377 research outputs found

    Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

    Get PDF
    Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect

    Artificial Neural Network Logic-Based Reverse Analysis with Application to COVID-19 Surveillance Dataset

    Get PDF
    The Boolean Satisfiability Problem (BSAT) is one of the crucial decision problems in the fields of computing science, operation research, and mathematical logic that is resolved by deciding whether or not a solution to a Boolean formula exists. When there is a Boolean variable allocation that induces the Boolean formula to yield TRUE, then the SAT instance is satisfiable. The main purpose of this chapter is to utilize the optimization capacity of the Lyapunov energy function of Hopfield neural network (HNN) for optimal representation of the Random Satistibaility for COVID-19 Surveillance Data Set (CSDS) classification with the aim of extracting the relationship of dominant attributes that contribute to COVID-19 detections based on the COVID-19 Surveillance Data Set (CSDS). The logical mining task was carried based on the data mining technique of the energy minimization technique of HNN. The computational simulations have been carried using the different number of clauses in validating the efficiency of the proposed model in the training of COVID-19 Surveillance Data Set (CSDS) for classification. The findings reveals the effectiveness and robustness of k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward COVID-19 Surveillance Data Set (CSDS) logic

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks

    Full text link
    Minimal trap spaces (MTSs) capture subspaces in which the Boolean dynamics is trapped, whatever the update mode. They correspond to the attractors of the most permissive mode. Due to their versatility, the computation of MTSs has recently gained traction, essentially by focusing on their enumeration. In this paper, we address the logical reasoning on universal properties of MTSs in the scope of two problems: the reprogramming of Boolean networks for identifying the permanent freeze of Boolean variables that enforce a given property on all the MTSs, and the synthesis of Boolean networks from universal properties on their MTSs. Both problems reduce to solving the satisfiability of quantified propositional logic formula with 3 levels of quantifiers (∃∀∃\exists\forall\exists). In this paper, we introduce a Counter-Example Guided Refinement Abstraction (CEGAR) to efficiently solve these problems by coupling the resolution of two simpler formulas. We provide a prototype relying on Answer-Set Programming for each formula and show its tractability on a wide range of Boolean models of biological networks.Comment: Accepted at 21st International Conference on Computational Methods in Systems Biology (CMSB 2023

    Implicit Loss of Surjectivity and Facial Reduction: Theory and Applications

    Get PDF
    Facial reduction, pioneered by Borwein and Wolkowicz, is a preprocessing method that is commonly used to obtain strict feasibility in the reformulated, reduced constraint system. The importance of strict feasibility is often addressed in the context of the convergence results for interior point methods. Beyond the theoretical properties that the facial reduction conveys, we show that facial reduction, not only limited to interior point methods, leads to strong numerical performances in different classes of algorithms. In this thesis we study various consequences and the broad applicability of facial reduction. The thesis is organized in two parts. In the first part, we show the instabilities accompanied by the absence of strict feasibility through the lens of facially reduced systems. In particular, we exploit the implicit redundancies, revealed by each nontrivial facial reduction step, resulting in the implicit loss of surjectivity. This leads to the two-step facial reduction and two novel related notions of singularity. For the area of semidefinite programming, we use these singularities to strengthen a known bound on the solution rank, the Barvinok-Pataki bound. For the area of linear programming, we reveal degeneracies caused by the implicit redundancies. Furthermore, we propose a preprocessing tool that uses the simplex method. In the second part of this thesis, we continue with the semidefinite programs that do not have strictly feasible points. We focus on the doubly-nonnegative relaxation of the binary quadratic program and a semidefinite program with a nonlinear objective function. We closely work with two classes of algorithms, the splitting method and the Gauss-Newton interior point method. We elaborate on the advantages in building models from facial reduction. Moreover, we develop algorithms for real-world problems including the quadratic assignment problem, the protein side-chain positioning problem, and the key rate computation for quantum key distribution. Facial reduction continues to play an important role for providing robust reformulated models in both the theoretical and the practical aspects, resulting in successful numerical performances

    Towards trustworthy computing on untrustworthy hardware

    Get PDF
    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

    2010 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Fourth Annual GREAT Day. This file has a supplement of three additional pages, linked in this record.https://knightscholar.geneseo.edu/program-2007/1004/thumbnail.jp

    Low Power Memory/Memristor Devices and Systems

    Get PDF
    This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

    Evolutionary Algorithms for Resource Constrained Project Scheduling Problems

    Full text link
    The resource constrained project scheduling problems (RCPSPs) are well-known challenging research problems that require efficient solutions to meet the planning need of many practical high-value projects. RCPSPs are usually solved using optimization problem-solving approaches. In recent years, evolutionary algorithms (EAs) have been extensively employed to solve optimization problems, including RCPSPs. Despite that numerous EAs have been developed for solving various RCPSPs, there is no single algorithm that is consistently effective across a wide range of problems. In this context, this thesis aims to propose a few new algorithms for solving different RCPSPs that include singular-resource and multiple-resource problems with single and multiple objectives. In general, RCPSPs are solved with an assumption that its activities are homogeneous, where all activities require all resource types. However, many activities are often singular, requiring only a single resource to complete an activity. Even though the existing algorithms that were developed for multi-resource problems, can solve this RCPSP variant with minor modifications, they are computationally expensive because they include some unnecessary resource constraints in the optimization process. In this thesis, at first, a problem with singular resource and single objective is considered. A heuristic-embedded genetic algorithm (GA) has been proposed for solving this problem, and it's effectiveness has been investigated. To enhance the performance of this algorithm, three heuristics are proposed and integrated with it. As there are no test problems available for singular resource problems, new benchmark problems are generated by modifying the existing multi-resource RCPSPs test set. As compared with experimental results of one of the modified algorithms and an exact solver, it was shown that the proposed algorithm achieved a better quality of solution while requiring a significantly smaller computational budget. The proposed algorithm is then extended to make it suitable for solving multi-resource cases with a single objective, which are known as traditional RCPSPs. A self-adaptive GA is developed for this problem. The proposed self-adaptive component of the algorithm selects an appropriate genetic operator based on their performance as the evolution progresses and increases. To judge the performance of this algorithm, small to large-scale problem instances have been solved from the PSP Library and the results are compared with state-of-the-art algorithms. Based on the experimental results, it was found that the proposed algorithm was able to obtain much better solutions than the non-self-adaptive GA. Furthermore, the proposed approach outperformed the state-of-the-art algorithms. In practice, cost of some resources varies with the day of the week or specific days in the month or year. To consider these day dependent costs, a new cost function is developed that is integrated with the usual cost fitness function in a multi-objective version of RCPSPs. Completion time is considered as the second objective. A heuristic-embedded self-adaptive multi-objective GA is proposed for both singular and multi-resource problems. In this algorithm, the selection mechanism is based on crowding distance and a reference point. A customized mutation operator is also introduced. The experimental results show that the proposed variant, with reference points-based selection, outperformed the variant, with crowding distance-based selection. In many situations, resource availability varies with time, such as time of the day and in some particular days. A dynamic multi-operators-based GA is proposed to deal with this variant. Along with the genetic operators, two local search methods are also included in the self-adaptive mechanism. The proposed approach has been validated using both large-scale singular and multi-resource problem instances with a single objective. Its experimental results demonstrate the efficiency of the proposed dynamic multi-operator-based approach. In summary, the proposed algorithms can solve different variants of RCPSPs that cover a broad spectrum of project scheduling problems, with significantly less computational tim
    • …
    corecore