Reviewerly/exHarmony huggingface dataset:
Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data

Abstract The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.
How can blockchain be integrated into renewable energy? --A bibliometric-based analysis

Blockchain technology and renewable energy links can realize the fusion of energy flow and information flow. The combination of the two has a high degree of matching, and the related research is also increasing. Although experts have expanded their research on renewable energy and blockchain, there are still restrictions and gaps in the existing research. In this paper, 920 articles from 2016 to 2023 were chosen and visualized using bibliometrics and CiteSpace software to analyze the research field's development base, collaborative groups, hot topics and evolutionary trends. Based on the foregoing research, we summarized the pertinent literature in the area of "Renewable energy & Blockchain" and thoroughly analyzed the current hot issues in depth to determine the future development direction. The findings show that, first, related research in the field increased rapidly in 2016–2023, the research topics and hotspots of "Renewable energy & Blockchain" focus on energy system optimization, clean energy technologies, circular development and renewable energy trading. Second, all subjects have a specific strength and influence contribution in the field, from the micro to the macro level. Among them, China, the United States and their universities have relatively mature research experience and research ability. Third, research gaps in blockchain-based renewable energy application, technology, and policy, as well as potential trends in energy internet, energy management, energy systems, green certificate trading, and green power trading. This study provides researchers with a theoretical foundation for gaining a thorough understanding of current research efforts and future research directions in this field.
Multi‐temporal scale aggregation refinement graph convolutional network for skeleton‐based action recognition

Abstract Skeleton‐based human action recognition is gaining significant attention and finding widespread application in various fields, such as virtual reality and human‐computer interaction systems. Recent studies have highlighted the effectiveness of graph convolutional network (GCN) based methods in this task, leading to a remarkable improvement in prediction accuracy. However, most GCN‐based methods overlook the varying contributions of self, centripetal and centrifugal subsets. Besides, only a single‐scale temporal feature is adopted, and the multi‐temporal scale information is ignored. To this end, firstly, in order to differentiate the importance of different skeleton subsets, we develop a refinement graph convolution, which can adaptively learn a weight for each subset feature. Secondly, a multi‐temporal scale aggregation module is proposed to extract more discriminative temporal dynamic information. Furthermore, a multi‐temporal scale aggregation refinement graph convolutional network (MTSA‐RGCN) is proposed, and four‐stream structure is also adopted in this paper, which can comprehensively model complementary features and eventually achieves a significant performance boost. In the empirical experiments, the performance of our approach has been greatly improved on both NTU‐RGB+D 60 and NTU‐RGB+D 120 datasets, compared to other state‐of‐the‐art methods.
A Bayesian Approach to Risk-Based Autonomy, with Applications to Contact-Based Drone Inspections

Abstract Enabling higher levels of autonomy while ensuring safety requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This article presents an approach to improve this ability for a robotic system executing a series of independent tasks by using a dynamic decision network (DDN). A simulation case study of an industrial inspection drone performing contact-based inspection is used to demonstrate the capabilities of the resulting system. The case study demonstrates that the system is able to infer the presence of internal faults and the state of the environment by fusing information over time. This information is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Lastly, the case study demonstrates that evaluating past states with new information enables the system to identify and counteract previous sub-optimal actions.
A robust VVC video encryption scheme based on the dynamical chaotification model

With the considerable surge of video assets, the demand for video content security has undergone substantial growth. Since the advent of H.264/AVC and H.265/HEVC selective encryption algorithms (SEAs) have become a focus. However, there is little research based on the H.266/VVC. To fill the gap, a VVC encryption scheme is proposed in this paper. Considering that most existing SEAs are easily cracked under cryptographic attacks, a dynamical chaotification model (DCM) is designed to resist chosen-plaintext attack (CPA). Additionally, given that a majority of existing SEAs are incapable of decrypting correctly in the case of packet loss, each slice is encrypted independently to enhance robustness. Firstly, the three-dimensional (3D) DCM is designed based on the exponential feedback and the boundary function. Secondly, the dynamical parameter in the 3D-DCM is updated by the encoding parameters in the VVC encoder. Then the initial values and other model parameters of the 3D-DCM are scrambled by the hash values of the slice header (SHs). Finally, the texture, motion, and structure information of VVC videos are encrypted using the iterative output of the 3D-DCM. In particular, three exclusive elements in H.266/VVC are selected in the RSES, including matrix-based intra prediction (MIP), cross-component linear model prediction (CCLM), and multiple transform selection (MTS). Experimental results demonstrate the outstanding performance of the proposed RSES in robustness and resistance to CPA, while gratifying bit rate and real-time requirements. Therefore, it is a promising encryption scheme in cryptographic applications.
Spatiotemporal knowledge teacher–student reinforcement learning to detect liver tumors without contrast agents

Detecting Liver tumors without contrast agents (CAs) has shown great potential to advance liver cancer screening. It enables the provision of a reliable liver tumor-detecting result from non-enhanced MR images comparable to the radiologists' results from CA-enhanced MR images, thus eliminating the high risk of CAs, preventing an experience gap between radiologists and simplifying clinical workflows. In this paper, we proposed a novel spatiotemporal knowledge teacher–student reinforcement learning (SKT-RL) as a safe, speedy, and inexpensive contrast-free technology for liver tumor detection. Our SKT-RL builds a teacher–student framework to realize the exploring of explicit liver tumor knowledge from a teacher network on clear contrast-enhanced images to guide a student network to detect tumors from non-enhanced images directly. Importantly, our STK-RL enables three novelties in aspects of construction, transferring, and optimization to tumor knowledge to improve the guide effect. (1) A new spatiotemporal ternary knowledge set enables the construction of accurate knowledge that allows understanding of DRL's behavior (what to do) and reason (why to do it) behind reliable detection within each state and between their related historical states. (2) A novel pixel momentum transferring strategy enables detailed and controlled knowledge transfer ability. It transfers knowledge at a pixel level to enlarge the explorable space of transferring and control how much knowledge is transferred to prevent over-rely of the student to the teacher. (3) A phase-trend reward function designs different evaluations according to different detection phases to optimal for each phase in high-precision but also allows reward trend to constraint the evaluation to improve stability. Comprehensive experiments on a generalized liver tumor dataset with 375 patients (including hemangiomas, hepatocellular carcinoma, and normal controls) show that our novel SKT-RL attains a new state-of-the-art performance (improved precision by at least 4% when comparing the six recent advanced methods) in the task of liver tumor detection without CAs. The results proved that our SKT-DRL has greatly promoted the development and deployment of contrast-free liver tumor technology.
Optimizing jointly mining decision and resource allocation in a MEC-enabled blockchain networks

In this paper, several recently published metaheuristic algorithms are adapted to optimize the NP-hard problem of jointly mining decision and resource allocation in mobile edge computing (MEC) enabled blockchain networks under two different encoding schemes. The first scheme represents individuals in a way that incorporates the mining decisions, transmission power, and computing resources of all miners for each individual, with mining decisions determined by a binary vector whose values indicate whether miners partake in mining or not. While, the second scheme makes each individual accountable for the transmission power and computing resources of each participant miner, treating all individuals as a singular solution to the problem. Then the Nutcracker optimization algorithm and gradient-based optimizer are modified to propose two robust variants, MNOA and MGBO, respectively. We then combine MNOA and MGBO to create HNOA, which further optimizes the mining decision and resource allocation in this problem. HNOA and other variants are validated using nine instances with a range of 150 to 600 miners. HNOA is also compared to several competing optimizers to demonstrate its efficacy in terms of several performance metrics, with the results showing the superiority of the proposed algorithm.
EN-Beats: A Novel Ensemble Learning-Based Method for Multiple Resource Predictions in Cloud

Cloud computing has become an important driving force in the economy and the fundamental facility for digitization transformation. Due to its rapid development and increasing demands, accurate resource usage prediction for the cloud has been a long-term challenge. To address this challenge, this paper introduces RCorrPolicy for resource metrics selection and proposes EN-Beats, an efficient ensemble learning-based approach, for predicting multiple resource usages in the cloud. The paper presents trace-driven experiments conducted on a real-world dataset, demonstrating notable improvements in predicting multiple resource metrics. Ablation experiments conducted on existing methods for RCorrPolicy indicate that the proposed policy enhances the performance of these methods across different evaluated metrics. Furthermore, EN-Beats outperforms existing methods by achieving the lowest NRMSE (lower values indicate better performance) for CPU util rate (up to 8% lower), memory usage (up to 6% lower), and network incoming traffic (up to 3% lower). Additionally, EN-Beats attains the highest R2 score (higher values indicate better performance) for predicting CPU util rate (up to 1.39 higher), memory usage (up to 0.57 higher), and network incoming traffic (up to 0.62 higher).
A Carbon-aware Workload Dispatcher in Cloud Computing Systems

The amount of carbon emission associated with the computational energy consumption in data centers depends, in a significant way, on the schedule of the workloads. Due to the inconsistent availability of renewable energy over time, in addition to the existence of various sources of power in grid regions, the carbon intensity of data centers changes over time and location. Thus, the placement and scheduling of flexible workloads, based on the carbon intensity of power sources in data centers, can remarkably decrease the carbon emission. In this paper, we address the problem of placement and scheduling of workloads over geographically distributed data centers. We propose two algorithms that take the variability of carbon intensity of the power sources of the data centers, as well as their computational resource availability, into account when deciding about the placement and scheduling of the workloads. The first is a randomized rounding approximation algorithm that provides solutions that are guaranteed to be within a given distance from the optimal solution. The second is a sample-based algorithm that improves the solutions obtained by the randomized rounding approximation algorithm. The experimental results show that the proposed algorithms can solve the problem efficiently.
A Framework for Characterizing Very Large Cloud Workload Traces with Unsupervised Learning

The rapid expansion of cloud infrastructure services as dominating computing paradigm across diverse applications has introduced new complexities in the allocation of on-demand cloud resources. Efficiently provisioning computing resources for various job types with distinct requirements poses a fundamental challenge for cloud infrastructure providers, who are responsible for managing data centers. In this paper, we propose a new clustering framework for large-scale workload traces from the Google cloud platform. Our approach intelligently identifies groups of similar jobs by considering key factors such as the centroid of clusters (representing CPU and memory usage deviation), fluctuations in CPU and memory usage, and the time distribution updates following each clustering step. We evaluate our proposed clustering approach by conducting empirical analysis on a substantial dataset consisting of 2.4 TB workload traces from eight different Borg cells within Google's cluster infrastructure. Through this evaluation, we derived seven key observations that shed light on the characteristics of the workload traces. These observations encompass diverse job types, resource utilization patterns, the efficiency of current resource allocation strategies, and the duration of resource usage for different job types and priorities. The insights gained from these observations can provide empirical evidence and valuable guidance for both cloud providers and researchers in designing optimal resource allocation and management strategies for large-scale cloud data centers.
Block Differential Evolution

In order to solve huge-scale optimization problems, many evolutionary algorithms have been proposed. In this paper, we introduce Block Differential Evolution (BDE) algorithm. The BDE can be categorized into the class of memory-efficient optimization algorithms. The main contribution is to solve large and huge-scale problems effectively and efficiently by reducing the problem dimension by utilizing a dimension-blocking scheme. With respect to the block size times reduced memory usage, furthermore, the proposed algorithm is computationally more efficient because DE operations (i.e., mutation, crossover, and selection) are performed on much smaller vector sizes. In fact, the employed blocking approach helps us to map the higher-dimensional problems into a lower-dimensional one which is more convenient to process during the optimization steps. There is a great demanding potential for the proposed approach to be utilized in embedded systems with low computational resources as a compressed optimization algorithm. This strategy is instantiated and evaluated on some well-known benchmark problems and compared with the baseline classic DE algorithm; the reported results are promising and encouraging for conducting further investigations. To the best of our knowledge, that is the first time a variable blocking scheme has been used in any meta-heuristic algorithm. A detailed explanation of the geometrical behavior of the proposed blocking approach is provided which explains how in a higher search space, a blocking approach is meaningful and applicable to accelerate convergence rate while the memory saving is huge. The reported results in this paper show the experiments on Large-Scale Global Optimization Problems proposed in CEC-2013 benchmark suite with 1,000, 10,000, and 100,000 dimensions and clearly are evidence of the possibility of satisfying two conflicting objectives, namely, efficiency and effectiveness, simultaneously. In order to utilize CEC-2013 benchmark problems for the huge dimensions 10,000D and 100,000D, some modifications and expansions have been done. The proposed approach can be utilized in another population-based optimization algorithm (swarm or evolutionary), and it is not restricted to the DE algorithm. In this paper, DE has been used as a parent algorithm for our conducted case study.
Investigating Style Forming and Twisting by Adapting AntsOMG for Composing Second Species Counterpoint

Human creators are often nourished from explicit knowledge while developing their own styles with creation, and such knowledge often comprises a multi-layered or certain much more complex structure, in which learners are guided to establish their creative vocabulary. Taking music creation as an example, countless composers have built their own composition skills by learning Species Counterpoint, while it is often ignored in current machine-learning-based creative AI works, resulting in some music generators that may be fraught with problems in their outputs when further inspected. In this study, we adapt an existing creative framework, AntsOMG, for composing Second Species Counterpoint in order to conduct an investigation on music style forming and twisting, thereby gaining insights into the crucial mechanisms and essential components of a creation process that leverages the white-box property, potentially enabling machines to incrementally learn complex, multilayered structures from a computational perspective. Step by step, we demonstrate how the target task is accomplished by expanding the base framework and exploiting its white-box property. Our implementation is capable of producing the outcomes that exhibit the desired effect, indicating a promising line of research on investigating into the use of white-box AI methodologies in the field of computational creativity, along with the forming of machines' own styles, perhaps even aesthetics in the future, based on autonomous development of complex knowledge.
xCloudServing: Automated ML Serving Across Clouds

As machine learning (ML) models have grown in complexity, so too have the expenses they incur when deployed in the cloud. In order to reduce the costs associated with ML serving, it is necessary to optimize the choice of cloud infrastructure used. Additionally, the chosen infrastructure must be able to deliver on the latency constraints that are typically defined for cloud services. This problem is made more challenging since today's organizations often need to work with more than one cloud provider, and each provider offers its own unique set of interfaces and infrastructure choices. In this work we present xCloudServing - a novel system for consistent and automated deployment of ML inference services across multi-ple cloud providers and regions. We describe the architecture and implementation of xCloudServing, as well as the different optimization algorithms implemented internally. These include established methods from the literature, as well as Niebo - our novel algorithm for minimizing cost whilst satisfying the tail latency constraint. We present simulation results for 5 different ML models over 3 cloud providers and multiple tail latency constraints that indicate that on average, Niebo outperforms state-of-the-art algorithms by 37%. Additionally, we evaluate xCloudServing with live runs and demonstrate that it is robust to nondeterministic effects and exhibits reproducible behavior.
A DevOps Approach to the Mitigation of Security Vulnerabilities in Runtime Environments

External sources, such as libraries and packages, are often used to accelerate software development. Whereas in-house developed code is frequently revised, dependencies to external sources or already delivered software systems often remain unchanged if the functionality fulfills the requirements. This can lead to vulnerabilities in individual components of the system over time, which can compromise the security of the entire system. The issue of outdated libraries and components increases with the number of running environments and the number of individual software modules common in cloud-native environments. Current vulnerability scanners detect a variety of known vulnerabilities, but often have problems fixing them due to a lack of integration into the developer's workflow. To prevent this, we present an approach that automatically detects known security vulnerabilities in running systems and supports the developer in closing the vulnerabilities and re-delivering the hardened software with a DevOps approach, by providing a feedback loop from the operation environments towards the development artifacts of the system.
A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. To predict the performance of control parameter configurations in unseen environments, this paper crafts a general framework leveraging machine learning classification and quantitative characteristics of the problem landscape. The proposed framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, a new parameter-reduced particle swarm optimization variant is constructed using the framework to eliminate the computational cost of parameter tuning and yields competitive performance amongst other leading methodologies across 60 unseen benchmark functions.