Rule-based Research Articles

A Student Performance Prediction Model Based on Hierarchical Belief Rule Base with Interpretability

Predicting student performance in the future is a crucial behavior prediction problem in education. By predicting student performance, educational experts can provide individualized instruction, optimize the allocation of resources, and develop educational strategies. If the prediction results are unreliable, it is difficult to earn the trust of educational experts. Therefore, prediction methods need to satisfy the requirement of interpretability. For this reason, the prediction model is constructed in this paper using belief rule base (BRB). BRB not only combines expert knowledge, but also has good interpretability. There are two problems in applying BRB to student performance prediction: first, in the modeling process, the system is too complex due to the large number of indicators involved. Secondly, the interpretability of the model can be compromised during the optimization process. To overcome these challenges, this paper introduces a hierarchical belief rule base with interpretability (HBRB-I) for student performance prediction. First, it analyzes how the HBRB-I model achieves interpretability. Then, an attribute grouping method is proposed to construct a hierarchical structure by reasonably organizing the indicators, so as to effectively reduce the complexity of the model. Finally, an objective function considering interpretability is designed and the projected covariance matrix adaptive evolution strategy (P-CMA-ES) optimization algorithm is improved. The aim is to ensure that the model remains interpretable after optimization. By conducting experiments on the student performance dataset, it is demonstrated that the proposed model performs well in terms of both accuracy and interpretability.

Industrial Sustainable Development: The Development Trend of Programmable Logic Controller Technology

Programmable Logic Controllers (PLCs) are indispensable for current and future industrial development, especially in smart factories, smart home technology, automated production lines, and machinery manufacturing. This study presents the trends in PLC software and hardware development through a technology roadmap and offers relevant suggestions to help industries achieve sustainable development, enhance market competitiveness, and provide references for research. Through expert interviews and fuzzy Delphi analysis, this study points out that future PLC development needs to focus on editing interfaces, syntax, Central Processing Units, Memory Units, and Communication Modules. Specific recommendations include visualizing regional/global label settings and connection settings, adding Python, JAVA, LabVIEW, and Scratch syntax, improving instruction execution speed, expanding program and expansion capacities, and adopting dual-channel Ethernet and connections to external networks and wireless networks. Fuzzy hierarchical analysis shows that Communication Modules are the most important component, followed by Central Processing Units and syntax expansion, and, finally, program and expansion capacity enhancements. These suggestions aim to promote product innovation and social environment demand evaluation, enhance product competitiveness, and achieve sustainable development goals.

Network Security Prediction of Industrial Control Based on Projection Equalization Optimization Algorithm

This paper predicts the network security posture of an ICS, focusing on the reliability of Industrial Control Systems (ICSs). Evidence reasoning (ER) and belief rule base (BRB) techniques are employed to establish an ICS network security posture prediction model, ensuring the secure operation and prediction of the ICS. This model first integrates various information from the ICS to determine its network security posture value. Subsequently, through ER iteration, information fusion occurs and serves as an input for the BRB prediction model, which necessitates initial parameter setting by relevant experts. External factors may influence the experts’ predictions; therefore, this paper proposes the Projection Equalization Optimization (P-EO) algorithm. This optimization algorithm updates the initial parameters to enhance the prediction of the ICS network security posture through the model. Finally, industrial datasets are used as experimental data to improve the credibility of the prediction experiments and validate the model’s predictive performance in the ICS. Compared with other methods, this paper’s prediction model demonstrates a superior prediction accuracy. By further comparing with other algorithms, this paper has a certain advantage when using less historical data to make predictions.

Development of a new social prescribing intervention for families of children with cerebral palsy.

To co-design a social prescribing intervention (the EPIC-CP programme: Equitable Pathways and Integrated Care in Cerebral Palsy) with children with cerebral palsy (CP), their families, and clinicians to address unmet social needs. The study was conducted (August 2021 to March 2023) at the paediatric rehabilitation departments of the three tertiary paediatric hospitals in New South Wales, Australia. Eligible participants attended or worked at one of the departments, including children with CP, parents/caregivers, and clinicians. Mixed-methods co-design was used in intervention co-production and prototyping. The project was overseen by research advisors with lived experience of CP. More than 200 participants contributed to the co-design research. Families experienced a substantial burden of unmet social needs. Co-designed interventions involved systematic identification of unmet social needs with (1) targeted community resources and (2) engagement with a 'community linker' who supported children/young people and their families to access health, education, and social services that matched their identified needs and preferences. Research participants co-developed the programme logic model and prototype. This was piloted in research action cycles and iteratively refined until consensus was achieved. We co-designed a social prescribing programme responsive to the needs of its end-users and purposefully developed to be embedded in the Australian health setting. A pilot randomized controlled trial will further evaluate this intervention.

A high accuracy Stewart–lift platform based on a programmable logic controller – comparative case studies

The study reports the results of a comparative analysis of advanced high-accuracy Stewart-Lift platform along with a comparative study of dynamic control. A control system powered by a programmable logic controller (PLC) was used. The properties of the system were described using a dynamic model using the Lagrange method. The real object was verified by performing several tests and comparing them using quality indicators. The results of verification tests conclusively demonstrate the system’s suitability for applications within industrial automation and robotics systems.

Embedded System Based on Task Interval Data Interaction Model

In the Internet of Things era, more intelligent systems can communicate with each other. Embedded system combined with network communication applications has become the basis for Internet of Things research. The programmable logic unit designed by ARM architecture has great advantages in running speed, power control and so on. In the paper, to solve the problems of common embedded MCU control resources occupying a large amount of memory and the slow speed of building engineering simulation model, it is necessary to use the queuing connection algorithm model to directly input the timing physical characteristics of the code stream in the embedded system to calculate and match the timing physical characteristics of the output code stream. The optimization algorithm of DTF-MARTE to detect the probability of timing deviation is used in the paper. It is to detect the problem of inaccurate timing information in the demand. We compare the expected physical characteristics of the timing sequence and obtain the timing deviation probability of the output data stream. The model developed in this paper has the characteristic of dynamic reconfiguration of the task interval. The design of monotonically decreasing data tasks can be realized, and the reconfigured task modules are used for interacting the data buffer area and dynamically reconstructing the instruction overhead and transmission. We analyze the performance comparison between the proposed model and the traditional communication connection model. It proves that the proposed model can further improve the priority queue and guide the data flow. According to that method, the problem of asynchronous spatial data interaction by controlling and combining different communication modes in a large scene can be solved. Data interaction can be triggered at a fixed time, and mutual interference of randomly triggered wireless communication and data acquisition modules can be avoided. It can solve the problem of insufficient computing power when future embedded devices need massive data encryption in the Internet of Things era, and provide a new way of thinking for fast, safe and efficient implementation.

NXRouting: A GPU-Enhanced CAD Tool for European Radiation-Hardened FPGAs

Field Programmable Gate Arrays (FPGAs) have witnessed an increase in space applications in the last years, mainly due to their cost-effective high-performances and flexibility. However, the susceptibility of these devices to radiation-induced effects when working in such an environment is well known. When common mitigation techniques are not sufficient to ensure the correct completion of a task, radiation-hardened FPGAs represent one of the most effective solutions. NanoXplore, in this context, is the first European developer of rad-hard FPGAs, which embed intrinsic high complexity in their architectures preventing the user from using or developing custom placement and routing algorithms. In this paper, we overcame these issues by proposing the first tool tailored to NanoXplore devices which allows the exploration of NanoXplore device architectures and routing of points through a Python interface. We developed a model that reflects the one used by the vendor, allowing the user to extract info about routes, nets and additional logic, otherwise unavailable. The tool also performs routing of points in the programmable logic, computing the optimal path. An implementation of the router on Graphic Processing Unit (GPU) is proposed to exploit the highly parallelizable nature of the problem. Finally, routing timing analyses on different benchmarks have been performed, improving the routing routine time.

Semantic-based neural network repair for AI models in dynamic environments

This paper addresses a critical challenge in the field of artificial intelligence (AI) and deep learning (DL) - the debugging and repair of AI models within dynamic environments. The rapid development of frameworks like TensorFlow or PyTorch can introduce version incompatibilities, potentially breaking existing code. To tackle this issue, it can be seen that a semantic-based neural network repair method that leverages an existing semantics called ExAIS is proposed, defined in the Prolog logic programming language. This method identifies architectural and parameter errors in AI models and generates debug messages to facilitate the repair process. Key contributions of this paper include introducing a novel semantic-based AI model repair method capable of suggesting multiple changes to rectify inefficiencies in AI models. We evaluate the effectiveness of our approach in a real-world context by collecting a set of real bugs from AI developers. The background section provides an overview of AI framework testing, machine learning, neural architecture search (NAS), and deep learning. The methodology section details the repair method based on ExAIS semantics, covering error identification and repair suggestions as the two main steps. The evaluation section showcases the repair effectiveness on automatically generated and manually written models, emphasizing the applicability and efficiency of the approach.

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

Digital support of spatial planning processes in the regions

Subject. This article deals with the design and modeling of a data-centered tool for supporting spatial planning processes. Objectives. The article aims to develop a tool designed to solve the problem of automated formation of volumetric surfaces, which is necessary for assessing the innovative development of the region. Methods. For the study, we used the methods and approaches of the fuzzy logic theory, and logical programming methods. Results. An integrated approach to solving regional problems, taking into account the empirical knowledge of experts, helped develop a tool for assessing the innovative potential of the region, its approbation through using a Russian region as a case study is illustrated with graphs. Relevance. The formed triad of sustainable development makes it possible to reflect the behavior of the regional innovative development index as a whole on the basis of social, economic and environmental indicators of sustainability.

Research on Personal Information Protection in the Context of Generative Artificial Intelligence

Personal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of purpose limitation and the principle of openness and transparency, increasing the risk of personal information leakage. At the information generation stage, generative AI can generate false and discriminatory information. Therefore, in the context of generative AI, personal information protection is faced with the problems of the notification and consent rules being hollowed out, the principle of minimum necessity being voided, and the frequent leakage of personal information. Based on this, it is necessary to promote the transformation of the "personal control center to the risk control center" of the notification and consent rules, promote the risk-based interpretation of the principle of least necessary, and improve the risk-based personal information protection compliance system to solve the problem of personal information protection in the context of generative AI.

Dissociable roles of the dorsolateral and ventromedial prefrontal cortex in human categorization.

Models of human categorization predict the prefrontal cortex (PFC) serves a central role in category learning. The dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) have been implicated in categorization; however, it is unclear whether both are critical for categorization and whether they support unique functions. We administered three categorization tasks to patients with PFC lesions (mean age = 69.6 years; 5 men, 5 women) to examine how prefrontal subregions contribute to categorization. These included a rule-based (RB) task that was solved via a unidimensional rule, an information integration (II) task that was solved by combining information from two stimulus dimensions, and a deterministic/probabilistic (DP) task with stimulus features that had varying amounts of category-predictive information. Compared to healthy comparison participants, both patient groups had impaired performance. Impairments in the dlPFC patients were largest during the RB task, whereas impairments in the vmPFC patients were largest during the DP task. A hierarchical model was fit to the participants' data to assess learning deficits in the patient groups. PFC damage was correlated with a regularization term that limited updates to attention after each trial. Our results suggest that the PFC, as a whole, is important for learning to orient attention to relevant stimulus information. The dlPFC may be especially important for rule-based learning, whereas the vmPFC may be important for focusing attention on deterministic (highly diagnostic) features and ignoring less predictive features. These results support overarching functions of the dlPFC in executive functioning and the vmPFC in value-based decision making.Significance Statement Category learning creates flexible memory representations that easily generalize to novel situations. Although it is generally established that the prefrontal cortex is central to categorization, it is unclear how different prefrontal subregions contribute to learning. Separate literatures have implicated both the dorsolateral prefrontal cortex (dlPFC) and the ventromedial prefrontal cortex (vmPFC) in categorization, but there has been little effort to bridge these literatures. The current study is the first to examine categorization in patients with lesions centered in the dlPFC and vmPFC. We found that, as a whole, the PFC orients attention to relevant stimulus information. The dlPFC is important for rule-based learning, whereas the vmPFC is important for focusing attention on highly diagnostic features and ignoring less predictive features.

Computationally Hard Problems for Logic Programs under Answer Set Semantics

Showing that a problem is hard for a model of computation is one of the most challenging tasks in theoretical computer science, logic and mathematics. For example, it remains beyond reach to find an explicit problem that cannot be computed by polynomial size propositional formulas (PF). As a model of computation, logic programs (LP) under answer set semantics are as expressive as PF, and also \(\mathtt{NP}\) -complete for satisfiability checking. In this paper, we show that the PAR problem is hard for LP, i.e., deciding whether a binary string contains an odd number of \(1\) ’s requires exponential size logic programs. The proof idea is first to transform logic programs into equivalent boolean circuits, and then apply a probabilistic method known as random restriction to obtain an exponential lower bound. Based on the main result, we generalize a sufficient condition for identifying hard problems for LP, and give a separation map for a logic program family from a computational point of view, whose members are all equally expressive and share the same reasoning complexity.

A temperature fluctuation suppression control method of fuel cell vehicles to reduce hydrogen consumption

During the driving of the fuel cell vehicles, temperature fluctuations occur in the Proton Exchange Membrane Fuel Cell (PEMFC), whose output efficiency decreases and hydrogen consumption increases. Rule-based (RB) and proportional-integral-derivative (PID) controllers can roughly suppress the temperature fluctuation, but they cannot perform multi-objective optimization, which cannot reduce hydrogen consumption at the same time. To solve this problem, in the first step of this paper, the hydrogen consumption factor and the temperature fluctuation factor in the operating temperature control are introduced to design the DDQN controller. In the second step of this paper, the DDQN controller is trained offline and verified under simulation conditions. The simulation results show that compared with the RB controller and PID controller, the average temperature fluctuation of the DDQN controller is reduced by 27.27 % and 28.50 %, and the hydrogen consumption is saved by 1.78 % and 2.58 %, respectively. The research in this paper is innovative in that it reduces the hydrogen consumption of PEMFC and improves the driving range of fuel cell vehicles from a specific point of view.

Integrating UAV Photogrammetry and Terrestrial Laser Scanning for the 3D surveying of the Fortress of Bashtova

Through the synergistic application of Aerial Photogrammetry using UAVs and Terrestrial Laser Scanning (TLS), this paper investigates how this combination can be used for conducting a 3D survey of the Fortress of Bashtova thereby demonstrating the effectiveness of such integrated methods in acquiring an all-encompassing image of this historical building. As the efforts towards preservation become intense, there arises the urgency of precise and detailed 3D documentation that will facilitate appropriate conservation processes and further studies. Therefore, combining TLS and UAV photogrammetry offers a powerful tool that can provide accurate architectural data for the documentation of heritage areas. Moreover, the TLS component acquires ground point-cloud data with laser scanners giving a complementary alternative for aerial perspective. The merging of these datasets ensures broad inclusion since it allows the production of accurate, detailed three-dimensional models of the Fortress of Bashtova. Thanks to the research on the case study of the Fortress of Bashtova in the article, it can be stated that the integration of data from aerial photogrammetry and TLS is seamless with the help of modern software while respecting the basic photogrammetric-geodetic rules and demonstrates the possibility of creating a complex 3D model, usable for further analyses for architects and conservation professionals, as well as for restorers and civil engineers. To estimate the accuracy of the point clouds derived from TLS and UAV, we compared the distances between the point clouds using CloudCompare software. We obtained a mean RMS of 2.199073 mm and std. dev was 7.356 mm. Research has shown that the difference between point clouds from TLS and UAV is within 1.7 centimeters.

What can we do in Philosophy using Frege’s and Kripke’s logics?

In this article I issue a challenge to philosophers engaged in constructing logical languages. Formal languages that have had a great influence on various areas of philosophy have ineffable statements that arise from metaphysical assumptions, thus limiting what we can do with them. I deal with two cases. The case of Frege known as “The paradox of the concept horse”, and that of Kripke that is not as famous as the Fregean problem, which I call “The necessary bearing of the name.” I briefly develop each case and show that together they give us sufficient evidence of the limits of what we can do with logic when the formal systems employ unexamined metaphysical assumptions.

From large language models to small logic programs: building global explanations from disagreeing local post-hoc explainers

The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.

From large language models to small logic programs: building global explanations from disagreeing local post-hoc explainers

The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.

Intelligent Traffic Signal Management with PLC

This study introduces a Smart Traffic Light Control System (STLCS) that uses advanced technologies to improve traffic regulation. The key components include a camera-based traffic density detection module and a specialized vehicle identification system using Radio Frequency (RF) technology. The system’s core is an Arduino Uno board along with several converters and a Programmable Logic Controller (PLC). Field tests show that STLCS effectively analyses traffic density and prioritizes emergency vehicles, proving its potential for global traffic management improvements

Development of a MIMO fuzzy inference system—PI controller for a closed‐circuit grinding ball mill circuit

AbstractThis article aims to study the implementation of classical proportional‐integrative (PI) controllers and their coupling with the fuzzy inference systems (FISs) in the act of closed‐circuit grinding (CCG) ball mill system. The system was formed for a multiple‐input multiple‐output (MIMO) system, with two inputs, the feed rate (WF) and speed classifier rotor (VR), and two outputs, a sieve fraction 45 μm (P45) and the amount of material by a weight inside the drum (hold up [HU]). The model was simulated based on experimental processes and control strategies. The fuzzy‐PI controllers were developed on the software, and the data from this process were used to build the database and the necessary knowledge to construct the FIS controllers (with fuzzy rules base 3 × 3 and 5 × 5). Their implementation decreases the error criteria integral of time multiplied by the absolute error (ITAE) and integral of the absolute magnitude of the error (IAE) by 35% and 65%, respectively. Although, applying fuzzy‐PI systems with a smaller rule‐based outcome gives the benefits of implementing the fuzzy logic (FL) but with a smaller oscillatory performance and a minor negative effect on HU control.