Reviewerly/exHarmony huggingface dataset:
cudaBRKGA-CNN: An Approach for Optimizing Convolutional Neural Network Architectures

This paper proposes an approach based on the Biased Random-Key Genetic Algorithm (BRKGA) metaheuristic to optimize hyperparameters of Convolutional Neural Network (CNN) architectures. We developed a Default version for computers that only have access to Central Processing Units (CPUs) and a version for Graphics Processing Units (GPUs) called cudaBRKGA-CNN, guaranteeing better computational performance during CNN training. Additionally, we developed a decoder that searches for CNN hyperparameters and returns competitive solutions. We compared the performance of our two proposed approaches with other state-of-the-art deep learning models using two datasets from the literature. Our approaches demonstrated high performance in terms of solution quality and convergence speed. Furthermore, our cudaBRKGA-CNN proposal presented competitive results and better computational time than the other evaluated models.
Verifying Neural Networks with SMT: An Experimental Evaluation

The popularity of neural networks has grown significantly in various domains, however their use in safety-critical areas has been restricted due to reliability concerns. The AIDOaRt project, an H2020-ECSEL European initiative, aims to develop dependable neural networks for safety-critical contexts. This work investigates the application of Satisfiability Modulo Theory technologies to verify neural networks with non-linear activation functions in computer vision tasks.
Verifying the Computational Integrity of Power Grid Controls with Zero-Knowledge Proof

The control of future power grids is migrating from a centralized to a distributed/decentralized scheme to enable a massive penetration of distributed energy resources with rising dependence on communication infrastructure. A common assumption made for most existing distributed/decentralized controllers is that local controllers would faithfully follow the designated controller dynamics based on the data received from communication channels. However, with increased probability of cyberattacks on Operational Technology infrastructure, such an assumption could be risky because proper execution of the controller dynamics is then built on trust in secure communication and computation. In this work, we leverage a cryptography technology known as zero-knowledge scalable transparent arguments of knowledge (zk-STARK) to verify the computational integrity of power grid control algorithms, with projected linear dynamics-based control schemes as the initial proof-of-concept test case. The method presented here converts the cybersecurity challenge of data integrity for grid control into a subset of computational integrity.
Toward Unbounded Search Space Exploration by Particle Swarm Optimization in Multi-Modal Optimization Problem

Since we cannot guarantee that the global optimal solution is included within a “pre-determined” search space in unknown problems such as real world problems, this paper addresses the “unbounded” search space exploration in swarm optimization. For this issue, this paper proposes the following three methods that dynamically extend the search area: Dynamic Search Area Exploration based on Gradient (DSAEG), which preferentially searches the area of a high gradient of solution space, Dynamic Search Area Exploration based on Novelty (DSAE-N), which preferentially searches the area where is far from the current search area, and Dynamic Search Area Exploration based on Gradient and Novelty (DSAE-GN), which integrates DSAE-G with DSAE-N. To investigate the effectiveness of the proposed three methods, this paper evaluates them in the six multi-modal optimization problems by applying them into Niching Migratory Multi-Swarm Optimiser (NMMSO) based on Particle Swarm Optimization (PSO). The intensive comparison of the proposed methods with NMMSO have been revealed the following implications: (1) DSAE-GN quickly finds almost of the global optimal solutions regardless of the shape of the evaluation function while DSAE-G and DSAE-N cannot always find them; and (2) DSAE-GN can find almost of the global optimal solutions even if the search area is not given, while NMMSO cannot find them even if the search area which contains all global optimal solutions is given.
Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud

With the popularity of cloud computing and machine learning, it has been a trend to outsource machine learning processes (including model training and model-based inference) to cloud. By the outsourcing, other than utilizing the extensive and scalable resource offered by the cloud service provider, it will also be attractive to users if the cloud servers can manage the machine learning processes autonomously on behalf of the users. Such a feature will be especially salient when the machine learning is expected to be a long-term continuous process and the users are not always available to participate. Due to security and privacy concerns, it is also desired that the autonomous learning preserves the confidentiality of users' data and models involved. Hence, in this paper, we aim to design a scheme that enables autonomous and confidential model refining in cloud. Homomor-phic encryption and trusted execution environment technology can protect confidentiality for autonomous computation, but each of them has their limitations respectively and they are complementary to each other. Therefore, we further propose to integrate these two techniques in the design of the model refining scheme. Through implementation and experiments, we evaluate the feasibility of our proposed scheme. The results indicate that, with our proposed scheme the cloud server can autonomously re-fine an encrypted model with newly provided encrypted training data to continuously improve its accuracy. Though the efficiency is still significantly lower than the baseline scheme that refines plaintext-model with plaintext-data, we expect that it can be improved by fully utilizing the higher level of parallelism and the computational power of GPU at the cloud server.
Research on Vehicle and Pedestrian Detection Algorithm in Foggy Weather Based on Improved YOLOv5s

Aiming at the problem of the bad performance of current automatic driving perception algorithm in foggy weather, this paper proposes a vehicle and pedestrian detection algorithm in foggy weather based on improved YOLOv5s. Firstly, based on Cityscapes dataset, artificially generate foggy images of three concentrations to form Foggy Cityscapes dataset. Secondly, C3STR structure is introduced into the backbone network to better improve the extraction ability of model for global features. Thirdly, de-weighted BiFPN is introduced into the neck network, adding a jump connection from backbone directly to PAN, strengthening the feature fusion and reducing the loss of feature information. Finally, EIoU is used in the boundary box loss function, which improves the regression precision and accelerates the convergence speed. The experiment results show that the algorithm proposed in this paper achieves good results in both synthetic foggy image dataset and real foggy image dataset, and meets the real-time requirement, which has certain practicability.
Skill-Dependent Representations for Object Navigation

Object navigation tasks aim to guide an agent to reach a target object with a monocular visual RGB sensor. The agent is required to possess various abilities, such as exploring unknown areas for environmental learning and navigating to the discovered target, to accomplish the tasks. However, most existing methods implicitly establish a holistic representation of these abilities, lacking interpretability and increasing training difficulty. In skill-based approaches, different skills are often fed with the same features, necessitating further learning of more suitable features for each skill by the agent. Therefore, we propose a Skill-Dependent Representations navigation frAMe-work (SDRAM), including a skill selection strategy, and two skill modules: exploration and navigation. The exploration skill utilizes high-level object representations and target embedding, while the navigation strategy employs detailed visual representations that are widely used in the state-of-the-art (SOTA) methods. Our method outperforms the SOTA models in the AI2Thor environment with higher navigation success rate, and we demonstrate that our framework has good generalizability.
Supervised Federated Neural Architecture Search and Its Application in Power System Forecasting

This paper investigates the merits and limitations of various neural architecture search (NAS) methods and proposes a novel method of engaging two or more NAS methods and improving their individual performances by introducing a supervising agent. Empirical results show that the proposed method achieves a lower root mean square error (RMSE) in time-series forecasting tasks in power systems and a lower time of convergence compared to baseline NAS methods.
Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms

Variational quantum algorithms (VQAs) are hybrid approaches between classical and quantum computation, where a classical optimizer proposes parameter configurations for a quantum parametric circuit which is iteratively sampled. The overall performance of the algorithm depends on how the classical optimizer tunes the parameters of the quantum circuit. Several gradient-free and gradient-based approaches have been proposed in the literature to face this task. Estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is updated and sampled at each generation to optimize a cost function. EDAs have shown to be able to achieve good solutions in a reasonable computation time for different optimization problems, and thus, we believe that this algorithm can be a good option to overcome VQAs challenges such as the Barren plateaus phenomenon. In this paper, we study the use of three different EDAs, characterized by different probabilistic model complexities, to tune the parameters of two different VQAs to solver the Max Cut problem and to a VQA to simulate the behaviour of a molecule. Three EDA variants are compared to some state-of-the-art optimizers widely used for this task. Our results show statistical significant improvement of the EDA variants compared to different optimizers, and identify the VQAs characteristics that best fit to each EDA type. We also perform an analysis of the main EDAs hyper-parameters.
Building Lightweight Semantic Search Engines

Despite significant advances in methods for processing large volumes of structured and unstructured data, surprisingly little attention has been devoted to developing general practical methodologies that leverage state-of-the-art technologies to build domain-specific semantic search engines tailored to use cases where they could provide substantial benefits. This paper presents a methodology for developing these kinds of systems in a lightweight, modular, and flexible way with a particular focus on providing powerful search tools in domains where non-expert users encounter challenges in exploring the data repository at hand. Using an academic expertise finder tool as a case study, we demonstrate how this methodology allows us to leverage powerful off-the-shelf technology to enable the rapid, low-cost development of semantic search engines, while also affording developers with the necessary flexibility to embed user-centric design in their development in order to maximise uptake and application value.
Lightweight UAV Image Segmentation Model Design with Edge Feature Aggregation

Nowadays, Unmanned Aerial Vehicles (UAVs) tend to become an attractable platform for smart city applications, especially for environmental sensing tasks. UAV downwards view image segmentation can extract meaningful information, but the challenge lies in segmenting small-size objects. This paper proposed a reasonable method where edge features were aggregated to enhance the performance of segmentation results, together with a suitable re-weighting method to alleviate the imbalance of interested object class distribution. The effectiveness of the designed network architecture was proven by experimental results that around a 2–4% increase of IoU for aimed small-size classes was achieved. Additionally, the proposed model remains in a lightweight structure.
Sequential Estimation of States and Parameters of Non-Linear State Space Models Taking Account of Ensembles Not Covering True States

This paper proposes a new sequential state and parameter estimation method for nonlinear state-space mod-els, named Robust PF/SNES, taking account of ensembles not covering true states. PF/SNES is one of the most promising methods for sequential estimation of states and parameters and reportedly showed better performance than the augmented particle filter (Augmented PF) and the augmented Merging PF (Augmented MPF) that are widely used. Augmented PF and Augmented MPF are the extended versions of PF and MPF that handle parameters as states. PF/SNES sequentially estimates states and parameters by updating a probability distribution of parameters (a parameter distribution) using the Separable Natural Evolution Strategies (SNES) and an ensemble using PF at each time step. However, once an ensemble does not cover the true state, the subsequent estimation performance of PF/SNES deteriorates. In order to remedy the problem of PF/SNES, if the accuracy of the ensemble is judged to be low before SNES updates the parameter distribution at each time step, Robust PF/SNES searches for a high-accuracy ensemble by DX-NES-IC, which maximizes a likelihood with respect to an observation at the previous time step, taking account of the incomplete observation and the multimodality of the likelihood space. We compare the estimation performance of Robust PF/SNES with that of PF/SNES, Augmented PF, and Augmented MPF in terms of state MSE (Mean Squared Error) and parameter MSE on some benchmark problems with initial ensembles that do not cover the true states. The benchmark problems are the 2-state, 4-parameter estimation problem of the Van der Pol model, the 40-state, 41-parameter estimation problem of the Lorenz96 model, and the 10-state, 1-parameter estimation problems with an unobservable state or a bi-modal likelihood space. As a result, Robust PF/SNES showed the best performance on all the benchmark problems. Robust PF/SNES improved the accuracy by about 51.34 - 34760.94 times in terms of state MSE and 14.96 - 760.34 times in terms of parameter MSE, compared to PF/SNES.
Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models. All codes and data are available as an open source repository.
Quantum Annealing for Distribution System Restoration via Resilient Microgrids Formation

Microgrids (MGs) formation enables distribution networks to enhance the resilience of the system after natural disasters and faults. In this paper, a quantum computing (QC) method is devised to resolve distribution grid restorations, which establishes a promising computational platform for grid resilience applications. The new method is based upon resilient MGs formation formulated as an combinatorial optimization problem to restore critical loads after natural disasters. Our main breakthrough consists of a quantum optimization model for resilient MGs formation and restoration, as well as a quantum annealing solution to combinatorially complex problems which are difficult for classical methods to tackle. To validate the efficacy of the quantum grid restoration/MGs formation, test results obtained by D-Waveare compared with those from classical solver, Gurobi.
Deep Learning Models Compression Based on Evolutionary Algorithms and Digital Fractional Differentiation

Neural Networks (NNs) have shown excellent results in a variety of Machine Learning (ML) tasks and are now being used in a wide range of applications. Meanwhile, the size of these models has increased, with some of the most recent state-of-the-art models comprising billions of parameters. The necessity for compact and efficient NN representations has been recognized by research, which has provided specialized compression techniques for various applications. In this paper, we propose the optimization of the compression based on evolutionary algorithms and fractional differentiation. To this end, three main criteria were taken into account: filters error approximation using fractional differentiation, NNs accuracy and the Compression ratio. The results obtained on LeNet5 model demonstrated that there is no loss in terms of accuracy with 50% compression ratio in MNIST, 18% in CIFAR10, and 9% in CIFAR100. Moreover, the analysis showed that kernels of the first layer are more complex to be compressed than those of the second layer.
A Benchmark for Design Pattern Recovery Tools

Design patterns are template solutions to well-known design problems. Several approaches have been developed to recover design pattern instances from software programs. However, there is no common agreed benchmark to validate the recovered design pattern instances. This paper presents a benchmark that involves the instances of design patterns recovered from three subject systems. This benchmark has been developed by investigating the design pattern instances recovered by several recovery tools. Our benchmark can be used by design pattern recovery tools to validate their recovered design instances.
Towards Distributed Learning of PMU Data: A Federated Learning based Event Classification Approach

This paper studies distributed learning of real-world phasor measurement unit (PMU) data to enable privacy-preserving data sharing and analytics among different entities (i.e., PMU data owners). As real-world PMU data are collected/owned by different entities, they may not share their data due to privacy/security concerns. To tackle this challenge, this paper develops a federated learning based event classification approach that enables multiple entities to collaboratively train a good event classifier while keeping the PMU data decentralized and private. As each entity may have different numbers of PMUs, the inputs from different entities to the event classification model can be different. To address this challenge, a federated XGBoost model is proposed, in which the same set of event features can be constructed by each entity with different numbers of PMUs. To enhance the privacy of the federated XGBoost model, the communication during the training process is encrypted using additive homomorphic encryption scheme, which eliminates the requirement of a trusted server. Numerical experiments using the real-world PMU dataset show that the proposed federated XGBoost model achieves almost the same performance as the centralized counterparts and is robust against different data distributions of entities.
Development of Public Management Information Platform Based on Data Mining Technology

DM technology is an automatic method to solve how to extract information and knowledge that is not known in advance but is useful from a large amount of incomplete, fuzzy, noisy and random data in practical applications. Information technology that transforms data into knowledge. With the acceleration of the development of informatization, DM technology has gradually been widely used in many fields of public management, especially to provide important technical support for the realization of government informatization management. The introduction of DM technology in the field of public management is a need for government decision-making, can provide technical support for government information sharing, and is an important guarantee for government informatization. It is an important tool that can extract valuable information resources from some scattered and fuzzy data. It is of great significance to meet different decision-making needs in public management.
A Novel Deep Learning Approach for Colon and Lung Cancer Classification Using Histopathological Images

Colon and Lung cancers are two of the most common causes of mortality in adults. They may simultaneously form in organs and have a detrimental effect on human life. There is a high risk that cancer will spread to the two organs if it is not discovered in the early stages. One of the most essential elements of successful therapy is the histological diagnosis of such cancers. Deep learning algorithms have improved the speed and accuracy of time-consuming and challenging procedures, enabling researchers to examine a huge number of patients swiftly and inexpensively. By examining their histological images and applying modern deep learning, this study develops a classification framework called DeepLCCNet to discriminate between five kinds of colon and lung tissues (three malignant and two benign). More precisely, we have classified five tissue types of Lung and Colon Cancer Histopathological Images data set using our model, i.e., benign tissue of the lung, squamous cell carcinoma of the lung, adenocarcinoma of the lung, benign tissue of the colon, and adenocarcinoma of the colon. According to the results, the proposed model can detect cancer tissues with an average accuracy of 99.67% and maximum accuracy of 99.84%. Medical professionals will be able to utilize a precise, automated system for detecting and classifying various kinds of colon and lung cancers.
A Comparison of Evolutionary Algorithms for Training Variational Quantum Classifiers

Quantum machine learning is a research area that explores the interplay of ideas from quantum computing and machine learning to speed up the time it takes to train or evaluate a machine learning model. Variational quantum classifiers are among the most widely used quantum models for supervised machine learning and base their operation on learning free parameters through conventional optimization algorithms. In spite of their potential benefits, there is a design issue making quantum variational classifiers not fully operative: the existence of exponentially vanishing gradients, known as “barren plateau landscapes”. A barren plateau is a trainability problem that occurs in optimization algorithms for quantum machine learning when the problem-solving space turns flat as the algorithm is run. In that situation, the algorithm cannot find the downward slope in what appears to be a featureless landscape and there's no clear path to the optimum of the cost function. In this challenging scenario, evolutionary optimization techniques can be a viable choice in solving the problem because of their gradient independence. This paper introduces a comparative study involving different evolutionary algorithms to assess their suitability in acting as optimizer for a specific quantum machine learning method, known as variational quantum classifier.