Problem In Computing Research Articles

Counting abelian squares efficiently for a problem in quantum computing

Counting abelian squares efficiently for a problem in quantum computing

Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm

In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment.

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimization Algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combines Particle Swarm Optimization (PSO) Meta-heuristics and deterministic Spanning Tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the PSO is used to schedule user task requests among fog devices, while hybrid MPSO-SPT will be used to perform resource allocation and resource management in the fog computing environment. The study proposed to implement the algorithms using iFogSim in the future work such that performance of the algorithms will be evaluated, assessed and compared with other state of art scheduling and resource management algorithms

The Problem of Ubiquitous Computing for Regulatory Costs

The Problem of Ubiquitous Computing for Regulatory Costs

The whole slide breast histopathology image detection based on a fused model and heatmaps

The analysis of whole slide breast histopathology image is one of the most effective techniques in the current breast cancer diagnosis. However, the size of whole slide image(WSI) is too large to be processed directly by machine learning or deep learning based classifier. To solve the problem of WSI-based computer aided diagnosis, a novel classification framework with three stages is proposed in this paper to locate and classify tumor region in the whole slide images. This framework contains three parts: patch-based classification, tumor region segmentation and location, and WSI-based classification. In the period of data processing, the Cycle-GAN(Cycle Generative Adversarial Network) model is firstly adopted to normalize the colors of the images. Then an improved DPN68(Dual Path Networks) based classifier and a Swin-Transformer based classifier are combined to improve the accuracy of patch-based classification. With the malignant confidence probability output by the fused model, the whole slide heatmap is generated to segment, locate and visually display the tumor regions. Statistical features are computed and selected from the heatmap based on medical diagnosis indicators, and then an SVM(Support Vector Machines) based classifier is applied to implement WSI-based classification to further confirm the tumor. Experimental results on the Camelyon16 dataset show that our proposed framework can effectively classify the whole slide breast histopathology image with high-precision.

Web Service Selection Based on QoS Prediction for Clustering and Ranking Services Using Auto-Encoder and K-Means

When selecting web services, users look for those that meet their requirements, primarily the overall functionality and non-functionality quality of service (QoS). In general, various service providers offer a large number of functionally similar services. That makes it very hard for users to find the best ones that satisfy their needs. Thus, service selection based on QoS has emerged as a challenging problem in service computing. So, the authors propose in this paper a web service selection method based on QoS prediction for clustering and ranking services using auto-encoder and k-means. Experiment results show that the proposed method efficiently improves the services' selection accuracy.

Evaluation of Operating System Using Electric Multi Criteria Decision Making Method

Operating system are computer software a user and a computer is between Communication an operating system (OS) is a All other applications on the computer Programs and programs through the launch program After loading into the computer A program that manages to make Limited application interface Requests for services through (API). Mostly, Apple Mac OS, Microsoft Windows and The top five, including Google's Android OS The IT sector is focused on OSes. Linux Operating system and Apple iOS. Operating system is most people are using the very difficult situation solving the problem in computer. ELECTRE (Elimination Et Choice Translating Reality) analysis using the ELECTRE methods, performance comparisons combining an Operating system 1, Operating system 2, Operating system 3, Operating system 4 Alternative value and Memory management, Process management, Storage management, protection and security, Software Features, Database management Evaluation Parameters in value. Operating system 1 is got the first rank whereas is the Operating system 4 is having the Lowest rank. In this paper Operating system for ELECTRE in Operating system 1 is got the first rank whereas is the Operating system 4 is having the Lowest rank.

Design of intelligent operation inspection platform based on the multi-agent system for live line measurement of substation

At present, substations are developing vigorously in an intelligent direction. In this paper, an intelligent operation inspection platform based on a multi-agent system is designed. The platform’s design includes the design of its physical architecture and its operation architecture. The physical architecture of the platform includes a substation main control server, fixed live line measurement device, intelligent operation inspection software, intelligent data centralization device, intelligent interface conversion device, wireless communication channel, etc. In terms of platform operation architecture, an operation framework based on a multi-agent system is designed, including the “environment” layer, data sensing layer, data processing layer, and analysis and decision layer, and the whole platform is built based on the concept of edge computing. To solve the task allocation problem of edge computing, a Markov decision model is designed to minimize time consumption and maximize resource utilization, and the deep Q network (DQN) algorithm is used to solve the problem. The design aims at being effective, practical, safe, compatible, easy to upgrade, and extensible, and builds an intelligent operation inspection platform for live line measurement of substation equipment based on a multi-agent system, to improve the intelligent automation level of substation operation inspection.

Cooperative Purchasing with General Discount: A Game Theoretical Approach

In some situations, sellers of certain commodities usually provide price discounts for large orders according to a decreasing unit price function. Buyers of such commodities can cooperate and form purchasing groups to benefit from these price discounts. A natural way to allocate the corresponding cost reductions is the equal price rule. We analyze this situation as a cooperative game. We show that when the decreasing unit price function is linear, the equal price rule coincides with the Shapley value and the nucleolus of the cooperative game. However, some buyers may argue that the equal price rule is not acceptable because it favors those who buy just a few units of the product. This can be more problematic when the decreasing unit price function is nonlinear: In that case, the equal price rule loses some of its good properties and it no longer matches the Shapley value or the nucleolus. Unlike the linear case, in this nonlinear case, the Shapley value and nucleolus do not assign the same price to all agents, so there are different price rules. However, they have a computability problem, as both are very laborious to calculate for a large number of agents. To find a suitable alternative, we first study the properties that a different price rule should have in this situation. Second, we propose a family of different price rules that hold those properties and are easy to calculate for a large number of agents. This family of different price rules provides buyers (companies, institutions, consumers, etc.) with an easy-to-implement method which ensures stability in cooperative purchasing.

Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning

Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli. Most existing approaches model the input-output behavior of an SNN in a deterministic fashion by assigning each input to a specific desired output spiking sequence. In contrast, in order to fully leverage the time-encoding capacity of spikes, this work proposes to train SNNs so as to match <i>distributions</i> of spiking signals rather than individual spiking signals. To this end, the paper introduces a novel hybrid architecture comprising a conditional generator, implemented via an SNN, and a discriminator, implemented by a conventional artificial neural network (ANN). The role of the ANN is to provide feedback during training to the SNN within an adversarial iterative learning strategy that follows the principle of generative adversarial network (GANs). In order to better capture multi-modal spatio-temporal distribution, the proposed approach – termed SpikeGAN – is further extended to support Bayesian learning of the generator's weight. Finally, settings with time-varying statistics are addressed by proposing an online meta-learning variant of SpikeGAN. Experiments bring insights into the merits of the proposed approach as compared to existing solutions based on (static) belief networks and maximum likelihood (or empirical risk minimization). In our experiments, handwritten digit images generated by SpikeGAN are observed to train an ANN classifier with <inline-formula><tex-math notation="LaTeX">$20\%$</tex-math></inline-formula> higher accuracy than a comparable belief network. Our experiments also demonstrate the use of SpikeGAN to generate neuromorphic data sets from handwritten digits. It is shown that these data can be used to train an SNN classifier that achieves an accuracy level approaching the baseline accuracy of an SNN classifier trained on rate-encoded real data.

Multiaccess Edge Integrated Networking for Internet of Vehicles: A Blockchain-Based Deep Compressed Cooperative Learning Approach

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing.

With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.

Shoeprint Image Retrieval Based on Dual Knowledge Distillation for Public Security Internet of Things

In order to implement rapid retrieval of large-scale crime scene investigation shoeprint image (SPI) in the intelligent mobile terminal of Public Security Internet of Things (PSIoT), a novel dual knowledge distillation (DKD) network is designed by fusing spatial attention (SA) distillation and feature distillation to solve its problems of limited computing and storage capacity. First, ResNet50 was modified by adding a new SA module and hash layer as the teacher model, and a light convolutional neural network (CNN) with the same SA and hash layer is designed as the student model. Then, the attention distillation loss function is constructed to distill the SA knowledge in the teacher module to the student module to improve the ability of the convolutional layer at the front end of the student network to capture the underlying visual features of the SPI. Finally, the feature distillation loss function is constructed to distill the semantic knowledge in the teacher module to the student module to improve the ability of the student module to express high-level semantics of the SPI. we compare our method on two data sets of SPID and FID-300 with other state-of-the-art methods in the SPI retrieval domain. The experimental results show that our method can improve the SPI retrieval baseline by a large margin and better than other methods.

Hybrid Annealing Method Based on subQUBO Model Extraction With Multiple Solution Instances

Ising machines are expected to solve combinatorial optimization problems efficiently by representing them as Ising models or equivalent quadratic unconstrained binary optimization (QUBO) models . However, upper bound exists on the computable problem size due to the hardware limitations of Ising machines. This paper propose a new hybrid annealing method based on partial QUBO extraction, called subQUBO model extraction, with multiple solution instances. For a given QUBO model, the proposed method obtains <inline-formula><tex-math notation="LaTeX">$N_I$</tex-math></inline-formula> quasi-optimal solutions (quasi-ground-state solutions) in some way using a classical computer. The solutions giving these quasi-optimal solutions are called <i>solution instances</i> . We extract a size-limited subQUBO model as follows based on a strong theoretical background: we randomly select <inline-formula><tex-math notation="LaTeX">$N_S$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$(N_S&lt;N_I)$</tex-math></inline-formula> solution instances among them and focus on a particular binary variable <inline-formula><tex-math notation="LaTeX">$x_i$</tex-math></inline-formula> in the <inline-formula><tex-math notation="LaTeX">$N_S$</tex-math></inline-formula> solution instances. If <inline-formula><tex-math notation="LaTeX">$x_i$</tex-math></inline-formula> value is much <i>varied</i> over <inline-formula><tex-math notation="LaTeX">$N_S$</tex-math></inline-formula> solution instances, it is included in the subQUBO model; otherwise, it is not. We find a (quasi-)ground-state solution of the extracted subQUBO model using an Ising machine and add it as a new solution instance. By repeating this process, we can finally obtain a (quasi-)ground-state solution of the original QUBO model. Experimental evaluations confirm that the proposed method can obtain better quasi-ground-state solution than existing methods for large-sized QUBO models.

Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, such as region-wise distance or functional similarity. To incorporate multiple relationships into a spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks. Leveraging the advantage of multi-modal machine learning, we propose to develop modality interaction mechanisms for this problem in order to reduce the generalization error by reinforcing the learning of multi-modal coordinated representations. In this work, we propose two interaction techniques for handling features in lower layers and higher layers, respectively. In lower layers, we propose grouped GCN to combine the graph connectivity from different modalities for a more complete spatial feature extraction. In higher layers, we adapt multi-linear relationship networks to GCN by exploring the dimension transformation and freezing part of the covariance structure. The adapted approach, called multi-linear relationship GCN, learns more generalized features to overcome the train–test divergence induced by time shifting. We evaluated our model on a ride-hailing demand forecasting problem using two real-world datasets. The proposed technique outperforms state-of-the art baselines in terms of prediction accuracy, training efficiency, interpretability and model robustness.

Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical Cloud computing

Cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical Cloud computing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical Cloud computing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical Cloud computing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic scheduling scenarios and DRLS using all of strategies achieves the best performance.

Time complexity analysis of quantum difference methods for linear high dimensional and multiscale partial differential equations

We investigate time complexities of finite difference methods for solving the high-dimensional linear heat equation, the high-dimensional linear hyperbolic equation and the multiscale hyperbolic heat system with quantum algorithms (hence referred to as the “quantum difference methods”). For the heat and linear hyperbolic equations we study the impact of explicit and implicit time discretizations on quantum advantages over the classical difference method. For the multiscale problem, we find the time complexity of both the classical treatment and quantum treatment for the explicit scheme scales as O(1/ε), where ε is the scaling parameter, while the scaling for the multiscale Asymptotic-Preserving (AP) schemes does not depend on ε. This indicates that it is still of great importance to develop AP schemes for multiscale problems in quantum computing.

Deployment and Allocation Strategy for MEC Nodes in Complex Multi-Terminal Scenarios.

Mobile edge computing (MEC) has become an effective solution for insufficient computing and communication problems for the Internet of Things (IoT) applications due to its rich computing resources on the edge side. In multi-terminal scenarios, the deployment scheme of edge nodes has an important impact on system performance and has become an essential issue in end–edge–cloud architecture. In this article, we consider specific factors, such as spatial location, power supply, and urgency requirements of terminals, with respect to building an evaluation model to solve the allocation problem. An evaluation model based on reward, energy consumption, and cost factors is proposed. The genetic algorithm is applied to determine the optimal edge node deployment and allocation strategies. Moreover, we compare the proposed method with the k-means and ant colony algorithms. The results show that the obtained strategies achieve good evaluation results under problem constraints. Furthermore, we conduct comparison tests with different attributes to further test the performance of the proposed method.

Challenges of Load Balancing Techniques in Cloud Environment: A Review

To highlight issues and challenges of the Load Balancing Algorithm (LBA) in the context of cloud computing. The authors of this work discussed load balancing and the load balancing problems in cloud computing, how to solve them, the basic techniques used to maintain load over the cloud, and the benefits and drawbacks of these algorithms. Furthermore, the static and dynamic load balancing strategies are compared. Load balancing methods in the cloud for data centers must avoid VM overload/low load, which is a challenge in cloud computing. Load balancing mechanisms aid in the management of workloads. The primary goal of load balancing among machines is to spread workloads and computing resources across one or more servers while lowering energy consumption and maximizing resource usage. Cloud computing is a type of computing in which scalable resources are made available on-demand and are frequently virtual, as in an online service. For a variety of reasons, including cost saving, speed, efficiency, performance, and security, cloud computing has recently become a popular choice for individuals and enterprises. Cloud computing uses the Internet to deliver a variety of services to a company's computers and devices. These resources can include data storage, databases, servers, networks, and software, among other tools and applications. In this paper, developers and researchers must design and construct a load balancer e for parallel and distributed cloud systems.

Relating Reported Speedup to Attainable Speedup in HPC Applications Based on a Programmer's Expertise

High Performance Computing (HPC) refers to the processing of complex computations using enhanced computing power for quick results and better accuracy. The parallel computing features of supercomputers provide an application program to obtain significant speedup over the serial implementation of certain problems in scientific computing. An HPC application largely relies on human expertise i.e., knowledge of the underlying architecture, algorithm used to solve the problem, his expertise in parallelizing the sequential code efficiently. Hence the speedup is largely dependent on the programmers expertise. Therefore, an obtained speedup of a certain HPC application, may not always be the best possible speedup that is potentially possible on that supercomputer. So, there is always a need of finding out if the obtained speedup is the highest possible speedup for a given problem and on a given supercomputer architecture. Thats why its important to objectively rate a programmers ability to relate it to his capability to parallelize a given code. Although, online judge platforms have given ratings to programmers when they are tied to online judges (such as- Topcoder, Codeforce and Codechef), types of contests (long, short, national-level, international level and based on problem categories), and the programmers sustained interest in participating (frequency of participation), problems solved, problems attempted, maturity (in years and capability) and other factors. The ranking could also be misleading in some cases for example, in solving a difficult problem, a relatively new programmer may climb to a top position because of his familiarity with the category of the problem although he may have less submissions and experience. On the other hand, an experienced programmer can see a drop in his rank if he under-performs in one or more contests. Hence, there is no generalized way to rank a programmer based on a rating provided by an online judge platform. Therefore, we need a more sophisticated and reliable ranking model that can help us to evaluate the actual rating of a programmer. This work will involve researching existing ranking algorithms and develop a model that establishes a relationship between Programmers grade or level and the Maximum Attainable Speedup in a particular HPC application.