Low Execution Research Articles

Tomography of nonlinear materials via the monotonicity principle

In this paper we present a first non-iterative imaging method for nonlinear materials, based on Monotonicity Principle. Specifically, we deal with the inverse obstacle problem, where the aim is to retrieve a nonlinear anomaly embedded in linear known background. The Monotonicity Principle (MP) is a general property for various class of PDEs, that has recently generalized to nonlinear elliptic PDEs. Basically, it states a monotone relation between the point-wise value of the unknown material property and the boundary measurements. It is at the foundation of a class of non-iterative imaging methods, characterized by a very low execution time that makes them ideal candidates for real-time applications. In this work, we develop an inversion method that overcomes some of the peculiar difficulties in practical application of MP to imaging of nonlinear materials, preserving the feasibility for real-time applications. For the sake of clarity, we focus on a specific application, i.e. the Magnetostatic Permeability Tomography where the goal is retrieving the unknown (nonlinear) permeability by boundary measurements in DC operations. This choice is motivated by applications in the inspection of boxes and containers for security. Reconstructions from simulated data prove the effectiveness of the presented method.

HCDR-based motion planning for mobile manipulators with the continuous trajectory task

Mobile manipulators have been widely used in modern industrial fields due to their advantages of large operating workspace and high motion dexterity. For the application, the path planning has been a core foundation for its motion control. However, as the environmental complexity and performance requirements increase, existing methods are limited by low execution efficiency, and lack of comprehensive consideration of motion performances. A motion planning technique is proposed for mobile manipulators with continuous trajectory task. In this technique, a hierarchical constraints dimension reduction (HCDR) strategy is presented based on the task decomposition and performance requirements analysis of each stage. HCDR strategy aims to improve the execution efficiency of the algorithm through local operations of performance constraints and the local simplification of the distance calculation at different stages of the task. Models of inverse kinematics and performance indexes are established to provide basis for constraint conditions, including distance models with different accuracy, manipulability index model and trajectory tracing error model. Two optimization strategies are presented to solve the motion planning problem considering the overall performance of the system. And simulation results show that the three-level strategy has the higher convergence efficiency than the bi-level strategy, as well as can guarantee the motion performance of the system.

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system’s load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven.

A Modular Direct Description Method With Low Memory Usage and Execution Time for Hysteresis Modeling and Compensation of Piezoelectric Actuators

A Modular Direct Description Method With Low Memory Usage and Execution Time for Hysteresis Modeling and Compensation of Piezoelectric Actuators

도시재생사업의 성과측정에 관한 연구

The urban regeneration project started under the Park Geun-hye administration and was promoted more heavily as a New Deal project under the Moon Jae-in administration. However, as the urban regeneration project progressed, problems arose that residents could not feel due to poor implementation performance and low execution ability. Accordingly, in this study, in order to establish and achieve the goals of the urban regeneration revitalization plan, a management system and performance indicators for monitoring, evaluation, and feedback are presented to systematically and comprehensively manage everything from plan establishment to performance and effect derivation, and a resident satisfaction survey to measure performance. We conducted an investigation. In order to accurately measure performance, the ‘Comprehensive Performance Index’ presented by the central government was used as a common indicator, and the ‘Local Government Performance Index’ was comprehensively analyzed and used as an auxiliary indicator. In this study, research was conducted targeting the ‘Jeonju Station Area Urban Regeneration Area’, which is currently under construction, to select indicators for performance measurement, and practical data was presented in relation to the research method.

The end of an era: Who paid the price when the livestock futures pits closed?

AbstractThis paper evaluates the impact of the Chicago Mercantile Exchange's (CME) decision to close the livestock futures pits on the execution quality of customer orders. Our findings suggest that, prior to its closure, the livestock futures pit offers high immediacy execution and attracts large orders. Because such high immediacy orders generally execute faster and cost more, their migration to the electronic market after the pit closure explains why the execution of electronic orders becomes on average speedier and more expensive for customers who used to be active pit users. However, our results also indicate that these pit‐user customers face a lower overall execution cost following the pit closure when we account for all their orders, pit and electronic.

An unrolled half-quadratic approach for sparse signal recovery in spectroscopy

This paper addresses the problem of sparse signal reconstruction frequently arising in analytical chemistry. Spectroscopic restoration is an important task that was tackled using two distinct approaches. First, model-based iterative methods were deployed. These methods rely on a strong theoretical background. However they face practical obstacles in terms of hyperparameter tuning. Conversely, approaches based on deep neural networks are fast and easy to implement. Nonetheless, they are often described as black-box tools, and their stability/robustness are ongoing research topics. In this paper, we propose a deep neural network based on unrolling a Majorization-Minimization (MM) Half-Quadratic (HQ) algorithm. This allows us to build interpretable layers mirroring iterations, making it possible to learn automatically data-driven hyperparameters such as regularization and stepsizes. Furthermore, we propose a dictionary of custom activation functions derived from potentials used in the original variational model. This interpretation of activations can be useful for analyzing the stability of neural networks. The efficiency of our method in comparison to iterative and learning-based methods is showcased through various experiments conducted on realistic mass spectrometry (MS) databases with various blur kernels and noise levels. All experiments show an improved reconstruction quality of chemical signals, while maintaining a low execution time at the test phase. All the results are reproducible, through the code shared on a repository available at https://github.com/GHARBIMouna/Unrolled-Half-Quadratic.

A computational approach to heat transfer and ablation in space capsule insulation

In this article, the heat transfers and ablation phenomena of thermal insulations utilized in manned space capsules are investigated. In this regard, by collecting and solving the equations related to ablation insulations, a computer program has been developed that is able to predict the thermal response of these insulations under various operating conditions. This approach considers mass and heat transfer equations in two-dimensional solid bodies. To solve these equations, finite volume method and implicit formulation for time dependence have been used. The reaction equation, written in the form of Arrhenius, is solved using the Runge-Kutta method, and the density and the flux of the gas produced at each step are obtained. Furthermore, a practical model with low computational requirements and execution time and execution time is presented to consider the regression rate based on determining the identifier for destroyed cells, charred cells, and virgin material. The research results demonstrate that increasing the thickness of the layers, the heat of decomposition and ablation, the intensity of the reaction, and reducing the thermal diffusion coefficient, density of char, and temperature of ablation enhance the efficiency of thermal insulation. Validation of the model with experimental results in silica-phenolic insulation reveals a good agreement between simulation and experimental observations. Additionally, it was shown that for a carbon-phenolic insulation with a thickness of 5 mm in the specified space capsule under the given conditions, the maximum recession rate is 1.9 mm at a maximum temperature of 1000 K and 4.9 mm at a maximum temperature of 1300 K.

CILP: Co-Simulation-Based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments

Intelligent Virtual Machine (VM) provisioning is central to cost and resource efficient computation in cloud computing environments. As bootstrapping VMs is time-consuming, a key challenge for latency-critical tasks is to predict future workload demands to provision VMs proactively. However, existing AI-based solutions tend to not holistically consider all crucial aspects such as provisioning overheads, heterogeneous VM costs and Quality of Service (QoS) of the cloud system. To address this, we propose a novel method, called CILP, that formulates the VM provisioning problem as two sub-problems of prediction and optimization, where the provisioning plan is optimized based on predicted workload demands. CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores. We extend the neural network to also act as an imitation learner that dynamically decides the optimal VM provisioning plan. A transformer based neural model reduces training and inference overheads while our novel two-phase decision making loop facilitates in making informed provisioning decisions. Crucially, we address limitations of prior work by including resource utilization, deployment costs and provisioning overheads to inform the provisioning decisions in our imitation learning framework. Experiments with three public benchmarks demonstrate that CILP gives up to 22% higher resource utilization, 14% higher QoS scores and 44% lower execution costs compared to the current online and offline optimization based state-of-the-art methods.

Resource allocation for fog computing based on software-defined networks

<p><span lang="EN-US">With the emergence of cloud computing as a processing backbone for internet of thing (IoT), fog computing has been proposed as a solution for delay-sensitive applications. According to fog computing, this is done by placing computing servers near IoT. IoT networks are inherently very dynamic, and their topology and resources may be changed drastically in a short period. So, using the traditional networking paradigm to build their communication backbone, may lower network performance and higher network configuration convergence latency. So, it seems to be more beneficial to employ a software-defined network paradigm to implement their communication network. In software-defined networking (SDN), separating the network’s control and data forwarding plane makes it possible to manage the network in a centralized way. Managing a network using a centralized controller can make it more flexible and agile in response to any possible network topology and state changes. This paper presents a software-defined fog platform to host real-time applications in IoT. The effectiveness of the mechanism has been evaluated by conducting a series of simulations. The results of the simulations show that the proposed mechanism is able to find near to optimal solutions in a very lower execution time compared to the brute force method.</span></p>

Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing and helps significantly in social media advertising. Previous Greedy and Reverse Influence Sampling-based IM approaches are ineffective in real-world social networks due to their significant computational cost and execution time. Further, even heuristic approaches applied to IM generally yield minimal performance gain relative to the decreased time complexity. This presents a challenge in developing cost-effective algorithms with low execution time that can handle diverse social networks. In this paper, we propose the discretization of the nature-inspired Harris’ Hawks Optimization meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris’ Hawks’ intelligence, we employ a neighbor scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace, reducing the total computational cost. We evaluate the efficacy of our proposed DHHO approach on eight social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.

ADRLO: Adaptive deep reinforcement learning-based offloading for edge computing

Computation offloading, as an efficient and promising computing paradigm for mobile edge computing (MEC), provides optimal computation allocation between MEC servers and Internet of Things (IoT) devices. However, in modern wireless communications, most of the computational tasks are latency-sensitive, presenting an efficiency challenge for offloading services. Moreover, to make the best use of computation resources, it remains an issue to ensure stable optimal offloading for the allocation of computation resources. In this paper, a novel adaptive deep reinforcement learning-based offloading (ADRLO) framework is developed to achieve a stable optimal computation revenue with low execution latency for MEC offloading. Structurally, we implement a residual connected deep neural network (DNN) to generate a series of offloading strategies and use them to obtain the global system computation rate. Especially, the strategy that achieves the maximum computation rate is then selected as experience to support the DNN training, forming a closed-loop deep reinforcement learning (DRL) mechanism. To further enhance the efficiency and stability of the MEC offloading, an adaptive regulator is presented for the framework parameters including the size of action space and the DNN learning rate. Numerical results show that, compared to existing state-of-the-art approaches, our proposal achieves optimal computation revenue, exhibits better convergence performance, and significantly reduces the execution latency by almost 50%.

PaaS platform security enhancement using fuzzy based access control and trust based signature

AbstractPlatform‐as‐a‐Service (PaaS) is one of the cloud computing services which will be offered to the clients through the virtualization platform. The problem is constructed in the PaaS platform effectively excluding internet service suppliers which do not meet highly secure standards, and so security issues can be a significant a hurdle to online computation. To address the aforementioned issues, the suggested method primarily concentrated proceeding the safety of PaaS infrastructure by using a novel fuzzy based access control technique, in which the information source is realized by using CDO security feature. To further enhance the security, security using trust based signature has been used in which the class and packet filter for each specified authorization were mapped to our class by modifying the security feature code. This public key will be utilized for authenticate that transaction relevant signature if the identity provider (IdP) has provided public safety data about this same two major components represents this little token on whose trusted trademark components are built put, that is, the entire item or even the claims contained. The suggested technique outperforms other current methods in terms of several performance parameters, with a high throughput of 14, CPU utilization of 12.5, and a low execution time of 5.2 s. The proposed ‘PaaS platform security enhancement with the use of fuzzy and trust‐based signatures’ provides high throughput and low execution time. This research work proposes a novel approach to enhancing the security of PaaS infrastructure, which is offered to clients through the virtualization platform in cloud computing. The current problem with PaaS is that it excludes internet service providers that do not meet high‐security standards, which poses a significant challenge to online computation. The suggested approach focuses on improving the safety of the PaaS platform by employing a fuzzy‐based access control technique, using the connectionless data objects security feature as the information source. To further strengthen security, the approach utilizes a trust‐based signature technique where the class and packet filter for each authorization are mapped to the class of the proposed method by modifying the security feature code. The proposed public key is then used to authenticate the transaction relevant signature, provided the IdP has given public safety data about these two components of the token on which Trusted Trademark components are built, that is, the entire item or even the claims contained within it. The proposed approach outperforms current methods in terms of several performance parameters, such as a high throughput of 14, a high CPU utilization of 12.5, and a low execution time of 5.2 s. In summary, the proposed PaaS platform security enhancement using fuzzy and trust‐based signatures provides an effective way to achieve high throughput and low execution time while ensuring platform security.

Utilizing MATLAB machine learning models to categorize transient events in a nuclear power plant using generic pressurized water reactor simulator

Enhancing safety and dependability within nuclear power facilities holds paramount importance in safeguarding both individuals and the environment. The adoption of machine learning for diagnosing faults in these plants is steadily gaining interest, driven by its capacity to detect faults, alleviate human errors in high-pressure scenarios, and ensure the secure and consistent operation of these facilities swiftly and accurately.This paper examines the use of machine learning models for fault diagnostics, specifically, the identification of transient events in a nuclear power plant to reduce human errors. The data was collected from WSC’s Generic Pressurized Water Reactor (GPWR) simulator and processed using MATLAB. The simulator encompasses models for both the primary and secondary systems of the nuclear power plant (NPP). Additionally, it incorporates models for the control systems and instrumentation responsible for monitoring and regulating the reactor, serving as integral components for data extraction and transient modeling. A total of 9 different transient events were simulated with 12 different initial conditions to create a dataset with 72,000 observations. Nine types of classification models (33 total preset models) were trained and validated using the classification learners application. Among them, the Neural Network Classifiers (NNC) displayed the highest average accuracy of 90%. The Fine Tree, Ensemble Bagged Trees, and Medium Neural Network models were the best-performing individual models with validation accuracies above 90% and a maximum training time of 8 min. These models were further analyzed using accuracy, confusion matrix, precision, recall, and F1 score. To optimize these models, techniques such as different validation schemes and feature selection were utilized to further reduce their training time and improve their prediction accuracy. The optimized models boasted comparable accuracies with a maximum training time of under 1.5 min. The results of this study exhibited favorable comparisons with other machine-learning endeavors in the field of reactor transient detection and diagnostics. Notably, the study maintained low execution and computation times while preserving high levels of accuracy. This study offers insightful information on how AI and machine learning can be used to improve nuclear power plant diagnostics, enhance safety, and provide support to the operator.

WITHDRAWN: Efficient fragmentation and allocation on clustering in distributed environment (FACE)

Distributed data processing systems require a reliable storage structure that ensures consistent, high-scale, and scalable data access for all users. Data fragmentation and subsequent allocation are important aspects of a flexible distributed database system. Existing techniques may result in significant data loss while performing data allocation and fragmentation. To overcome these issues a novel fragmentation and Allocation on Clustering in Distributed Environment (FACE) is proposed for reliable storage with scalable data access for all users. Initially, the user query is given to distributed database system (DDBS), then the query is fed to the query parser. The query parser parses and converts a textual query into a query object. After parsing the query data, it is passed to the query optimizer, which optimizes the query to access the data more efficiently. In the next step, the query is fragmented using dual (horizontal and vertical) fuzzy C means clustering. In Dual fuzzy C means clustering, the vertical fragmentation focuses on clustering attribute values that are frequently accessed together, and horizontal fragmentation focuses on grouping records that meet data selection criteria. After fragmentation, the query is allocated using sequential rule mining, in which queries occur in sequential order. The allocated query is optimized using the Efficient Fragment Security (EFS) algorithm to normalize the parameters to achieve better results. The performance metrics of the proposed techniques are calculated based on Final Prediction Error (FPE), and Execution time. The proposed FACE achieves a lower execution time of the 1.56 s. The result shows that the proposed FACE decreases the overall execution time lower than the 4.70 s, 4.46 s, 3.3 Sec, and 3.14 s in MAPA, DMA, BBE, and RATS-HM respectively.

Design and implementation of an efficient container tag dynamic taint analysis

Dynamic taint analysis is a commonly used technique in software security. By tracking the processing of tainted data in the program, dynamic taint analysis can provide users with information on how the variables they are interested in are affected by the program input. The information is stored in tags corresponding to the variables, and the type of the tag determines the level of detail it can store. While integer tags can only indicate whether the taint exists, container tags provide knowledge of which part of the input the taint originated from. This knowledge is crucial for fields such as protocol reverse engineering and fuzzing. Despite their advantages, container tags suffer from low execution efficiency. In some applications, the execution time can increase by thousands of times as compared to the use of integer tags. In this paper, we propose an efficient container tag scheme based on the Reduced Ordered Binary Decision Diagram. The test results indicate that our container tag scheme achieves average speedups of 7.53x and 100.96x compared to the two container tag schemes utilized in libdft64.

COPS: An improved information retrieval-based bug localization technique using context-aware program simplification

Information Retrieval Based Bug Localization (IRBL) techniques are well suited for large-scale software debugging with fewer external dependencies and lower execution costs. However, existing IRBL techniques have several challenges, including localization granularity and applicability. First, existing IRBL techniques have not yet achieved statement-level bug localization. Second, almost all studies are limited to Java-based projects, while their effectiveness for other popular programming languages (e.g., Python) is unknown. The reason for these deficiencies is that existing IRBL techniques mainly rely on conventional NLP techniques to analyze the bug reports and have not yet fully utilized the stack traces attached to the bug reports. To improve the IRBL technique, we propose a context-aware program simplification technique – COPS – that can localize defective statements in suspicious files by analyzing the stack traces in bug reports, enabling statement-level bug localization for Python-based projects. Our experiment is based on 948 bug reports, and the results show that COPS can effectively localize buggy statements. First, compared to the original stack traces, Top@10 is improved by 102.6%, MAP@10 by 56.2%, and MRR@10 by 95.6%. We found that actual buggy code entities are more likely to appear in the first five frames of the stack trace. Second, COPS can achieve equally good localization performance compared to state-of-the-art statement-level bug localization techniques and achieve 92% buggy statement coverage with a full-scope search. Finally, experiments found that the stack trace’s first two-thirds of information is more conducive to localizing buggy statements.

Predicting Student Performance in Future Exams via Neutrosophic Cognitive Diagnosis in Personalized E-Learning Environment

To provide intelligent learning guidance for students in e-learning systems, it is necessary to accurately predict their performance in future exams by analyzing score data in past exams. However, existing research has not addressed the uncertain and dynamic features of students' cognitive status, whereas these features are essential for improving the accuracy of performance prediction. A novel approach via neutrosophic cognitive diagnosis is proposed to predict student performance in a personalized e-learning environment. This approach innovatively employs the neutrosophic set (NS) theory to comprehensively measure the students' cognitive status on knowledge concepts from three features, i.e., understanding level, degree of misunderstanding, and uncertainty. A new measurement method of NS similarity is used to identify top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> neighboring students for one student. Then, we predict a student's performance on exercises in future exams by integrating a neighborhood-based collaborative filtering algorithm with a probabilistic matrix factorization method to address the data sparsity problem. Finally, the experiments based on real-world datasets demonstrate that the proposed approach provides higher prediction accuracy than the existing ones with a low execution cost.

Provably secure authentication protocol for traffic exchanges in unmanned aerial vehicles

Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled regions. In addition, they have been deployed in civilian critical infrastructure monitoring. In this environment, real-time and massive data is exchanged between the aerial vehicles and the ground control stations. Depending on the mission of these aerial vehicles, some of the collected and transmitted data is sensitive and private. Therefore, many security protocols have been presented to offer privacy and security protection. However, majority of these schemes fail to consider attack vectors such as side-channeling, de-synchronization and known secret session temporary information leakages. This last attack can be launched upon adversarial physical capture of these drones. In addition, some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high overheads. In this paper, an authentication protocol based on lightweight quadratic residues and hash functions is developed. Its formal security analysis is executed using the widely deployed random oracle model. In addition, informal security analysis is carried out to show its robustness under the Dolev–Yao (DY) and Canetti–Krawczyk (CK) threat models. In terms of operational efficiency, it is shown to have relatively lower execution time, communication costs, and incurs the least storage costs among other related protocols. Specifically, the proposed protocol provides a 25% improvement in supported security and privacy features and a 6.52% reduction in storage costs. In overall, the proposed methodology offers strong security and privacy protection at lower execution time, storage and communication overheads.

Deep Reinforcement Learning Empowers Wireless Powered Mobile Edge Computing: Towards Energy-Aware Online Offloading

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.