking on the one hand, and increase the cognitive movement sensibly, analytically, critically, synthetically, and evaluative on the other hand. Yet, as for the study sample, it was purposive consisting of many studies, literature, documents, and documented experiments that were predetermined and purposively. However, the research reached a number of conclusions most notably: that strategic awareness plays a (synergistic) role to strategic foresight from the realism simulation perspective. This research depends on the reliable knowledge already gathered from people of knowledge and books. Whereas, strategic awareness plays the (influential) role of strategic awareness from the rationalism simulation perspective which uses logical knowledge that inquires into the fact thru knowing the cause. The research has presented several recommendations first of which: the necessity to invest in the relation of strategic awareness with strategic foresight to activate the synergistic role thru stimulating critical thinking where this type of thinking would help in developing the area of strategic foresight. As well as to activate the influential role thru developing creative thinking, because this thinking encourages the detection of new relations, the adoption of authentic solutions, and the capability to sense problems.

The Significance of Applying Logical Knowledge in Addressing Viral Culture on Social Media

The Significance of Applying Logical Knowledge in Addressing Viral Culture on Social Media