Reviewerly/exHarmony huggingface dataset:
Surrogate-Assisted Evolution of Convolutional Neural Networks by Collaboratively Optimizing the Basic Blocks and Topologies

Convolutional neural networks (CNNs) are prominent in many fields owing to their outstanding feature extraction abilities. Many excellent CNNs have been carefully designed by algorithm researchers; however, the design process is limited by the inherent knowledge of the researchers. Inspired by the existing successful block-based neural architecture search, we develop a collaboratively automatically evolutionary CNNs algorithm (CAE-CNN), which employs a surrogate-assisted genetic algorithm to search for a satisfactory CNN architecture by collaboratively optimizing the basic blocks in ResNet and DenseNet and their topologies. The encoding space of CAE-CNN consists of three basic units (pooling, ResNet, and DenseNet units) and a connection topology with a variable size. To effectively evolve the population, we design a double-module crossover operation and a multi-type mutation operation to collaboratively evolve the units and the topology. To address the problem of a rapidly increasing search space caused by the topological search, we use a random forest as the surrogate model to estimate the fitness of an individual to accelerate the search. CAE-CNN is a completely automatic algorithm in which a satisfactory CNN architecture can be obtained without any manual intervention. Experimental results show that CAE-CNN could archive competitive performance in terms of classification accuracy on four image classification datasets, and it consumes fewer computing resources than many algorithms.
High-Resolution Synthetic Solar Irradiance Sequence Generation: An LSTM-Based Generative Adversarial Network

The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources’ primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy Laboratory. The results of the case studies demonstrate the proposed approach’s capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.
Dynamic Spectrum Allocation for Bursty Traffic Using Quantum Annealing

As the demand for wireless communications continues to grow, securing limited spectrum resources has become an issue. Under such circumstances, Dynamic Spectrum Allocation (DSA), in which existing frequency bands are jointly used and allocated to carriers, has been attracting attention. However, the problem is that a large number of base stations and interferers causes a combinatorial explosion, which requires an enormous amount of computation time. In a previous study, this problem was viewed as a combinatorial optimization problem and formulated to be solved by quantum annealing for the time slot-specific requirements of each base station. However, each base station uses the frequency band differently, and the timeslot length requirement must also be considered. In this paper, we focus on the DSA problem of maximizing the satisfaction of the time-based requirements of each base station and describe a formulation for solving the problem using quantum annealing. These time-based requirements can accommodate the demand for bursty traffic, which was not the case in previous papers. We also present implementation results using Fixstars Amplify and discuss future prospects.
Simulation of Pier Ground Motion Wave Propagation Based on Machine Learning Algorithm and FEPG Finite Element Analysis System

Earthquake early warning system is a new improvement of earthquake monitoring technique and a new means to reduce earthquake disasters, which uses seismic instruments to observe data and quickly measure earthquake parameters to release information such as arrival time and intensity before destructive seismic waves arrive. By systematically considering the influence of seismogenic fault (source), three-dimensional crustal layered velocity structure (propagation path, medium) and local site conditions (overburden, surface undulation), a simplified calculation means based on Green’s function and finite element model is used to numerically simulate near-field ground motions, so as to meet the urgent needs of current earthquake prevention and disaster reduction and engineering investigation. In essence, FEPG finite element analysis and simulation means is to solve the wave equation of seismic wave, so the simulated seismic wave field contains all the information of seismic wave propagation, which can provide more evidence for studying the propagation mechanism of seismic wave and the interpretation of complex strata. Therefore, FEPG finite element analysis and simulation means has been playing a vital role in seismic simulation. Based on machine learning algorithm and FEPG finite element analysis system, it is of great significance to study the simulation of pier ground motion wave propagation in this paper.
Adjacent Intensity Matrix with Linkage Identification for Large-Scale Optimization in Noisy Environments

This paper proposes a novel decomposition method: Adjacent Intensity Matrix with Linkage Identification (AIM-LI) for large-scale optimization problems (LSOPs) in noisy environments. The most advanced differential grouping (DG)-based methods such as DG2, Recursive DG, and dual DG are environmentally sensitive, and the presence of noise may misguide these methods to identify the originally separable decision variables as non-separable. Although the noise may affect the absolute intensity of interactions, we can detect the intensity between separable and non-separable decision variables with potential relative differences. This difference can help us classify the separability. Based on this hypothesis, we define the interaction intensity of pairwise decision variables and save them to AIM. A significant intensity (SI) is selected from AIM to transform the AIM into the dependency structure matrix (DSM). To verify the feasibility of AIM-LI, we first run the pre-experiments and mathematically analyze the possibility of interaction identification in noisy environments. Furthermore, we implement a decomposition experiment on CEC2013 with different strengths of noise. Theoretical analysis and experimental results show that our proposal has great potential to detect and classify the separability of problems in noisy environments.
Reproducible Query Processing and Data Citation of in Situ Soil Moisture Data

Data in today's dynamic world undergoes constant change and evolution, spanning various formats such as text, websites, tweets, and sensor readings. Storing and referencing these diverse data types pose significant challenges due to data movement, changes in content or structure, and limited availability. Efficient data identification is crucial for speeding up scientific discovery and result validation, especially when data accessibility is guaranteed. Recent years have witnessed progress in data citation practices, with conferences mandating the inclusion of utilized and generated data. However, existing solutions primarily cater to static datasets, rendering them ineffective for dynamically evolving ones. This paper addresses this gap by providing a tailored dynamic data citation prototype for the International Soil Moisture Network, one of the largest scientific in situ soil moisture databases. Our work encompasses the implementation and evaluation of different data versioning strategies and a query store architecture that enables the citation, reproducibility, and verification of large sets of SQL queries to recreate data requests by users. By applying the RDA Dynamic Data Citation Guidelines, we assess the necessary needs for such a system and further measure the performance and storage impact of our proposed approaches.
An Hyper-Heuristic Based Population Management Through Statistical Analysis and Phases Optimization

Hyper-heuristics (HH) are strategies that have a high-level mechanism to combine, select or generate heuristics at a low level to find solutions based on the information received during the search process. Typically, an HH approach involves evaluating several algorithms and constructing a priori a structure containing the information required to select, combine or generate the appropriate heuristic to solve a given problem using a usually probabilistic selection method. This paper considers a constructive online learning HH with an agent population selection and control strategy for each heuristic with a reward-punishment approach based on a mean absolute deviation criterion and exploitation-exploration stages to optimize these phases in the optimization process. We compare our proposal, denominated Hyper-heuristic based on the mean absolute deviation metric and the exploration-exploitation stages for the population management (HH-MAD) using uni-modal, multi-modal, composite, and shifted benchmark functions among different optimization algorithms. The experimental results validated the central tendency measures and the non-parametric tests, showing that HH-MAD is competitive, outperforming the other approaches.
Client Recruitment for Federated Learning in ICU Length of Stay Prediction

Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data for which sharing and centralizing remains a challenge as the result of data and privacy regulations. Federated Learning (FL) is well-suited to tackle these challenges. However, FL comes with a new set of open problems related to communication overhead, efficient parameter aggregation, client selection strategies and more. In this work, we address the step prior to the initiation of a federated network for model training, client recruitment. By intelligently recruiting clients, communication overhead and overall cost of training can be reduced without sacrificing predictive performance. Client recruitment aims at pre-excluding potential clients from partaking in the federation based on a set of criteria indicative of their eventual contributions to the federation. In this work, we propose a client recruitment approach using only the output distribution and sample size at the client site. We show how a subset of clients can be recruited without sacrificing model performance whilst significantly improving computation time. By applying the recruitment approach to the training of federated models for accurate patient Length of Stay prediction using data from 189 intensive care units (clients), we show how the models trained in federations made up from only recruited clients significantly outperform federated models trained with the standard procedure in terms of predictive power and training time.
Quantum Implementation of Core Operations in Classic McEliece

If large-scale quantum computers capable of running quantum algorithms are developed, it is expected that powerful quantum cryptanalysis will become possible. To establish secure quantum-resistant encryption systems, it is necessary to evaluate the quantum security of cryptographic algorithms. As a result, various quantum cryptanalysis research studies have been published, and implementation techniques that minimize the resources required for analyzing encryption have been proposed. In this paper, we present an efficient quantum circuit implementation of Classic McEliece, which is one of the candidate algorithms in the NIST Post-Quantum Cryptography Standardization Round 4. We optimize the encoding, and decoding operations of Classic McEliece on quantum circuits, with a specific focus on binary field arithmetic, linear operations, and the Berlekamp-Massey decoding quantum circuit.
Big Data Technology Application in Software Security Vulnerability Analysis

Along with the rapid development of the times, the information age has quietly come, the Internet of Things as a new era of information technology, and human life, learning, work and other aspects of deep integration together, more Internet of Things devices connected through the network is very widely distributed in many important security areas, and the software is an important enabling component of the Internet of Things devices. With the increase in the variety of software and the scope of applications continue to become larger, the requirements for its security has become increasingly high, software systems that have security vulnerabilities, in the networked environment is easy to be used by criminals, resulting in very serious consequences. The use of traditional methods for vulnerability mining and detection is not only time-consuming and labor-intensive, but also less efficient. Therefore, this paper applies the big data technology, analyzes the attack methods often used by attackers, and designs the vulnerability analysis platform and device processing module according to the Spark technology in big data technology, and increases the efficiency of security vulnerability mining by analyzing the attack characteristics and constructing a model using decision tree algorithm.
Research on Basic Data Sharing System of Education Management Based on Cloud Storage

Information construction in Colleges and universities is the key factor to realize modern education. Although the major colleges and universities gradually have network teaching, they basically fight their own battles. Now the college students have a wide range of interests and hobbies. Different teaching methods may bring great differences in knowledge acquisition. Therefore, it has become a development trend to open up the threshold of network education among colleges and universities, increase the sharing of teaching resources, and promote the information exchange and resource sharing among schools and students. Educational resources are the basis of online education. Whether there are rich online educational resources will directly affect the quality of online education. Therefore, strengthening resource construction has become an important link of online education. However, due to the lack of perfect standards for resource construction at this stage, the strength of resource sharing is not enough. At the same time, due to the inconsistency of resource development standards, management norms and structural framework, it also affects the sharing of resources, seriously restricts the efficiency and quality of network education resource construction, and has become an important problem restricting the development of network education. By renting storage space from cloud storage service providers (CSP for short), users can enjoy high-quality storage services with only a small fee. At present, according to the actual development of informatization and networking, although the existing educational network stations have realized the function of sharing educational resources to a certain extent, there are still some problems such as “Information Island”, low resource utilization rate and redundant construction. In this study, the key technologies in the education management system based on cloud storage are discussed and studied, and the data layout algorithm and the security of ciphertext re-encryption are analyzed in detail, aiming at building an open, shared, collaborative and public data service center.
The Case for the Anonymization of Offloaded Computation

Computation offloading (often to external computing resources over a network) has become a necessity for modern applications. At the same time, the proliferation of machine learning techniques has empowered malicious actors to use such techniques in order to breach the privacy of the execution process for offloaded computations. This can enable malicious actors to identify offloaded computations and infer their nature based on computation characteristics that they may have access to even if they do not have direct access to the computation code. In this paper, we first demonstrate that even non-sophisticated machine learning algorithms can accurately identify offloaded computations. We then explore the design space of anonymizing offloaded computations through the realization of a framework, called Camouflage. Camouflage features practical mechanisms to conceal characteristics related to the execution of computations, which can be used by malicious actors to identify computations and orchestrate further attacks based on identified computations. Our evaluation demonstrated that Camouflage can impede the ability of malicious actors to identify executed computations by up to 60%, while incurring modest overheads for the anonymization of computations.
DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-Clouds with Dynamic Power-Budgets

The Internet of Things (IoT) driven latency-critical applications are deployed on lightweight Micro-Clouds at the network's edge. Renting physical space from geographically distributed colocation datacenters connected via a Wide Area Network (WAN) is a cost-effective way of deploying Micro-Clouds, despite WANs' dynamic communication latency from traffic congestion. However, this deployment approach can limit Micro-Clouds to operate within a soft power budget, as colocation datacenter providers utilize it to add more servers and lower capital costs through oversubscribing power infrastructure. As a result, Micro-Clouds use extreme energy reduction measures like power capping and task throttling to address power overdraw events, where power consumption exceeds soft power budget limits, which reduces the performance of latency-critical applications. We propose a solution where a dynamic power budget can be achieved by adding renewable energy sources to the existing soft power budget without upgrading power delivery systems. To take advantage of this, we propose a dynamic, decentralized task-scheduling algorithm called DEMOTS. DEMOTS effectively utilizes the available dynamic power budget in a WAN with varying degrees of network traffic congestion, thereby avoiding the need for extreme energy reduction measures. We implement DEMOTS on a simulation test-bed. Compared to state-of-the-art baseline using MCOP for decentralized task-scheduling in Micro-Clouds, DEMOTS reduces Power Overdraw Impact up to 19%, Task Latency Increase Impact up to 47 %, and Task Schedule Time Impact up to 49%.
Extreme Learning Machine-based Genetic Algorithm for the facility location problem with distributed demands on network edges

This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assigned to its closest open facility. Finding the best combination for establishing facilities and using them as a basis for decomposing network edges form the main decision variables. Due to the NP-hardness of this problem, a Genetic Algorithm is used as the optimization method. This algorithm is known as one of the best metaheuristics for solving this problem. To accelerate the optimization process considering the computationally expensive fitness evaluation of the edge-based location problems, an extreme learning machine is hybridized with the implemented genetic algorithm to serve as a surrogate model for approximating the fitness of the majority of individuals. The results obtained from solving generated instances indicate that while keeping the same quality of solutions, the developed surrogate model-based genetic algorithm significantly reduces the required computational time.
Design and Strategy Research of UAV Dynamic Take-Off and Landing Platform Based on Inertial Stability Control Platform

In this paper, a dynamic takeoff and landing device of UAV based on inertial stability control platform is designed. The platform can effectively isolate the disturbance caused by the disturbance of carrier (vehicle, ship, etc.), and provide a stable platform for the takeoff and landing of UAV. During the take-off stage of UAV, the azimuth pointing Angle of the apron can be calculated in real time according to the first target point of UAV mission route, and the attitude adjustment mechanism can drive the UAV to ensure that the nose points to the first target point of UAV mission route in real time. In the landing stage of the UAV, the orientation Angle of the apron can be calculated in real time according to the nose orientation, so as to ensure that the orientation of the apron is consistent with the nose of the UAV during the moving process of the carrier. There is no need to adjust the nose in real time according to the rotation Angle of the carrier during the moving landing of the UAV. This method greatly reduces the difficulty of the moving landing of the UAV and saves about 10% of the electric power of the UAV. After landing on the platform, the UAV can realize automatic centering, locking, charging and other fully autonomous processes without human intervention.
Time-Dependent Pricing and Scheduling for Cloud Object Storage Service Providers

There is increasing competition among cloud object storage service (COSS) providers as demand for COSSs grows. While various pricing models exist for commercial COSS providers, they do not effectively adapt to changing client demand and resource supply. As a result, many COSS providers are still facing fundamental problems in operational strategy to maximize their profit. In this paper, we propose TD-PnS based on the Lyapunov-drift-minus-profit technique which jointly and dynamically makes decisions on (i) service pricing, (ii) CPU clock scaling and encoding scheduling, (iii) network scheduling, and (iv) energy storage management to maximize COSS provider's profits. We also propose an additional version of TD-PnS, namely TD-PnS-Adv, that adds realistic aspects such as system stabilization. Finally, through trace-driven simulation using real dataset, we demonstrate that the proposed algorithms outperform existing algorithms and pricing models in terms of profit.
Generating Wind Power Pseudo-Measurements by Machine-Learning-based Methods

Protection and control of power systems rely on a large number of measurement sensors, whose data is transmitted from remote terminal units to the energy management system. Unfortunately, data transmission streams may fail due to sensor or transmission equipment failures or cyber-attacks, compromising the system’s operation. In such scenarios, generating pseudo-measurements is crucial to maintain system awareness, but this task can be challenging due to multiple missing data and computational time constraints. To address this, the authors of this paper have developed several methods to generate pseudo-measurements, such as wind power profiles, by combining different clustering and machine learning techniques. The study includes a comprehensive analysis and discussion of the experimental results, which have identified the most promising model combinations.