Logical Inference Research Articles

FUZZY INFERENCE SUBSYSTEM FOR CLASSIFYING THREATS TO COMPUTER INFORMATION

This article was analyzed the threats to computer data in computer systems and classified these threats by their attributes. The relationships between threat classes, attributes, and related security methods and tools have defined the mathematical model of the computer threat classification problem.
 Based on the model analysis was concluded that the computer threats classification problem belongs to difficult formalized problems and requires for its solution methods of intellectual analysis, one of which is the subsystem of logical inference implemented in this article.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 7. Internal logic 2 Oleg M. Polyakov

Introduction. The article continues a series of publications on the linguistics of relations (hereinafter referred to as R-linguistics) and is devoted to the study of the functioning of logical connectives with nouns and adjectives. The article is the second part of the discussion of internal logic, which examines the use of logical connectives within sentences. This research involves the formation of semantic logic, that is, logic that takes into account the semantics of sentences.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of internal logic, the previously formulated semantic concepts and operations are used.Results and discussion. Two types of negation are introduced and their properties are defined. The properties of the linguistic model are formulated. The use of logical connectives with nouns and adjectives is analyzed. It is shown that the connective NOT can be attributed to both external and internal logic. The connectives AND and OR do not have a logical content, but serve to identify the state of a particular concept. Three semantic substitutions are defined. The reasons for the stability of semantic interpretation when changing the state of relations are substantiated.Conclusion. Abandoning the traditional view of natural language logic means abandoning logical operations and inference. This forces us to consider logical operations that now take into account semantics, since they are related to the structure of the linguistic model. Analysis of the functioning of logical connectives with nouns and adjectives shows that they either provide identification of the state of concepts, or can be transferred to external logic, or can operate within the framework of a linguistic model. The formulated substitution rules provide semantically correct substitutions and justify the stability of semantic interpretation in the conditions of changing states of concepts.

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

Internet of Things (IoT) applications have led to exploding contextual data for predictive analytics and exploration tasks. Consequently, computationally data-driven tasks at the network edge, such as machine learning models’ training and inference, have become more prevalent. Such tasks require data and resources to be executed at the network edge, while transferring data to Cloud servers negatively affects expected response times and quality of service (QoS). In this paper, we study certain computational offloading techniques in autonomous computing nodes (ANs) at the edge. ANs are distinguished by limited resources that are subject to a variety of constraints that can be violated when executing analytical tasks. In this context, we contribute a task-management mechanism based on approximate fuzzy inference over the popularity of tasks and the percentage of overlapping between the data required by a data-driven task and data available at each AN. Data-driven tasks’ popularity and data availability are fed into a novel two-stages Fuzzy Logic (FL) inference system that determines the probability of either executing tasks locally, offloading them to peer ANs or offloading to Cloud. We showcase that our mechanism efficiently derives such probability per each task, which consequently leads to efficient uncertainty management and optimal actions compared to benchmark models.

The earth vibrates with analogies: The Dirac sea and the geology of the vacuum

The debate around analogy in modern physics that focuses on its role as a logical inference often correspondingly overlooks its historical dimension and the other equally important functions and aspects that are intertwined with this dimension. Inspired by a close investigation of the primary sources and archival material of a few historical actors, this paper lays out a framework on analogy-making which preserves as much as possible its historical complexity. While not losing sight of the logical role, our framework puts a special emphasis on the heuristic process, and aims at offering to the historian and philosopher of science as well as the physicist some tools to capture the subtle functions of analogical reasoning involved in such a process. After having traced it out theoretically, we make use of this framework to interpret the growth of the ideas of two remarkable physicists dealing with the multifaceted notion of vacuum in 20th century physics. We first consider the trajectory followed by John A. Wheeler, between the 1960s and 1970s, towards (in his own words) a “geology of the vacuum”; and then examine, starting from the hitherto neglected Japanese reception of the idea of Dirac sea in the early 1930s, the pathway that led Yoichiro Nambu to the discovery of spontaneous symmetry breaking.

Assessing Students’ Mathematical Knowledge with Fuzzy Logic

Assessing student mathematical knowledge is an important factor in the mathematics learning process because students obtain important feedback to improve their knowledge and learning. Despite the importance of student assessment, several researchers have shown that student grades comprise noncognitive and metacognitive factors and teachers’ prejudices and beliefs. One method to obtain a more objective view of student mathematical knowledge is through standardized assessments. In this paper, we analyze two methods of assessing student mathematical knowledge by considering their written and oral grades and achievements on the Italian National Assessment of Knowledge (INVALSI). The final grade was produced using the fuzzy logic inference system. It was tested on a sample of 2279 Grade 13 Italian high school students, who had both an oral and written grade in mathematics and who took the INVALSI assessment in the school year 2020–2021. Both tested fuzzy-logic-based assessment methods lowered the mean grades.

Through the eyes of a hunter: assessing perception and exclusion performance in ground-hornbills.

Logical inference, once strictly associated with spoken language, is now reported in some non-human animals. One aspect of logical inference, reasoning by exclusion, has been traditionally explored through the use of the cups task (cup A and cup B, if not cup A, then exclude cup A and select cup B). However, to fully understand the factors that drove the evolution of logical processes in animals, this latter paradigm needs to cover a taxonomically broader spectrum of species. In this study, we aimed to test the capacity of Southern ground-hornbills (Bucorvus leadbeateri) to show exclusion performance in a two-way object-choice task. First, we determined whether subjects could perceive and choose correctly between two containers (one rewarded, one unrewarded) using visual or acoustic cues (sensory phase). If successful, individuals were then presented with three experimental conditions (test phase): Full information (content of both cups revealed), Exclusion (content of the empty cup revealed), and Control (no content revealed). During the sensory phase, ground-hornbills succeeded in choosing the rewarded container only in the visual modality. Birds were able to select the rewarded container more than would be expected by chance in the Full information condition, but their performances were equal to chance in the Control condition. The without-learning performance of two individuals within the Exclusion condition indicates that this task is not trivial, which invites further investigation on this species' capacity to represent the dependent relationship between the cups (true logical inference).

Complexity of the interpretability logics ILW and ILP

Abstract The interpretability logic ILP is the interpretability logic of all sufficiently strong $\varSigma _1$-sound finitely axiomatised theories, such as the Gödel-Bernays set theory. The interpretability logic IL is a strict subset of the intersection of the interpretability logics of all so-called reasonable theories, IL(All). It is known that both ILP and ILW are decidable, however their complexity has not been resolved previously. In [10] it was shown that the basic interpretability logic IL is PSPACE-complete. Here we prove the same for ILP and ILW.

MaxSAT-based temporal logic inference from noisy data

MaxSAT-based temporal logic inference from noisy data

Data driven based fuzzy logic inference algorithm for quality of experience modeling for video streaming in LTE network: Case of Addis Ababa City LTE network

Data driven based fuzzy logic inference algorithm for quality of experience modeling for video streaming in LTE network: Case of Addis Ababa City LTE network

Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference Methodology for Soft Computation and Modeling of Ion Sensing Data of a Terpyridyl-Imidazole Based Bifunctional Receptor.

Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F- and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F- and deprotonation of the receptor by F- and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.

LTL-Specification of Bounded Counter Machines

The article revises the results of the work devoted to the problem of representing the behaviour of a program system as a set of formulas of the linear temporal logic LTL, followed by the use of this representation to verify the satisfiability of the program system properties through the procedure of proving the validity of logical inferences, expressed in terms of the LTL logic. The LTL logic is applied to bounded Minsky counter machines that are considered as program systems of which we need to get the specification of its behaviour. Earlier, when working with the temporal logic LTL as a program logic, a special pseudo-operator was actually introduced to refer to the previous values of variables involved in elementary propositions. Despite the fact that this pseudo-operator is easily implemented in the Cadence SMV verifier when proving the validity of logical LTL-inferences, the classical definition of the LTL logic does not imply its presence. In this article, only binary variables will be used to build an LTL-specification for the behaviour of a bounded counter machine, and tracking of previous values of these variables will be carried out exclusively within the LTL logic itself through the appropriate formulas.

Comparative Study of DGA Based Fault Diagnosis using ANN and Fuzzy Systems

Dissolved Gas Analysis (DGA) method for fault detection has been implemented using Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Adaptive Neuro Fuzzy Inference System (ANFIS). Incipient faults can be detected using DGA which provides reasonably good results. We have tried to improve this method in order to surpass its limitations. Comparative analysis using the mentioned methods have been done on IEC 599 standard, Rogers Ratio Method and Doernenburg’s method. Using Fault databases, the training has been done to improve the diagnostic capabilities. The obtained results clearly show the superiority of ANFIS on ANN and FL. Being a combination of both, its degree of accuracy in prediction and ease of use, provides a promising alternative in replacing the conventional methods.

Mathematical Proofs and Logical Inferences in the Philosophy Surrounding Coronavirus Pandemic in Nigeria

Mathematical Proofs and Logical Inferences in the Philosophy Surrounding Coronavirus Pandemic in Nigeria

Logic of Causal Inference from Data Under Presence of Latent Confounders

Logic of Causal Inference from Data Under Presence of Latent Confounders

CORRELATION OF SYMBOLIC LANGUAGE AND CAUSE AND EFFECT RELATIONSHIP TO MATHEMATIC MODELING IN SENIOR HIGH SCHOOL PHYSICS LESSONS

The generic science skills are the basic abilities that must be possessed by every student who studies science. This ability consists of various indicators namely direct observation, indirect observation, symbolic language, logical inference, causal relations, mathematical modeling, and construct concepts. This study only focused on indicators of symbolic language, causal relationships, and mathematical modeling. This research is a correlation study that aims to find out the relationship between symbolic language abilities and causal relationships on mathematical modeling abilities in physics subjects. The results of the study indicate that there is a relationship in the ‘sufficient’ category between symbolic language abilities and causal relationships to mathematical modeling abilities in physics subjects. Suggestions for further researchers are that in the learning process it should focus on the science process, which needs to guide students to develop their generic abilities in science, especially physics.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 7. Internal Logic 1

Introduction. The article continues a series of publications on the linguistics of relations (hereinafter referred to as R-linguistics) and is devoted to the study of the functioning of logical connectives with verbs. The article is the first part of the discussion of internal logic, which examines the use of logical connectives within sentences. This research involves the formation of semantic logic, that is, logic that takes into account the semantics of sentences.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of internal logic, the previously formulated semantic concepts and operations are used.Results and discussion. The use of logical connectives with verbs is analyzed. It is shown that these connectives actually refer to external logic, although in some cases it is necessary to adjust part of sentence, taking into account the semantics of the linguistic model. The concept of semantic substitution is defined and the first rule of substitution for verbs is formulated and justified.Conclusion. Abandoning the traditional view of natural language logic means abandoning logical operations and logical inference. This forces us to consider logical operations that now take into account semantics, since they are related to the structure of the linguistic model. Analysis of the functioning of logical connectives with verbs shows that they are related to the linguistic model, which leads to the need for various semantic transformations of the text when such connectives are used. In particular, the use of logical connectives can lead to the loss of the meaning of the text. The rejection of logical inference is compensated by the appearance of semantic substitution rules, one of which is considered in this paper.

A novel fuzzy logic scheme for PID controller auto-tuning

This paper presents a novel method for PID (proportional–integral–derivative) controller auto-tuning based on expert knowledge incorporated into a fuzzy logic inference system. The proposed scheme iteratively tries to improve the performance of the closed-loop system. As performance measures, the proposed scheme uses the characteristics of the step response (rise time, overshoot, and settling time). PID parameters in the first iteration can be calculated based on the basic open-loop step response experiment or it is possible to use current parameters. In each successive iteration, step response characteristics are measured and the relative changes expressed in the percentage of value in the first iteration are calculated and converted into linguistic values. The fuzzy expert system computes fuzzy values that are used after defuzzification as multiplying factors for current PID parameters. To achieve a balance between the aggressive and robust closed-loop response, as well as between the slower and the faster one, the fuzzy expert system works in three operating modes: the one for speeding up the system, the one for reducing the overshoot, and the one for a balanced reduction of rise time and overshoot. The performance and robustness are verified by computer simulation using an extensive range of different processes.

A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual’s best solution (pbest) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

Leveraging Virtual Learning to Facilitate Training Transfer in VUCA Times

The purpose of this case study is to highlight the efficacy of Virtual Learning as a tool to facilitate training transfer of behavioral skills and knowledge imparted during the course of a leadership intervention in a manufacturing firm during the pandemic lockdown. A mixed-method approach involving qualitative depth interviews and survey questionnaire responses were used to draw logical inferences. The findings of the study revealed that virtual learning supports effective training transfer of behavioral skills and knowledge. The learners also highlighted virtual learning to be offering convenience and an enriching learning experience. The study offers practical insights for learning and development practitioners on leveraging virtual learning as a powerful tool to promote the transfer of trained knowledge at the workplace. The study is first of its kind as it offers key insights on training transfer in organizational learning and development context, gathered from personal experiences of learners involved in the training intervention conducted during the pandemic lockdown.

Cross-system legacy data applied to digital soil mapping: A case study of Second National Soil Survey data in China

The accuracy of digital soil maps (DSMs) is influenced by the quality of the input data. The application of legacy data obtained from past soil surveys was considered a potential method to improve DSM input data. However, changes in soil classification systems have occurred in many countries, and legacy data are therefore not directly applicable in the generation of the latest DSMs. This research established a cross-system framework for digital soil mapping, which enabled the conversion of legacy data and application to a new classification system for digital soil mapping. Jiangxi Province, China, was considered to evaluate this framework, where the classification system transitioned from the Genetic Soil Classification of China (GSCC) to the China Soil Taxonomy (CST). Second National Soil Survey legacy point data acquired based on the GSCC were converted and updated. Moreover, boundary placement of legacy polygon maps (BP-LPMs) containing GSCC soil class maps and soil parent material maps were converted into the expert knowledge of CST soil class boundaries. Fuzzy logic inference was employed to construct DSMs. The actual sampling points were extracted and considered in simple digital soil map (S-DSM) construction. The actual sampling points, the converted legacy point, and expert knowledge of soil class boundaries were used for hybrid digital soil map (H-DSM) construction. Cross-validation was used to evaluate the accuracy of digital soil mapping. The evaluation results indicated that compared to the S-DSM, the total accuracy of the H-DSM were improved by 17.0%, 15.1%, and 9.4% at the soil Order, Suborder, and Group class levels, respectively, and the producer's accuracy was further improved for most soil classes. This study demonstrated that legacy data obtained from past soil classification systems could be applied to improve digital soil maps under new soil classification systems through updating and conversion. This cross-system framework provides important implications for many countries with similar legacy data contexts.