Execution Of Algorithm Research Articles

On the Estimation of Logistic Models with Banking Data Using Particle Swarm Optimization

This paper presents numerical works on estimating some logistic models using particle swarm optimization (PSO). The considered models are the Verhulst model, Pearl and Reed generalization model, von Bertalanffy model, Richards model, Gompertz model, hyper-Gompertz model, Blumberg model, Turner et al. model, and Tsoularis model. We employ data on commercial and rural banking assets in Indonesia due to their tendency to correspond with logistic growth. Most banking asset forecasting uses statistical methods concentrating solely on short-term data forecasting. In banking asset forecasting, deterministic models are seldom employed, despite their capacity to predict data behavior for an extended time. Consequently, this paper employs logistic model forecasting. To improve the speed of the algorithm execution, we use the Cauchy criterion as one of the stopping criteria. For choosing the best model out of the nine models, we analyze several considerations such as the mean absolute percentage error, the root mean squared error, and the value of the carrying capacity in determining which models can be unselected. Consequently, we obtain the best-fitted model for each commercial and rural bank. We evaluate the performance of PSO against another metaheuristic algorithm known as spiral optimization for benchmarking purposes. We assess the robustness of the algorithm employing the Taguchi method. Ultimately, we present a novel logistic model which is a generalization of the existence model. We evaluate its parameters and compare the result with the best-obtained model.

Balancing Security and Efficiency: A Power Consumption Analysis of a Lightweight Block Cipher

This research paper presents a detailed analysis of a lightweight block cipher’s (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications.

Equipment control algorithm for cooling plant based on graph theory and path planning

This paper presents a novel path planning-based start-stop control algorithm for equipment in cooling plants designed to address the limitations of traditional serial start-stop control logic. The algorithm employs graph theory and path planning techniques to abstract the cooling plant schematic into a directed graph, enabling the selection of appropriate control paths and facilitating effective start-stop control decisions for the equipment. A key feature is the dynamic updating of the graph after each operation, which enables adaptive control target adjustments and prevents control blocking issues. Validation with real case scenarios demonstrates the effectiveness of the algorithm in automatic start-stop control, identifying equipment deviations, and handling faulty equipment switching. The algorithm also has the ability to handle some incorrectly turned on equipment, shutting down redundant equipment in the cooling plant system that is not in efficient operation. This algorithm shows the potential as an underlying execution algorithm of an advanced optimization algorithm to accelerate the implementation of advanced optimization algorithms.

Performance Comparison of UCB, TS, and -Greedy TS Algorithms through Simulation of Multi-Armed Bandit Machine

Multi-Armed Bandit (MAB) algorithms are classic algorithms that address sequential decision-making under uncertainty by solving the exploration-exploitation trade-off dilemma. This study investigates the performance comparison of multiple MAB algorithms using a simulated multi-armed bandit machine with Bernoulli reward distribution as the experimental environment. This study compares the performance differences among Upper Confidence Bound (UCB), Thompson Sampling (TS) and -Greedy Thompson Sampling (-TS) in this environment, and attempts to set different parameters, namely the number of arms and the number of experimental rounds and calculate the corresponding cumulative regret of the algorithm under various conditions. The size of the cumulative regret reflects the performance of each algorithm in the simulated slot machine model with rewards that conform to the Bernoulli distribution. In addition, the algorithm running time under the same conditions is also recorded to analyze from the perspective of algorithm efficiency. The experimental results show that under the experimental environment of this study, the cumulative regret produced by the UCB algorithm is more than three times that of the other two algorithms. When the number of trials is small, the cumulative regret generated by the TS algorithm is small, but overall, the performance of the TS algorithm and the -TS algorithm set in this experiment in minimizing the cumulative regret is not much different. However, TS runs in a shorter time under the same conditions. The results of this experiment show that after the number of rounds of experimental operation reaches a large enough number, the operating efficiency of the TS algorithm will be significantly higher than that of the -TS algorithm. TS algorithms have higher randomness, so they show better performance under this experimental condition. The -TS under the parameter setting of this study encourages exploration more in the early stage of the experiment, so it will produce greater cumulative regret than the traditional TS algorithm. In the long run, the performance difference between the two in the multi-armed bandit problem with Bernoulli distribution of rewards is very small. However, the TS algorithm has more advantages in algorithm execution efficiency, so when solving similar problems, the TS algorithm is a better choice.

AI and Discrimination: Sources of Algorithmic Biases

In this editorial, we define discrimination in the context of AI algorithms by focusing on understanding the biases arising throughout the lifecycle of building algorithms: input data for training, the process of algorithm development, and algorithm execution and usage. We draw insights from a few empirical studies to illustrate biases codified in algorithms that could result in harmful outcomes. We call on information systems scholars to prioritize scholarship in the area of algorithmic discrimination that can help generate new knowledge systems that would help safeguard against widespread and unaccountable harm.

Combining Genetic Algorithm with Local Search Method in Solving Optimization Problems

This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the same input data. The results show that when the number of possible Hamiltonian cycles in a graph is increased, the memetic algorithm finds better solutions. The execution time of both algorithms is comparable. Also, the number of solutions that mutated during the execution of the genetic algorithm exceeds 50% of the total number of all solutions generated by the crossover operator. In the memetic algorithm, the number of solutions that mutate does not exceed 10% of the total number of all solutions generated by the crossover operator, summed with those of the local search method.

Simulating the quantum Fourier transform, Grover's algorithm, and the quantum counting algorithm with limited entanglement using tensor networks

Quantum algorithms reformulate computational problems as quantum evolutions in a large Hilbert space. Most quantum algorithms assume that the time evolution is perfectly unitary and that the full Hilbert space is available. However, in practice, the available entanglement may be limited, leading to a reduced fidelity of the quantum algorithms. To simulate the execution of quantum algorithms with limited entanglement, tensor-network methods provide a useful framework, since they allow us to restrict the entanglement in a quantum circuit. Thus, we here use tensor networks to analyze the fidelity of the quantum Fourier transform, Grover's algorithm, and the quantum counting algorithm as the entanglement is reduced, and we map out the entanglement that is generated during the execution of each algorithm. In all three cases, we find that the algorithms can be executed with high fidelity even if the entanglement is somewhat reduced. For example, the quantum counting algorithm can be executed with a high fidelity beyond a certain threshold level of entanglement. Our results provide an understanding of the entanglement that is required to execute the three algorithms, and our simulations based on tensor networks can easily be applied to other algorithms. Published by the American Physical Society 2024

Design of an FPGA-Based Controller for Fast Scanning Probe Microscopy.

Atomic-scale imaging using scanning probe microscopy is a pivotal method for investigating the morphology and physico-chemical properties of nanostructured surfaces. Time resolution represents a significant limitation of this technique, as typical image acquisition times are on the order of several seconds or even a few minutes, while dynamic processes-such as surface restructuring or particle sintering, to be observed upon external stimuli such as changes in gas atmosphere or electrochemical potential-often occur within timescales shorter than a second. In this article, we present a fully redesigned field programmable gate array (FPGA)-based instrument that can be integrated into most commercially available standard scanning probe microscopes. This instrument not only significantly accelerates the acquisition of atomic-scale images by orders of magnitude but also enables the tracking of moving features such as adatoms, vacancies, or clusters across the surface ("atom tracking") due to the parallel execution of sophisticated control and acquisition algorithms and the fast exchange of data with an external processor. Each of these measurement modes requires a complex series of operations within the FPGA that are explained in detail.

Formalization of Elements and Control Schemes in Intelligent System Models

The purpose of the work is to formalize the concepts and principles that form the basis of the mathematical model for controlling the life cycles of intelligent systems. The controlling elements formal description is part of a universal model of such systems. This model part is important for implementing complex intelligent systems with a large amount and diversity of knowledge flows and knowledge processing schemes performed simultaneously. The other two areas of this model are associated with a unified logical-algebraic description of the concept of knowledge representation formalism, as well as concepts related to the architecture of intelligent systems. Formalization of ideas about control in intelligent systems is carried out according to the principle of a hierarchy of controlling elements and processes. The elements of the mathematical model that implements this principle correspond to event-controlled automata with infinite memory and associated algorithms for the automata simulation in specific systems. The objectives of the research include the development of formats for describing the hierarchy of diagrams and assemblies of control elements in a universal model of an intelligent system, and diagrams of algorithms for the functioning of intelligent systems based on the processing of model descriptions. Models of intelligent system control elements hierarchy and execution algorithms for the sets of activated control elements for the implementation time of life cycles of intelligent systems are described by extending the special ontological structures. These structures implement customizations of universal control model

HashGrid: An optimized architecture for accelerating graph computing on FPGAs

Large-scale graph processing poses challenges due to its size and irregular memory access patterns, causing performance degradation in common architectures, such as CPUs and GPUs. Recent research includes accelerating graph processing using Field Programmable Gate Arrays (FPGAs). FPGAs can provide very efficient acceleration thanks to reconfigurable on-chip resources. Although limited, these resources offer a larger design space than CPUs and GPUs.We propose an approach in which data are preprocessed in small chunks with an optimized graph partitioning technique for execution on FPGA accelerators. The chunks, located on the host, are streamed directly into a customized memory layer implemented in the FPGA, which is tightly coupled with the processing elements responsible for the graph algorithm execution. This improves application memory access latency, which is crucial in large-sale graph computing performance.This work presents a hardware design that, combined with graph partitioning, enables us to achieve high-performance and potentially scalable handling of large graphs (i.e., graphs with millions of vertices and billions of edges in current scenarios) while using popular graph algorithms. The proposed framework accelerates performance 56 times compared with CPU (multicore with 16 logical cores in our reference experiments), 2.5 times and 4 times faster compared to state-of-the-art FPGA and GPU solutions (FPGA has 15 compute units, and GPU reference has 128 streaming-multiprocessors in our experiments), respectively, when using the PageRank algorithm. For the Single-Source-Shortest-Past (SSSP) algorithm, we achieve speedups of up to 65x, 26x, and 18x compared to CPU, GPU, and FPGA works, respectively. Lastly, in the context of the Weakly Connected Component (WCC) algorithm, our framework achieves a speedup of up to 403 times compared to the CPU, 7.4x against the GPU, and it is faster than the FPGA alternatives up to 10.3x.

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner.

Recently developed long-read RNA sequencing technologies promise to provide a more accurate and comprehensive view of transcriptomes compared to short-read sequencers, primarily due to their capability to achieve full-length sequencing of transcripts. However, realizing this potential requires computational tools tailored to process long reads, which exhibit a higher error rate than short reads. Existing methods for assembling and quantifying long-read data often disagree on expressed transcripts and their abundance levels, leading researchers to lack confidence in the transcriptomes produced using this data. One approach to address the uncertainties in transcriptome assembly and quantification is by assigning the long reads to transcripts, enabling a more detailed characterization of transcript support at the read level. Here, we introduce TranSigner, a versatile tool that assigns long reads to any input transcriptome. TranSigner consists of three consecutive modules performing: read alignment to the given transcripts, computation of read-to-transcript compatibility based on alignment scores and positions, and execution of an expectation-maximization algorithm to probabilistically assign reads to transcripts and estimate transcript abundances. Using simulated data and experimental datasets from three well-studied organisms - Homo sapiens, Arabidopsis thaliana, and Mus musculus - we demonstrate that TranSigner achieves accurate read assignments, obtaining higher accuracy in transcript abundance estimation compared to existing tools.

Adaptive robust safety‐critical control of switched systems via single barrier function

SummaryThis paper addresses the problem of adaptive robust safety‐critical control (ARSCC) for switched systems, where the safety of subsystems is not necessary. A novel ARSCC framework and an executable algorithm are presented to solve the ARSCC problem by finding a switching signal and controllers of subsystems. To estimate uncertainties, a novel switched piecewise‐constant adaptive law is presented, which guarantees a pre‐computable estimation error boundary. A single barrier function (SBF) method is proposed to ensure safety of switched systems with uncertainties, where the safety of subsystems is satisfied only in some subregion of a given safe set, instead of the whole safe set. Based on the SBF method, a novel state‐dependent switching law possessing different dwell times for different subsystems, is established to orchestrate the switching among potentially unsafe subsystems. As a special case of the SBF method, a common barrier function method is presented to achieve safety of switched systems with uncertainties under switching signals with given dwell times. In addition, some sufficient conditions are derived to obtain safety and asymptotic stability for switched systems with uncertainties by combining SBF and single Lyapunov function. Finally, a switched RLC circuit system is given to illustrate the effectiveness of the theoretical results.

Nonlinear Multivariable System Identification: A Novel Method Integrating T-S Identification and Multidimensional Membership Functions

In this paper, a new multidimensional Takagi–Sugeno (T-S) identification technique is proposed for multivariable nonlinear systems. In this technique, multidimensional membership functions are designed using concepts from solid mechanics. The design of membership functions is carried out in multidimensional space, defining the principal axes from the eigenvectors of the inertia matrix, and it has the characteristic of dividing the space into regions with the same inertia. These regions are analyzed to define the center of gravity for each rule. Illustrative examples of multivariable nonlinear systems, such as a thermal mixing process and a binary distillation column, are selected to evaluate the effectiveness of the proposed method. The proposed method is compared with traditional T-S identification that uses one-dimensional membership functions and shows a reduction in the relative identification error and the algorithm execution time. Additionally, the proposed method prevents rules from being positioned outside the system’s range, thereby avoiding the generation of unnecessary rules.

Neural Network forAutomatic Encryption Using Key Component Analysis to Detect Network Intrusion inCloud Computing

Cloud computing is one of the networks that has attracted more people's attention than other networks in today's world, and the reason for this is that it stores data in itself. This network consists of different computers that are scattered in different places and each user will be able to be a member in this space by registering in it. Among the many important issues related to the environment is the issue of security, which has become a very complex challenge. In order to secure networks, tools have beencreated, and one of these tools is the use of artificial intelligence methods to establish security in this environment. In most of the researches carried out in this field, either the duration of the algorithm execution was very long or the researches did not have enough accuracy, so article, we tried to detect the penetration in cloud computing. This was done in two different scenarios for feature selection and reduction. In one scenario, we reduced the dimensions using the fundamental analysis method, and in the other scenario, we did this using the bat meta-heuristic algorithm. In both scenarios, we used self-encrypting neural network for classification, and the structure of this network was the same in both scenarios. The reason for using these two scenarios was that we want to compare the accuracy obtained and the execution time of the algorithm for the dimensionality reduction method with principal component analysis and meta-heuristic algorithm. , the obtained results showed that the accuracy of the dimension reduction method with the meta-heuristic algorithm was more accurate than the principal component analysis method, but its execution time was approximately 13 minutes more than the principal component analysis method.

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

Utilizing classical programming principles in the Intel Quantum SDK: implementation of quantum lattice Boltzmann method

We explore the use of classical programming techniques in implementing the quantum lattice Boltzmann method in the Intel Quantum SDK – a software tool for quantum circuit creation and execution on Intel quantum hardware. As hardware access is limited, we use the state vector simulator provided by the SDK. The novelty of this work lies in leveraging classical techniques for the implementation of quantum algorithms. We emphasize the refinement of algorithm implementation and devise strategies to enhance quantum circuits for better control over problem variables. To this end, we adopt classical principles such as modularization, which allows for systematic and controlled execution of complex algorithms. Furthermore, we discuss how the same implementation could be expanded from state vector simulations to execution on quantum hardware with minor adjustments in these configurations.

Virtual Time III, Part 3: Throttling and Message Cancellation

This is Part 3 of a trio of papers that unify in a natural way the two historically distinct parallel discrete event synchronization paradigms, optimistic and conservative, combining the best properties of both into a single framework called Unified Virtual Time (UVT) . In this part we survey the synchronization effects that can be achieved by restricting to corner cases the relationships permitted among the control variables, GVT , CVT , TVT , and LVT , which were defined in Part 1 . We also survey various throttling policies from the literature and describe how they can be implemented in UVT by controlling the value of TVT , including policies that can take advantage of rollback in addition to LP blocking. A significant result is a new category of efficient and higher precision throttling algorithms for optimistic execution that are based on optimistic lookahead , defined in a way that is symmetric to what we now call the conservative lookahead information that is traditionally used for conservative synchronization. Finally, we present a novel algorithm allowing the choice between lazy and aggressive cancellation to be made on a message-by-message basis using either external logic expressed in the model code, or policy code internal to the simulator, or a mixture of both.

Synergistic Dynamical Decoupling and Circuit Design for Enhanced Algorithm Performance on Near-Term Quantum Devices.

Dynamical decoupling (DD) is a promising technique for mitigating errors in near-term quantum devices. However, its effectiveness depends on both hardware characteristics and algorithm implementation details. This paper explores the synergistic effects of dynamical decoupling and optimized circuit design in maximizing the performance and robustness of algorithms on near-term quantum devices. By utilizing eight IBM quantum devices, we analyze how hardware features and algorithm design impact the effectiveness of DD for error mitigation. Our analysis takes into account factors such as circuit fidelity, scheduling duration, and hardware-native gate set. We also examine the influence of algorithmic implementation details, including specific gate decompositions, DD sequences, and optimization levels. The results reveal an inverse relationship between the effectiveness of DD and the inherent performance of the algorithm. Furthermore, we emphasize the importance of gate directionality and circuit symmetry in improving performance. This study offers valuable insights for optimizing DD protocols and circuit designs, highlighting the significance of a holistic approach that leverages both hardware features and algorithm design for the high-quality and reliable execution of near-term quantum algorithms.

Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

Optimization of Large-Scale Sparse Matrix-Vector Multiplication on Multi-GPU Systems

Sparse matrix-vector multiplication (SpMV) is one of the important kernels of many iterative algorithms for solving sparse linear systems. The limited storage and computational resources of individual GPUs restrict both the scale and speed of SpMV computing in problem-solving. As real-world engineering problems continue to increase in complexity, the imperative for collaborative execution of iterative solving algorithms across multiple GPUs is increasingly apparent. Although the multi-GPU-based SpMV takes less kernel execution time, it also introduces additional data transmission overhead, which diminishes the performance gains derived from parallelization across multi-GPUs. Based on the non-zero elements distribution characteristics of sparse matrices and the trade-off between redundant computations and data transfer overhead, this paper introduces a series of SpMV optimization techniques tailored for multi-GPU environments and effectively enhances the execution efficiency of iterative algorithms on multiple GPUs. Firstly, we propose a two-level non-zero elements-based matrix partitioning method to increase the overlap of kernel execution and data transmission. Then, considering the irregular non-zero elements distribution in sparse matrices, a long-row-aware matrix partitioning method is proposed to hide more data transmissions. Finally, an optimization using redundant and inexpensive short-row execution to exchange costly data transmission is proposed. Our experimental evaluation demonstrates that, compared with the SpMV on a single GPU, the proposed method achieves an average speedup of 2.00x and 1.85x on platforms equipped with two RTX 3090 and two Tesla V100-SXM2, respectively. The average speedup of 2.65x is achieved on a platform equipped with four Tesla V100-SXM2.