Rule-based Research Articles

ОЦІНКА ОБЧИСЛЮВАЛЬНОЇ СКЛАДНОСТІ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given. A mathematical apparatus for estimating the asymptotic complexity of a genetic algorithm is presented, which takes into account the costs of forming the initial population and performing evolution. Evolution occurs through iterations, during which generations of individuals are subjected to certain operations in order to find an optimal solution (crossing, mutation, chromosome decoding, etc.). GA, as a global search algorithm, is considered to find the optimal path without getting stuck in local minima. To assess the computational complexity of GA, we consider solving the traveling salesman problem (TSP) for 28 cities of Ukraine using a modified TSPLIB library and the DEAP platform created in the Python programming language. A block diagram of the GA is presented, the main elements of which are the tournament selection operator, the ordered crossover operator, and the inversion mutation operator. The influence of the population size and the number of generations on the asymptotic complexity of the genetic algorithm in solving the TSP problem is studied. The study considered changing the size of the GA population from 50 to 500 with a step of 50, while for each such value four sets of the number of generations were modelled: from 50 to 200 with a step of 50. Based on the obtained results, we show a linear dependence of the GA execution time on the size of the considered input data. It is shown that the smallest time complexity of the presented GA for the given TSP problem is 0.33848 seconds with a population size of 50 and a similar number of generations, while the largest value is 3.752734 seconds with a population size of 500 and a number of generations of 200. The obtained results can be used to optimise the performance of a GA in the TSP problem.

Cybersecurity Measures Safeguarding Digital Assets and Mitigating Risks in an Increasingly Interconnected World

As interconnectivity and reliance on digital technologies continue to rise exponentially, so too do cybersecurity threats and risks. Malicious cyber actors are employing increasingly sophisticated techniques to target valuable data and disrupt critical systems and infrastructure. The impacts of successful cyber-attacks can be financially devastating and undermine an organization's reputation and customer trust. This paper examines how firms may utilize strong cybersecurity to protect digital assets and reduce risks in a connected society. As technology and communications improve, cyber threats do too. Problems keep popping up. Malware, phishing scams, and data breaches still harm businesses. Hackers can also get in simpler with more linked devices and the Internet of Things. This makes protecting digital assets and systems, networks, and critical infrastructure from sophisticated cyberattacks even more crucial. Cybercriminals can steal data, intellectual property, money, and customer information in many ways. Advanced persistent threats are used by state-backed parties for eavesdropping and geopolitics. Therefore, organizations in all professions and industries must prioritize safety and risk reduction. Businesses may increase their safety with technical, process-based, and strategic cybersecurity solutions, according to the report. This requires strong access restrictions, networks, encryption, software security, and audits. Human aspects like cyber awareness training and a comprehensive plan that matches an organization's risks and resources are considered. A thorough security plan discusses international conventions, following the rules, and using modern technologies. Two case studies demonstrate real-world operations. The first describes how a large retailer stopped a complex APT with numerous defenses. Learn how crucial segmentation, detection tools, incident reaction, and public-private partnerships are. The second one examines a major hotel chain data breach and offers simple measures like strong passwords and vendor tracking to reduce similar dangers. Even if digital adversaries are getting smarter, basic cybersecurity "hygiene." can block many attempts, the report concludes. No organization is totally safe, but following basic rules, laws, and best practices can help protect people, organizations, and governments' assets in today's linked globe. Researchers may investigate how blockchain and AI might lessen hacking risks.

A logic device based on memristor-diode crossbar and CMOS periphery as spike router for hardware neural network

A programmable logic device based on a memristor-diode crossbar and CMOS logic has been developed. The crossbar implements NAND logic gates using memristor ratioed logic and CMOS inverters. The digitally controlled peripheral circuit provides digital signals transmission and allows modification and evaluation memristor states in the crossbar. The proposed logic device circuit requires fewer transistors than known analogues and less area on the chip.The maximum size of the crossbar in a logic device is estimated by numerical simulation at the level of electrical circuits. The limited size is caused by the degradation of the logic levels voltages in the memristor-diode crossbar. The operability of peripheral circuits as part of a complete electrical circuit of a logic device is demonstrated during the simulation of the execution of logical operations, the processes of modification and evaluation states of individual memristors.

Analisis Penerapan Sanksi Pidana Pelaku Residivis Tindak Pidana Narkotika

Abstract In positive law, narcotics crimes are qualified as extraordinary crimes, because the impact of these crimes has a dangerous situation that is widespread. Law No. 35 of 2009 is the basic rule for law enforcement officers to carry out law enforcement for perpetrators. In Article 54 of the Law it is explained that judges are obliged to consider conducting medical rehabilitation for drug users. In Decision No. 745/Pid. Sus/2018/PN Srg, the defendant is a drug case recidivist who had previously been rehabilitated. By using a normative juridical research method that uses the approach of legislation and legal principles, this study aims to examine and determine the application of punishment to recidives in Decision No. 745/Pid. Sus/2018/PN Srg by using the theory of judges' considerations and the purpose of sentencing. The results of this study are the application of sanctions against narcotics crime recidivists must use the purpose of punishment which results in a deterrent effect for the perpetrator, so that the perpetrator does not repeat the action.

The generalised distribution semantics and projective families of distributions

We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic programming as such to encompass frameworks from probabilistic databases, probabilistic finite model theory and discrete lifted Bayesian networks. To demonstrate the usefulness of such a general approach, we completely characterise the projective families of distributions representable in the generalised distribution semantics and we demonstrate both that large classes of interesting projective families cannot be represented in a generalised distribution semantics and that already a very limited fragment of logic programming (acyclic determinate logic programs) in the deterministic part suffices to represent all those projective families that are representable in the generalised distribution semantics at all.

Research on the optimization of belief rule bases using the Naive Bayes theory

The belief rule base is crucial in expert systems for intelligent diagnosis of equipment. However, in the belief rule base for fault diagnosis, multiple antecedent attributes are often initially determined by domain experts. Multiple fault symptoms related to multiple antecedent attributes are different when an actual fault occurs. This leads to multiple antecedent attributes matching with multiple fault symptoms non-simultaneously, thereby resulting in a fault diagnosis lacking timeliness and accuracy. To address this issue, this paper proposes a method for belief rule-based optimization based on Naive Bayes theory. First, a fault sample is taken in a long enough window and divided into several interval samples, making the analysis samples approximate the overall samples. Second, using Gaussian mixture clustering and Naive Bayes optimization, iteration is performed over the threshold and limit values of fault symptoms in the belief rule base based on the requirements of the timeliness and accuracy of fault diagnosis results. Finally, the belief rule base is optimized. Using fault samples from high-pressure heaters and condensers, the validation results show that there is a there is a significant improvement in the timeliness and accuracy of fault diagnosis with the optimal belief rule base.

A semantic reasoning-based emergency rescue assistant decision method for maritime accidents involving chemicals

Maritime accidents involving chemicals (MAICs) constitute a unique category of marine emergency characterized by low frequency, high risk, and complex disposal challenges. These accidents often entail fires, explosions, and pollutant leakage, demanding effective emergency responses. The success of MAICs rescue operations relies on multiple factors, encompassing the structural features of the involved vessel, personnel, cargo, marine environment, and the availability of resources and response procedures. Proficiency in various professional fields, including ship transportation, chemistry, firefighting, and environmental science, is essential for informed decision-making. This paper addresses the need for a standardized description of knowledge in the MAICs emergency rescue decision-making process, aiming to enhance practical application frequency and decision-making efficiency. Employing ontology theory, the approach involves structurally modeling the knowledge of MAICs emergency rescue, constructing an ontology model, and establishing a decision rule base. Validating the proposed method, the emergency response to the “Longqing 1″ accident is examined as a case study, illustrating its potential for supporting MAICs emergency rescue decision-making. The application of ontology theory in this context holds both theoretical and practical significance. It facilitates the sharing, reuse, and expansion of knowledge in the emergency rescue field, providing crucial technical support for intelligent decision-making in MAICs emergencies.

A novel approach detection for IIoT attacks via artificial intelligence

A novel approach detection for IIoT attacks via artificial intelligence

Direct and indirect switch from semiconductor to metallic behavior defined by the competition between Cation–Anion–Cation and Cation–Cation interactions leading to an important technological aspect of perovskite systems

We discuss in this work the key interactions that explain the possible electrical behaviors in different perovskite systems. Multi-behavior observation characterizes each frequency and temperature dependence of various perovskite systems. We classify such observations according to the temperature regimes. Indeed, a direct switch from semiconductor to metallic feature describes the conductivity evolution indicating the direct change from the localized (cation–anion–cation interactions) to delocalized (cation–cation interactions) state. The metallic character is reached due to the reinforcement of charge delocalization effect at high temperatures and frequencies. An indirect transformation separated by a frequency-independent regime is equally observed and discussed. Subsequently, a mixed regime is described by the conductivity temperature-independence which is explored at a specific frequency. Such investigations lead to demonstrate basic and common rules relevant for oxide-perovskites multi-behavior. Equally, important technological aspects are confirmed for systems that displays such multi-behavior character according to the temperature range.

The Narrations of Female Companions in Surat Al-Ma’idah According to Imam Al-Suyuti in his Book Al-Dur Al-Manthur fi Al-Tafsir Bilma’athoor (Asma’ bint Yazid as an Example)

This research aims to briefly introduce Imam Al-Suyuti and study the narrations of one of the female companions, other than the Mothers of the Believers, in Surah Al-Ma’idah in his book “Al-Dur Al-Manthur fi Al-Tafsir bi Al-Ma’thur”. This woman is Asma’ bint Yazid. This study will be through basic rules in the science of Hadith and the science of interpretation and combining them. It is through these narrations it becomes clear to us the explanation of its narrators, the judging on it, the types of chain of transmission, the general meaning of the narration, and what can be learned from it, as well as an explanation of the exegetical aspects of the verse related to the narration in terms of explaining the interpretation, the reasons for the revelation, the aspects of the readings, and the rhetorical aspects of the verse, if any.

Fruit Sorting by Pizzo-electric Sensor and PLC Controlling

Abstract: The fruit sorting process traditionally relies on visual inspection, primarily considering size as a key quality parameter. However, industries engaged in large-scale fruit trading have turned to image processing technology for sorting, despite its high cost and impracticality for small traders. This paper proposes an alternative sorting system that offers an economical solution suitable for automated fruit sorting at various scales. Aim: The aim of this study is to develop an economical automated fruit sorting system capable of classifying fruits based on their weight, thereby eliminating the need for manual labor and improving sorting accuracy and efficiency. Place and Duration of Study: Department of Electrical Engineering Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, between July 2023 to April 2024. Study Design: This study employs a practical approach to design an automated fruit sorting system that utilizes Programmable Logic Controller (PLC) technology in conjunction with load cells. The system is designed to classify fruits based on their weight as they move along a conveyor belt, thereby automating the sorting process. Methodology: The proposed sorting system integrates PLC technology and load cells to accurately measure and classify fruits based on their weight. By eliminating the need for manual intervention, the system enhances sorting efficiency and accuracy. The system is designed to be cost-effective and suitable for implementation by small traders and farmers. Results: The developed sorting system demonstrates precise, reliable, and consistent sorting outcomes based on fruit weight. By automating the sorting process, the system achieves improved efficiency and productivity compared to traditional manual sorting methods. Moreover, the cost-effectiveness of the system makes it accessible to small traders and farmers seeking to enhance their sorting practices. Conclusion: The proposed sorting system offers an economical and efficient solution for automated fruit sorting, particularly for small traders and farmers. By leveraging PLC technology and load cells, the system provides reliable alternative to costly image processing-based methods. The automation of the sorting process eliminates the risk of errors and inconsistencies associated with manual sorting, while ensuring accurate and quantitative classification based on fruit weight. This sorting system presents a practical approach to improving fruit trading operations, enhancing efficiency, and productivity.

On Verified Automated Reasoning in Propositional Logic: Teaching Sequent Calculus to Computer Science Students

As the complexity of software systems is ever increasing, so is the need for practical tools for formal verification. Among these are automatic theorem provers, capable of solving various reasoning problems automatically, and proof assistants, capable of deriving more complex results when guided by a mathematician/programmer. In this paper we consider using the latter to build the former. In the proof assistant Isabelle/HOL we combine functional programming and logical program verification to build a theorem prover for propositional logic. We also consider how such a prover can be used to solve a reasoning task without much mental labor. The development is extended with a formalized proof system for writing machine-checked sequent calculus proofs. We consider how this can be used to teach computer science students about logic, automated reasoning and proof assistants.

Integrated Simulation Research of Multi-natural Energy-driven Unmanned Surface Vehicle

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV’s equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations.

Tuning fuzzy SPARQL queries

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called FASILL that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the FASILL tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to FASILL transformation, data fuzzification and query customization in FSA-SPARQL become FASILL tuning problems. We have validated the approach with queries against datasets from online communities.

Hopping Proofs of Expectation-Based Properties: Applications to Skiplists and Security Proofs

We propose, implement, and evaluate a hopping proof approach for proving expectation-based properties of probabilistic programs. Our approach combines EHL, a syntax-directed proof system for reducing proof goals of a program to proof goals of simpler programs, with a "hopping" proof rule for reducing proof goals of an original program to proof goal of a different program which is suitably related (by means of pRHL, a relational program logic for probabilistic program) to the original program. We prove that EHL is sound for a core language with procedure calls and adversarial computations, and complete for the adversary-free fragment of the language. We also provide an implementation of EHL into EasyCrypt, a proof assistant tailored for reasoning about relational properties of probabilistic programs. We provide a tight integration of EHL with other program logics supported by EasyCrypt, and in particular probabilistic Relational Hoare Logic (pRHL). Using this tight integration, we give mechanized proofs of expected complexity of in-place implementations of randomized quickselect and skip lists. We also sketch applications of our approach to cryptographic proofs and discuss the broader impact of EHL in the EasyCrypt proof assistant.

Outcome Separation Logic: Local Reasoning for Correctness and Incorrectness with Computational Effects

Separation logic’s compositionality and local reasoning properties have led to significant advances in scalable static analysis. But program analysis has new challenges—many programs display computational effects and, orthogonally, static analyzers must handle incorrectness too. We present Outcome Separation Logic (OSL), a program logic that is sound for both correctness and incorrectness reasoning in programs with varying effects. OSL has a frame rule—just like separation logic—but uses different underlying assumptions that open up local reasoning to a larger class of properties than can be handled by any single existing logic. Building on this foundational theory, we also define symbolic execution algorithms that use bi-abduction to derive specifications for programs with effects. This involves a new tri-abduction procedure to analyze programs whose execution branches due to effects such as nondeterministic or probabilistic choice. This work furthers the compositionality promised by separation logic by opening up the possibility for greater reuse of analysis tools across two dimensions: bug-finding vs verification in programs with varying effects.

Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation

Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Prior policy-agnostic MPC languages relied on dynamic enforcement to decouple privacy requirements from program logic. Unfortunately, the resulting overhead makes it difficult to scale MPC applications that manipulate structured data. This work proposes to eliminate this overhead by instead transforming programs into semantically equivalent versions that statically enforce user-provided privacy policies. We have implemented this approach in a new MPC language, called Taypsi; our experimental evaluation demonstrates that the resulting system features considerable performance improvements on a variety of MPC applications involving structured data and complex privacy policies.

Falsity and untruth

Abstract Jc Beall's Divine Contradiction is a fascinating defence of the idea that contradictions are true of the tri-personal God. This project requires a logic that avoids the consequence that every proposition follows from a contradiction. Beall presents such a logic. This ‘gap/glut’ logic is the topic of this article. A gap/glut logic presupposes that falsity is not simply the absence of truth – for a proposition that is true may also be false. This article is essentially an examination of the idea that falsity is not simply untruth. The author rejects this position but does not claim to have an argument against it. In lieu of an argument, he presents three ‘considerations’. First, it is possible to give an intuitive semantics for the language of sentential logic that yields ‘classical’ sentential logic (including ‘p, ¬ p ⊢ q’) and which makes no mention of truth-values. Second, it is possible to imagine a race who manage their affairs very well without having the concept ‘falsity’. Third, it is possible to construct a semantics that yields a logic identical with the dialetheist logic and which makes no mention of truth-values – and which, far from being plausible, seems pointless.

Finding Cross-Rule Optimization Bugs in Datalog Engines

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations; bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation problem. The core idea behind the test oracle is to compare the results of an optimized program and a program without cross-rule optimization; any difference indicates a bug in the Datalog engine. Our core insight is that, for an optimized, incrementally-generated Datalog program, we can evaluate all rules individually by constructing a reference program to disable the optimizations that are performed among multiple rules. Incrementally generating test cases not only allows us to apply the test oracle for every new rule generated—we also can ensure that every newly added rule generates a non-empty result with a given probability and eschew recomputing already-known facts. We implemented IRE as a tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely Soufflé, CozoDB, μZ, and DDlog, and discovered a total of 30 bugs. Of these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the bugs identified by Deopt, queryFuzz might be unable to detect 5. Our incremental test case generation approach is efficient; for example, for test cases containing 60 rules, our incremental approach can produce 1.17× (for DDlog) to 31.02× (for Soufflé) as many valid test cases with non-empty results as the naive random method. We believe that the simplicity and the generality of the approach will lead to its wide adoption in practice.

Thermo-mechanical behaviours investigation of Nano-Sized Al2O3, TiO2, and Graphene Nanoplatelet Reinforced Epoxy Composites

Composite materials find extensive applications in various industries, thanks to their remarkable properties. These sectors include energy, maritime, motor sports, aviation, space and defense. The materials commonly used in these sectors are fiber reinforced plastic (FRP) composite materials. Epoxy materials are commonly used as matrix in the production of FRP materials. This study delves into the enhancement of epoxy-based nanocomposites by using graphene nanoplatelets (GNP-5nm), TiO2 (13nm), and Al2O3 (8nm) nanoparticles. These nanoparticles were added at varying mass ratios into a commercial epoxy to investigate their effects on some chemical, thermal and mechanical properties. Meticulous mixing methodologies were used to reduce clumping effects and ensure even distribution during the process. The curing process was carried out in a PLC (Programmable Logic Controller) controlled hot air oven under isothermal conditions under the influence of 100 °C for 30 minutes. Tensile strength, elongation at break, toughness, resilience modulus, elasticity modulus, hardness, FTIR analysis and thermal conductivity properties were characterized to assess the nanoparticle influence on the epoxy matrix. The results showed that there were remarkable improvements in mechanical properties with nanoparticle reinforcement. Especially, 1.25% Al2O3 inclusion exhibited a substantial increase of 140.32% in tensile strength and a 7% rise in shore D hardness compared to pure epoxy. This enhancement was attributed to enhanced O-H bonding between 'O' atoms in Al2O3 nanoparticles and epoxy polymer chains, enhancing matrix-filler interactions. Additionally, the effect of 1.0% TiO2 led to plasticity, displaying a 32% rise in elongation at break, signifying improved deformation energy absorption compared to neat epoxy. In thermal conductivity measurements, the highest thermal conductivity was observed in the sample with 1.25% GNP added and it increased by 123.5% compared to neat epoxy. In TiO2 and Al2O3 added samples, an increase of 69% and 47%, respectively, was observed at 1.25% additive rates compared to neat epoxy. According to the results, thanks to the nanoparticle reinforcement added into the epoxy matrix, composite structures can be given new and superior properties specific to the applications.