Deletion Algorithm Research Articles

Improved FPT Algorithms for Deletion to Forest-Like Structures

Improved FPT Algorithms for Deletion to Forest-Like Structures

Algorithms for 2-club cluster deletion problems using automated generation of branching rules

In the 2-Club Cluster Vertex Deletion (resp., 2-Club Cluster Edge Deletion) problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k vertices (resp., edges) whose removal from G results in a graph in which the diameter of every connected component is at most 2. In this paper we give algorithms for 2-Club Cluster Vertex Deletion and 2-Club Cluster Edge Deletion whose running times are O⁎(3.104k) and O⁎(2.562k), respectively. Our algorithms were obtained using automated generation of branching rules. Our results improve the previous O⁎(3.303k)-time algorithm for 2-Club Cluster Vertex Deletion [Liu et al., FAW-AAIM 2012] and the O⁎(2.695k)-time algorithm for 2-Club Cluster Edge Deletion [Abu-Khzam et al., TCS 2023].

A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network.

With the advancement of neural networks, more and more neural networks are being applied to structural health monitoring systems (SHMSs). When an SHMS requires the integration of numerous neural networks, high-performance and low-latency networks are favored. This paper focuses on damage detection based on vibration signals. In contrast to traditional neural network approaches, this study utilizes a stochastic configuration network (SCN). An SCN is an incrementally learning network that randomly configures appropriate neurons based on data and errors. It is an emerging neural network that does not require predefined network structures and is not based on gradient descent. While SCNs dynamically define the network structure, they essentially function as fully connected neural networks that fail to capture the temporal properties of monitoring data effectively. Moreover, they suffer from inference time and computational cost issues. To enable faster and more accurate operation within the monitoring system, this paper introduces a stochastic convolutional feature extraction approach that does not rely on backpropagation. Additionally, a random node deletion algorithm is proposed to automatically prune redundant neurons in SCNs, addressing the issue of network node redundancy. Experimental results demonstrate that the feature extraction method improves accuracy by 30% compared to the original SCN, and the random node deletion algorithm removes approximately 10% of neurons.

L-rCBF: Learning-Based Key–Value Data Structure for Dynamic Data Processing

Learning-based data structures, such as a learned Bloom filter and a learned functional Bloom filter (L-FBF), have recently been proposed to replace traditional structures. However, using these structures for dynamic data processing is difficult because a specific element cannot be deleted from a trained model. A counting Bloom filter with return values (rCBF) is a more efficient key–value structure than a functional Bloom filter (FBF) for repetitive insertions and deletions. In this study, we propose a learned rCBF (L-rCBF) comprising a model, a Bloom filter, and an rCBF and the deletion algorithm for the L-rCBF. To delete a specific element from the L-rCBF, two different operations are performed according to four different cases. In the experiments, the proposed L-rCBF is compared with a single rCBF and an L-FBF in terms of undeletables and search failures, and this comparison is conducted using two different models. In addition, we present a theoretical analysis of the rCBF with experimental results to demonstrate that a structure with an rCBF is more suitable for dynamic data than a structure with an FBF.

Triclustering Implementation Using Hybrid δ-Trimax Particle Swarm Optimization and Gene Ontology Analysis on Three-Dimensional Gene Expression Data

Triclustering is a data mining method for grouping data based on similar characteristics. The main purpose of a triclustering analysis is to obtain an optimal tricluster, which has a minimum mean square residue (MSR) and a maximum tricluster volume. The triclustering method has been developed using many approaches, such as an optimization method. In this study, hybrid δ-Trimax particle swarm optimization was proposed for use in a triclustering analysis. In general, hybrid δ-Trimax PSO consist of two phases: initialization of the population using a node deletion algorithm in the δ-Trimax method and optimization of the tricluster using the binary PSO method. This method, when implemented on three-dimensional gene expression data, proved useful as a Motexafin gadolinium (MGd) treatment for plateau phase lung cancer cells. For its implementation, a tricluster that potentially consisted of a group of genes with high specific response to MGd was obtained. This type of tricluster can then serve as a guideline for further research related to the development of MGd drugs as anti-cancer therapy.

Edge-Based Minimal k-Core Subgraph Search

In social networks, k-core is commonly used to measure the stability of a network. When a user in a k-core leaves the network, other users may follow the user to leave. Hence, maintaining a key user is important to keep the stability of a network. It is known that an edge between two users models the relationship between the two users. In some scenarios, maintaining a relationship comes at a cost. Therefore, selectively in maintaining the relationships between users is crucial. In this paper, we for the first time conceive the concept of an edge-based minimal k-core model. An edge-based minimal k-core is a k-core with a minimal number of edges. In other words, removing any edge in an edge-based minimal k-core would make it not be a k-core any more. Based on this model, we proposed two problems, namely, an edge-based minimal k-core subgraph search (EMK-SS) and an edge-based minimal k-core subgraph search with a query node q (EMK-q-SS). Given a graph G, an integer k, and a query node (a key user) q, the EMK-q-SS problem is to find all the edge-based minimal k-cores containing the query node q, and the EMK-SS problem is to find all the edge-based minimal k-cores. We also theoretically prove that the two problems are both NP-complete. To deal with the proposed problems, we design two novel algorithms, namely the edge deletion algorithm and edge extension algorithm. Further, a graph partitioning technique is employed to speed up the computation. Comprehensive experiments on synthetic and real networks are conducted to demonstrate the effect and efficiency of our proposed methods.

A Deletion Algorithm for the Marginal Problem in Propositional Logic Based on Boolean Arrays

This paper proposes a deletion algorithm for the marginal problem in propositional logic. The algorithm is based on the general Davis and Putnam deletion algorithm DP, expressed as a bucket elimination algorithm, representing sets of clauses with the same set of variables employing a Boolean array. The main contribution is the development of alternative procedures when deleting a variable which allow more efficient computations. In particular, it takes advantage of the case in which the variable to delete is determined by a subset of the rest of the variables. It also provides a set of useful results and tools for reasoning with Boolean tables. The algorithms are implemented using Python and the NumPy library. Experiments show that this procedure is feasible for intermediate problems and for difficult problems from hard Bayesian networks cases

Deletion to scattered graph classes II - improved FPT algorithms for deletion to pairs of graph classes

The problem of deletion of vertices to a hereditary graph class is a well-studied problem in parameterized complexity. Recently, a natural extension of the problem was initiated where we are given a finite set of hereditary graph classes and we determine whether k vertices can be deleted from a given graph so that the connected components of the resulting graph belong to one of the given hereditary graph classes. The problem is shown to be fixed parameter tractable (FPT) when the deletion problem to each of the given hereditary graph classes is fixed-parameter tractable, and the property of being in any of the graph classes is expressible in the counting monodic second order (CMSO) logic. This paper focuses on pairs of specific graph classes (Π1,Π2) in which we would like the connected components of the resulting graph to belong to, and design simpler and more efficient FPT algorithms.

Efficient Underground Target Detection of Urban Roads in Ground-Penetrating Radar Images Based on Neural Networks

Ground-penetrating radar (GPR) is an important nondestructive testing (NDT) tool for the underground exploration of urban roads. However, due to the large amount of GPR data, traditional manual interpretation is time-consuming and laborious. To address this problem, an efficient underground target detection method for urban roads based on neural networks is proposed in this paper. First, robust principal component analysis (RPCA) is used to suppress the clutter in the B-scan image. Then, three time-domain statistics of each A-scan signal are calculated as its features, and one backpropagation (BP) neural network is adopted to recognize A-scan signals to obtain the horizontal regions of targets. Next, the fusion and deletion (FAD) algorithm is used to further optimize the horizontal regions of targets. Finally, three time-domain statistics of each segmented A-scan signal in the horizontal regions of targets are extracted as the features, and another BP neural network is employed to recognize the segmented A-scan signals to obtain the vertical regions of targets. The proposed method is verified with both simulation and real GPR data. The experimental results show that the proposed method can effectively locate the horizontal ranges and vertical depths of underground targets for urban roads and has higher recognition accuracy and less processing time than the traditional segmentation recognition methods.

Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN.

Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN.

Algorithm for emergency deletion of files by voice command

The Currently, there is a growing interest in various methods and means of protecting information, which is due to the growing number of crimes related to the violation of confidentiality of information, including through physical access to a computer. There are many ways to protect information from unauthorized access, such as physical security measures, password protection, and so on. However, a situation may arise when all defense lines have already been overcome and there is no other way to quickly block or destroy confidential information in any other way than with the help of a voice command. Thus, the topic of the article is relevant. The object of research is an information system containing confidential information. The subject of the research is the methods of emergency deletion of confidential information. The purpose of this article is to develop an algorithm for urgent deletion of files by voice command. In the course of the work, the algorithm for the emergency deletion of files by voice command was developed, which allows blocking the means of inputting information (keyboard and mouse) and deleting files located in a predetermined folder. The algorithm provides protection against accidental pronunciation of a voice command: before deleting files, a timer is first started, the time of which is pre-set by the user. During the duration of the timer, it is possible to cancel the deletion of files by entering a password, however, there is no way to access the information until the appropriate password is entered.

Low-Complexity and High-Coding-Efficiency Image Deletion for Compressed Image Sets in Cloud Servers

Image deletion refers to removing images from a compressed image set in cloud servers, which has always received much attention. However, in some cases images are not successfully deleted, and coding performance still remains to rise. In this paper, we propose a low-complexity and high-coding-efficiency image deletion algorithm. First, all the images are classified into to-be-deleted images, images unneeded to be processed, and images needed to be processed further divided into images needed to be only decoded and images needed to be re-encoded. Then, we also propose a depth- and subtree-constrained minimum spanning tree (DSCMST) heuristics to produce the DSCMST of images needed to be processed. Third, every image unneeded to be processed is added to the just obtained DSCMST as the child of the vertex that is still its parent in the compressed image set. Finally, after the encoding of images needed to be re-encoded, a new compressed image set is constructed, implying the completion of image deletion. Experimental results show that under various circumstances our proposed algorithm can effectively remove any images, including root vertex, internal vertices, and leaf vertices. Moreover, compared with state-of-the-art methods, the proposed algorithm achieves higher coding efficiency while having the minimum complexity.

Development and Implementation of Element Deletion Algorithm into an Open-Source Software Based on the Fracture Locus of Materials

An accurate fracture simulation is often associated with how reliably the material model is represented. Hence, many models dealing with the calibration of ductile damage of materials have already been developed to predict failure initiation. Nevertheless, the challenge remains in obtaining an accurate representation of the fracture growth. Herein, an element deletion algorithm is developed and implemented into finite element open-source software. The deleted elements are replaced by new cells made of a virtual low-stiffness material. To better visualize the failure progression, the final model excludes these virtual cells from the representation. The functionality of the algorithm is tested through a series of two-dimensional simulations on three different geometries with a well-known behavior under uniaxial tension. Moreover, the failure response of a three-dimensional lattice structure is numerically investigated and compared against experimental data. The results of the two-dimensional simulations showed the capability of the algorithm to predict the onset of failure, crack nucleation, and fracture growth. Similarly, the onset and the initial fracture region were accurately captured in the three-dimensional case, with some convergence issues that prevent the visualization of the fracture growth. Overall, the results are encouraging, and the algorithm can be improved to introduce other computational functionalities.

A Blockchain Storage Architecture Based on Information-Centric Networking

Blockchain technology is a unique distributed ledger technology that has been widely used in various areas. With the increase in data on the blockchain and the append-only nature of the blockchain, the traditional blockchain’s full replica storage technique leads to blockchain storage scalability problem. Existing methods prioritize minimizing the storage strain on blockchain nodes while ignoring the availability of data, resulting in a lengthy average response time for users to access the blockchain. To address the shortcomings, this paper proposes an Information-Centric Networking-based blockchain storage architecture. The architecture uses the enhanced resolution system for community division to build blockchain node partitions and store blockchain ledgers in the underlying network. It introduces virtual chain for rapid blockchain indexing and adopts a collaborative block replica deletion algorithm across neighboring partitions, including replica number decision based on blockchain access decay characteristics and replica deletion based on resource relationship. Finally, we compare and analyze the proposed blockchain storage architecture with BC-store and KASARASA, and the results demonstrate that this architecture has significantly lower average access time than others. The replica data volume of this method is reduced by 57.2% compared to the full replica policy, but the access time is only 5.2% slower when compared to the full replica policy, which substantially increases the replica storage utilization.

Edge Deletion Algorithms for Minimizing Spread in SIR Epidemic Models

This paper studies algorithmic strategies to effectively reduce the number of infections in susceptible-infected-recovered (SIR) epidemic models. We consider a Markov chain SIR model and its two instantiations in the deterministic SIR (D-SIR) model and the independent cascade SIR (IC-SIR) model. We investigate the problem of minimizing the number of infections by restricting contacts under realistic constraints. Under moderate assumptions on the reproduction number, we prove that the infection numbers are bounded by supermodular functions in the D-SIR model and the IC-SIR model for large classes of random networks. We propose efficient algorithms with approximation guarantees to minimize infections. The theoretical results are illustrated by numerical simulations.

TIIA: A blockchain-enabled Threat Intelligence Integrity Audit scheme for IIoT

With the widespread deployment of the Industrial Internet of Things technology (IIoT), the cyber security threats of IIoT systems are increasing. Threat intelligence sharing is an effective way to resist cyber security threats. However, there is a contradiction between information integrity and the requirement of building a complete attack chain in the threat intelligence sharing and utilization. Due to the limited capacity of block storage pattern, it can be found that threat intelligence is prone to fragmentation on blockchain. In this paper, a blockchain-enabled Threat Intelligence Integrity Audit (TIIA) scheme for IIoT is proposed. This scheme can audit the integrity of threat intelligence in ciphertext state to ensure the confidentiality protection requirements of threat intelligence on blockchain. A double chain structure is presented in the TIIA scheme. The storage-chain is used to store threat intelligence ciphertext, while the audit-chain is designed to achieve the integrity audit of the corresponding fragment on the storage-chain. In addition, Paillier homomorphic encryption and searchable encryption are used to realize the confidentiality and ciphertext retrieval of threat intelligence on the storage-chain. A redundant block deletion algorithm is designed to reduce the audit-chain load and improve audit efficiency. The performance analysis shows that the proposed scheme can effectively reduce the computational and communication costs, and has a high audit efficiency.

Improved kernel and algorithm for claw and diamond free edge deletion based on refined observations

In the ▪-Free Edge Deletion problem (CDFED), we are given a graph G and an integer k>0, and the question is whether there are at most k edges whose deletion results in a graph without claws and diamonds as induced subgraphs. Based on some refined observations, we propose a kernel of O(k3) vertices and O(k4) edges, significantly improving the previous kernel of O(k12) vertices and O(k24) edges. In addition, we derive an O⁎(3.792k)-time algorithm for CDFED.

Resolving challenges of monitoring PV systems: a case study for three 1.5 kW generators in Lima, Peru

This paper addresses challenges from monitoring three grid-connected PV systems with different module technologies in Lima, Peru, following the International Electrotechnical Commission (IEC) standards. Missing and erroneous data due to sensor malfunction or partial shadowing can negatively impact the measured data quality. We developed a program to automatically detect erroneous measurements, enhance the data quality, and calculate PV performance parameters following the IEC-61724 standard to resolve these challenges. The program includes mathematical methods to replace faulty or missing measurements. Finally, we present the impact of the algorithms for erroneous data detection, deletion, and correction on performance calculations.

A fusion data security protection scheme for sensitive E-documents in the open network environment.

E-documents are carriers of sensitive data, and their security in the open network environment has always been a common problem with the field of data security. Based on the use of encryption schemes to construct secure access control, this paper proposes a fusion data security protection scheme. This scheme realizes the safe storage of data and keys by designing a hybrid symmetric encryption algorithm, a data security deletion algorithm, and a key separation storage method. The scheme also uses file filter driver technology to design a user operation state monitoring method to realize real-time monitoring of user access behavior. In addition, this paper designs and implements a prototype system. Through the verification and analysis of its usability and security, it is proved that the solution can meet the data security protection requirements of sensitive E-documents in the open network environment.

Computation of Kullback-Leibler Divergence in Bayesian Networks.

Kullback–Leibler divergence is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.