Reviewerly/exHarmony huggingface dataset:
Cooperative Coevolutionary CMA-ES With Landscape-Aware Grouping in Noisy Environments

Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision variables. In reality, many problems are high dimensional. Cooperative coevolutionary (CC) algorithms based on a divide-and-conquer strategy are promising in solving complex high-dimensional problems. However, noisy fitness evaluations pose a challenge in problem decomposition for CC. The state-of-the-art grouping methods, such as differential grouping (DG) and recursive DG, are unable to work properly in noisy environments. Because it is impossible to distinguish whether the change of one variable’s difference value is caused by noise or the perturbation of its interacting variables. As a result, every pair of variables will be identified as nonseparable in these methods. In this article, we study how to group decision variables with the covariance matrix adaptation evolution strategy (CMA-ES) in noisy environments and subsequently propose a landscape-aware grouping (LAG) method. Instead of detecting pairwise interacting variables, we directly identify a nonseparable subcomponent. To this end, we propose to use two convergence features: 1) variable convergence time and 2) accumulative path, to describe variables’ fitness landscapes; then, variables are clustered according to these two features. Numerical experiments show that LAG can more effectively identify interactive decision variables in the presence of multiplicative noise than the DG and some of its variants. Up to 500 dimensions, the performance of CC CMA-ES with landscape-aware grouping (CC-CMAES-LAG) is competitive compared with existing CC algorithms and uncertainty-handling CMA-ES (UH-CMA-ES).
Device Selection and Resource Allocation for Layerwise Federated Learning in Wireless Networks

In this article, we study device selection and resource allocation (DSRA) for layerwise federated learning (FL) in wireless networks. For effective learning, DSRA should be carefully determined considering the characteristics of both layerwise FL and wireless networks. To address this, we propose a DSRA algorithm for layerwise FL, called LAFLAS, that maximizes the total average number of the shallow and entire parameter transmissions over time in FL while guaranteeing the ratio between their numbers. Through experiments, we show that the model trained by our LAFLAS outperforms those by state-of-the-art algorithms, which demonstrates the effectiveness of DSRA by LAFLAS.
Communication-Efficient Distributed SGD Using Random Access for Over-the-Air Computation

In this paper, we study communication-efficient distributed stochastic gradient descent (SGD) with data sets of users distributed over a certain area and communicating through wireless channels. Since the time for one iteration in the proposed approach is independent of the number of users, it is well-suited to scalable distributed SGD. Furthermore, since the proposed approach is based on preamble-based random access, which is widely adopted for machine-type communication (MTC), it can be easily employed for training models with a large number of devices where MTC is used for their connectivity. For fading channel, we show that noncoherent combining can be used. As a result, no channel state information (CSI) estimation is required. For over-the-air computation, the proposed approach takes advantage of random access, where the access probability is to encode the norm of local gradient vector (without using any additional bits) and the preamble is to encode the quantized normalized gradient vector. From analysis and simulation results, we can confirm that the proposed approach is not only scalable, but also provides improved performance as the number of devices increases. From analysis and simulation results, we can confirm that the proposed approach is not only scalable, but also provides improved performance as the number of devices increases.
The Black-Box Simplex Architecture for Runtime Assurance of Autonomous CPS

The Simplex Architecture is a runtime assurance framework where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain the safety of an autonomous cyber-physical system. In this work, we show that runtime checks can replace the requirement to statically verify safety of the baseline controller. This is important as there are many powerful control techniques, such as model-predictive control and neural network controllers, that work well in practice but are difficult to statically verify. Since the method does not use internal information about the advanced or baseline controller, we call the approach the Black-Box Simplex Architecture. We prove the architecture is safe and present two case studies where (i) model-predictive control provides safe multi-robot coordination, and (ii) neural networks provably prevent collisions in groups of F-16 aircraft, despite the controllers occasionally outputting unsafe commands.
Design of virtual reality-based mathematics learning media on trigonometry material in senior high school

Students' understanding of the concept of trigonometry is still low. This is because students have difficulty imagining the concept of angles in trigonometry which is only limited to memorizing formulas. Thus, it is hoped that through virtual reality-based mathematics learning media, students will be very helpful to learn about trigonometry concepts. This study aims to produce a virtual reality-based mathematics learning media on trigonometry material in high school that meets the valid criteria. This type of research is development research that is applied with the ADDIE model which includes the Analysis, Design, Development, Implementation, and Evaluation stages. The research subjects involved were students of class X SMA N 1 Tahunan Jepara. The sampling technique used was purposive sampling. The results of this study are virtual reality-based mathematics learning media on trigonometric material with material expert validation results with a feasibility percentage of 91,25% and media expert validation results of 92,00% with very good criteria. Thus, this product is suitable for senior high school students as an innovative learning medium.
The temporal inference with the use of ant-based clustering algorithm and flow graphs in the problem of prognosing complications of medical surgical procedures

In the era of a rapidly aging European society, the demand for proven clinical decision support systems, links health observations with medical knowledge in order to assist clinicians in decision making is constantly growing. An increasing problem for this type of systems is not only the size of the processed data sets but also the heterogeneity of these data. Clinical forecasting often requires processing of both numerical data and multi-category data which are temporal. The conducted research has shown that a good solution to this problem may lie in the use of temporal inference, the ant-based clustering algorithm, rough sets, and fuzzy sets. The experiments used a real set of medical data representing cases of a disease that significantly reduces a woman's quality of life. Each case of uterine myoma disease (which affects more than 50% of women over the age of 35) is represented by more than 140 heterogeneous features. An incorrect decision about the type of surgery (thermoablation or surgery) not only affects female fertility but also the high risk of complications. Therefore, the solution discussed in this paper may turn out to be extremely important.
Breast Cancer Classification from Histopathological Images using Convolutional Neural Network

Breast cancer is one of the leading causes of mortality among women, and it has a severe impact on global public health. Breast cancer diagnosis in its early stages can save thousands of lives every year. The clinical diagnosis is based mostly on subjective analysis of the breast histopathological images and requires a considerable lot of expertise from the pathologists. Computer-aided diagnosis (CAD) system can be used to provide a second opinion to make the breast cancer diagnosis error-free as much as possible. BreakHis, a publicly available breast histopathological image dataset, is used in this study. In this research, a deep convolutional neural network with EfficientNet-B0 as the feature extractor is used to classify the histopathological images of the breast tissues. In terms of accuracy, precision, recall, F-measure, and Area Under Curve (AUC), this model beat prior research by a considerable margin.
Cooperative multi-swarm particle swarm optimization based on adaptive and time-varying inertia weights

Optimization of particle swarms is a stochastic optimization method based on swarm intelligence applied in many fields of endeavor to solve technical, scientific and economic problems. Due to its ease of application, it has gained great importance in recent years. As the swarm may lose its diversity and lead to premature convergence, it is very easily trapped in local optima. To solve this problem, we propose, in this research work, an cooperative multi-swarm particle swarm optimization algorithm called cooperative multi-swarm particle swarm optimization (C-MsPSO). The introduced algorithm divides the entire population into four cooperative sub-swarms with an adaptive and time-varying inertia weight. The particles of each sub-swarm share the best overall optimum to ensure the cooperation between the four sub-swarms . On the other hand, the adaptive and time-varying inertia weight is used to create search potential and effectively maintain a balance between the local research (exploitation) and the global (exploration). To show the efficiency of the developed C-MsPSO algorithm, several uni-modal and multi-modal benchmark test functions are considered. The introduced algorithm demonstrates surprising efficiency and precision in identifying the optimal solution.The experimental results reveal that C-MsPSO outperforms the other PSO algorithms on twelve reference functions.
RAEF: Energy-efficient Resource Allocation through Energy Fungibility in Serverless

Datacenters' excessive energy consumption has become an increasingly significant pain point to cloud service providers. However, existing research usually tends to focus on the server power cap to increase application throughput and do not address the rapid growth in the energy consumption of datacenters. Although the serverless architecture makes it more difficult to improve datacenter energy efficiency due to its high density and high dynamic nature, we observe that the emerging serverless workloads bring new opportunities for energy reduction. First, the energy consumption of serverless workloads during their execution time can be divided into three stages with clear boundaries: startup, runtime and idle. As the three stages have quite different energy usage patterns, we are able to reduce overall energy consumption through orchestrating the energy consumption of the stages. Second, we observe the energy fungibility phenomenon, i.e., the different combinations of multidimensional resource allocations may lead to the same latency but different energy consumption. Exploiting the balanced combination of energy consumption and performance, we can reduce the overall energy consumption without violating the latency service level agreement (SLA). Based on this, we propose RAEF, a function-level resource allocator that proactively adjusts the resources of the functions for minimizing the energy consumption with SLA guarantees. Evaluation results demonstrate that RAEF reduces energy consumption by up to 21.2 % compared to the state-of-the-art technique while guaranteeing the SLA.
Let the Games Speak by Themselves: Towards Game Features Discovery Through Data-Driven Analysis and Explainable AI

The idea behind this work is to start exploring the application of data analytics and (explainable) machine learning techniques to better understand games and discover new features that will possibly help in effectively exploiting them in different socially useful domains. We prove the feasibility of the idea by: (i) collecting a large dataset of board game information; (ii) designing and testing an information processing pipeline for automatically discovering game categories and game mechanics, with some first encouraging results. In the future, we plan to further generalize this approach for different kinds of games and for discovering currently unknown but useful aspects, e.g. games or game features that could better foster Computational Thinking in education, those better suited to be applied in social distancing contexts, and so on.
Bilinear Graph Neural Network-Enhanced Web Services Classification

With the growing number of Web services, classifying Web services accurately and efficiently has become a challenging problem. Effective service classification is conducive to improving the quality of service discovery and the efficiency of service composition. Existing graph neural network methods represent the target service node by a weighted sum of the features of the neighbor service nodes, and have achieved remarkable results in service classification. However, these methods ignore the impact of possible interactions between neighbor service nodes on the target service node. To address this problem, we propose a Web services classification method based on bilinear graph neural network. Our proposed method can model the bilinear interactions between neighbor service nodes and effectively improve the accuracy of service classification. First, our proposed model uses the Word2Vec model to extract the latent semantic vectors from service description documents. We then construct the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes, highlighting the local common attributes. Finally, the bilinear aggregator is combined with the weighted sum aggregator to construct a bilinear graph neural network, which can learn more comprehensive representations of services. The experimental results on the real dataset from ProgrammableWeb show that the classification accuracy of the proposed method is greatly improved compared with DeepWalk, Node2Vec, GCN, and GAT.
Blockchain-based decentralized federated learning: A secure and privacy-preserving system

To solve the data silos and data security dilemma faced by machine learning (ML), the concept of federated learning (FL) was first proposed, and federated learning has also attracted great attention from the information security community in recent years, where the most concerned issue is the security of data and models in federated learning. The current centralized federated learning scheme is prone to single point of failure as well as central server evil problem, so blockchain is used to replace the central server, but the blockchain network is not immune to poisoning attacks by participants on the global model and inference attacks on participants&#x0027; local data. To this end, in this paper, we propose a blockchain-based federated learning security and privacy protection framework (BFLSP), design a model storage scheme on the blockchain, and add a node scoring mechanism as well as a node election scheme to ensure the security of the model and data during the training process while guaranteeing the accuracy of the model. Finally, we design experiments to confirm the feasibility as well as the security of the framework.
Evolutionary latent space search for driving human portrait generation

This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic human portraits. The proposed research can contribute to improving the security of face recognition systems.
Enabling SQL-based training data debugging for federated learning

How can we debug a logistic regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan applications)? The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting. Given an unexpected query result over model predictions, this framework automatically removes the label errors from training data such that the unexpected behavior disappears in the retrained model. In this paper, we enable this powerful framework for federated learning. The key challenge is how to develop a security protocol for federated debugging which is proved to be secure, efficient, and accurate. Achieving this goal requires us to investigate how to seamlessly integrate the techniques from multiple fields (Databases, Machine Learning, and Cybersecurity). We first propose FedRain, which extends Rain, the state-of-the-art SQL-based training data debugging framework, to our federated learning setting. We address several technical challenges to make FedRain work and analyze its security guarantee and time complexity. The analysis results show that FedRain falls short in terms of both efficiency and security. To overcome these limitations, we redesign our security protocol and propose Frog, a novel SQL-based training data debugging framework tailored for federated learning. Our theoretical analysis shows that Frog is more secure, more accurate, and more efficient than FedRain. We conduct extensive experiments using several real-world datasets and a case study. The experimental results are consistent with our theoretical analysis and validate the effectiveness of Frog in practice.
A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning

Machine learning on graph data has gained significant interest because of its applicability to various domains ranging from product recommendations to drug discovery. While there is a rapid growth in the algorithmic community, the computer architecture community has so far focused on a subset of graph learning algorithms including Graph Convolution Network (GCN), and a few others. In this paper, we study another, more scalable, graph learning algorithm based on random walks, which operates on dynamic input graphs and has attracted less attention in the architecture community compared to GCN. We propose high-performance CPU and GPU implementations of two important graph learning tasks, that cover a broad class of applications, using random walks on continuous-time dynamic graphs: link prediction and node classification. We show that the resulting workload exhibits distinct characteristics, measured in terms of irregularity, core and memory utilization, and cache hit rates, compared to graph traversals, deep learning, and GCN. We further conduct an in-depth performance analysis focused on both algorithm and hardware to guide future software optimization and architecture exploration. The algorithm-focused study presents a rich trade-off space between algorithmic performance and runtime complexity to identify optimization opportunities. We find an optimal hyperparameter setting that strikes balance in this trade-off space. Using this setting, we also perform a detailed microarchitectural characterization to analyze hardware behavior of these applications and uncover execution bottlenecks, which include high cache misses and dependency-related stalls. The outcome of our study includes recommendations for further performance optimization, and open-source implementations for future investigation.
Constructing Aircraft Fault Knowledge Graph for Intelligent Aided Diagnosis

In order to design an intelligent diagnosis expert system which helps grass-roots maintenance engineers to quickly and accurately locate the fault unit of aircraft, constructing aircraft fault knowledge graph as knowledge base of the expert system is proposed in this paper. Fault knowledge model based on ontology which describes what entity concepts and what relationship between these concepts exist in aircraft fault knowledge graph is introduced, and the fault entity identification and relation extraction method based on deep learning is illustrated in this paper.
Unsupervised adversarial domain adaptation abnormal sound detection for machine condition monitoring under domain shift conditions

Relying on mechanical sound signals to carry out anomaly detection is a challenging task. Due to the stability of the production process of complex industrial mechanical systems, there are very few or no abnormalities, and the types of mechanical failures are also difficult to describe in detail. In addition, the sound characteristics of the machine itself will change with the change of production operating conditions, and traditional anomaly detection models are prone to misjudge normal sounds as abnormal. We recommend that the change of mechanical conditions in similar situations be regarded as a domain shift between the source domain and the target domain. For unsupervised anomaly detection under the premise of domain shift, we propose an unsupervised adversarial domain adaptation method (UADA-OCSVM) based on Adversarial Domain Adaptation and One-Class SVM. Through adversarial learning strategy, the source domain and target domain data are aligned in an unsupervised method. Meanwhile, a special loss is introduced for the feature extraction layer. Finally, the anomaly detection based only on normal data is regarded as the one class classification problem, and the anomaly detection task after feature extraction is performed by OCSVM.We applied the proposed method to the MIMII DUE dataset for verification, and compared it with the autoencoder-based anomaly detection method. Experiments show that the AUC of our method is better than the method based on the autoencoder in different mechanical types, especially on the Value data set, the average AUC is increased by 15.31%, indicating that the method we proposed is better than the method based on AE Significant improvement.
SLUASCIoT: A Secure and Lightweight User Authentication Scheme for Cloud-IoT Services

In today's fast paced world, transformation is the absolute need of the hour, IoT and Cloud Computing both serve to maximize the performance in our day-to-day operations, in which IoT generates loads of statistics, and cloud becomes a pathway for this information to reach its destination. At the moment, security and privacy stand as key issues in such circumstances which generates the requirement for a secure and efficient user authentication scheme that only allows genuine entities to acquire the data and services. In this paper, we have come up with a secure and lightweight user authentication scheme that works on a Cloud-IoT based healthcare system. SLUASCIoT has been officially demonstrated secure using AVISPA and comparisons with the related schemes ensures the efficiency of SLUASCIoT.