Reviewerly/exHarmony huggingface dataset:
Lifting the Fog of Uncertainties

The advances in virtualization technologies have sparked a growing transition from virtual machine (VM)-based to container-based infrastructure for cloud computing. From the resource orchestration perspective, containers' lightweight and highly configurable nature not only enables opportunities for more optimized strategies, but also poses greater challenges due to additional uncertainties and a larger configuration parameter search space. Towards this end, we propose Drone, a resource orchestration framework that adaptively configures resource parameters to improve application performance and reduce operational cost in the presence of cloud uncertainties. Built on Contextual Bandit techniques, Drone is able to achieve a balance between performance and resource cost on public clouds, and optimize performance on private clouds where a hard resource constraint is present. We show that our algorithms can achieve sub-linear growth in cumulative regret, a theoretically sound convergence guarantee, and our extensive experiments show that Drone achieves an up to 45% performance improvement and a 20% resource footprint reduction across batch processing jobs and microservice workloads.
High-Capacity Imperceptible Data Hiding Using Permutation-Based Embedding Process for IoT Security

The internet of things (IoT) has become a popular technology in communication which utilizes the concept of connecting things together and exchanges information through various networks. Since data can be transferred through a wide range of channels, IoT systems suffer from potential data leakages. One of the common ways to reduce such risks is to engage steganography with secret information during transmission. A novel scheme proposed in this paper exploits simple pixel permutations to embed secret data. Instead of handling pixel blocks, the proposed scheme maneuvers on pixels directly. The proposed scheme simply manipulates the sequenced pixels using two coefficients, a threshold of range for pixel values, and a specified clustering count to fulfill the two major requirements of effective data hiding. The experimental results indicate that the proposed scheme provides a satisfactory embedding capacity and preserves a high level of image visual quality. The overall performance of the proposed scheme demonstrates its high potential in IoT security.
RepVGG-SimAM: An Efficient Bad Image Classification Method Based on RepVGG with Simple Parameter-Free Attention Module

With the rapid development of Internet technology, the number of global Internet users is rapidly increasing, and the scale of the Internet is also expanding. The huge Internet system has accelerated the spread of bad information, including bad images. Bad images reflect the vulgar culture of the Internet. They will not only pollute the Internet environment and impact the core culture of society but also endanger the physical and mental health of young people. In addition, some criminals use bad images to induce users to download software containing computer viruses, which also greatly endanger the security of cyberspace. Cyberspace governance faces enormous challenges. Most existing methods for classifying bad images face problems such as low classification accuracy and long inference times, and these limitations are not conducive to effectively curbing the spread of bad images and reducing their harm. To address this issue, this paper proposes a classification method (RepVGG-SimAM) based on RepVGG and a simple parameter-free attention mechanism (SimAM). This method uses RepVGG as the backbone network and embeds the SimAM attention mechanism in the network so that the neural network can obtain more effective information and suppress useless information. We used pornographic images publicly disclosed by data scientist Alexander Kim and violent images collected from the internet to construct the dataset for our experiment. The experimental results prove that the classification accuracy of the method proposed in this paper can reach 94.5% for bad images, that the false positive rate of bad images is only 4.3%, and that the inference speed is doubled compared with the ResNet101 network. Our proposed method can effectively identify bad images and provide efficient and powerful support for cyberspace governance.
U2-Net: A Very-Deep Convolutional Neural Network for Detecting Distracted Drivers

In recent years, the number of deaths and injuries resulting from traffic accidents has been increasing dramatically all over the world due to distracted drivers. Thus, a key element in developing intelligent vehicles and safe roads is monitoring driver behaviors. In this paper, we modify and extend the U-net convolutional neural network so that it provides deep layers to represent image features and yields more precise classification results. It is the basis of a very deep convolution neural network, called U2-net, to detect distracted drivers. The U2-net model has two paths (contracting and expanding) in addition to a fully-connected dense layer. The contracting path is used to extract the context around the objects to provide better object representation while the symmetric expanding path enables precise localization. The motivation behind this model is that it provides precise object features to provide a better object representation and classification. We used two public datasets: MI-AUC and State Farm, to evaluate the U2 model in detecting distracted driving. The accuracy of U2-net on MI-AUC and State Farm is 98.34 % and 99.64%, respectively. These evaluation results show higher accuracy than achieved by many other state-of-the-art methods.
Robust expected improvement for Bayesian optimization

AbstractBayesian Optimization (BO) links Gaussian Process (GP) surrogates with sequential design toward optimizing expensive-to-evaluate black-box functions. Example design heuristics, or so-called acquisition functions, like expected improvement (EI), balance exploration and exploitation to furnish global solutions under stringent evaluation budgets. However, they fall short when solving for robust optima, meaning a preference for solutions in a wider domain of attraction. Robust solutions are useful when inputs are imprecisely specified, or where a series of solutions is desired. A common mathematical programming technique in such settings involves an adversarial objective, biasing a local solver away from “sharp” troughs. Here we propose a surrogate modeling and active learning technique called robust expected improvement (REI) that ports adversarial methodology into the BO/GP framework. After describing the methods, we illustrate and draw comparisons to several competitors on benchmark synthetic exercises and real problems of varying complexity.Keywords: Robust OptimizationGaussian ProcessActive LearningSequential DesignDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
A Population-Based Performance Evaluation of the ShakeAlert Earthquake Early Warning System for <i>M</i> 9 Megathrust Earthquakes in the Pacific Northwest, U.S.A.

ABSTRACT We evaluate the potential performance of the ShakeAlert earthquake early warning system for M 9 megathrust earthquakes in the Pacific Northwest (PNW) using synthetic seismograms from 30 simulated M 9 earthquake scenarios on the Cascadia subduction zone. The timeliness and accuracy of source estimates and effectiveness of ShakeAlert alert contours are evaluated with a station-based alert classification scheme using an alert threshold equal to the target threshold. We develop a population-based alert classification method by aligning a population grid with Voronoi diagrams computed from the station locations for each scenario. Using raster statistics, we estimate the PNW population that would receive timely accurate alerts during an offshore M 9 earthquake. We also examine the range of expected warning times with respect to the spatial distribution of the population. Results show that most of the population in our evaluation region could receive alerts with positive warning times for an alert threshold of modified Mercalli intensity (MMI) III, but that late and missed alerts increase because the alert threshold is increased. An average of just under 60% of the population would be alerted for MMI V prior to the arrival of threshold level shaking. Large regions of late and missed alerts for thresholds MMI IV and V are caused by delays in alert updates, inaccurate FinDer source estimates, and undersized alert contours due to magnitude underestimation. We also investigate an alerting strategy where ShakeAlert sends out an alert to the entire evaluation region when the system detects at least an M 8 earthquake along the coast. Because large magnitude offshore earthquakes are rare in Cascadia, overalerting is most likely to occur from an overestimated M 7+ on the Gorda plate. With appropriate criteria to minimize overalerting, this strategy may eliminate all missed and late alerts except at sites close to the epicenter.
Research on real-time perception method of key targets in the comprehensive excavation working face of coal mine

Abstract Aiming at the real-time perception problem of key target objects caused by harsh environmental factors of high dust, low illumination, motion blur, and multi-target mixing in the comprehensive excavation working face of coal mine, a multi-target detection and tracking algorithm based on DDEB-YOLOv5s and StrongSORT is proposed. First, the YOLOv5s model is improved by using C3-Dense module, decoupled head, ECIoU loss function, and weighted bi-directional feature pyramid network to enhance the detection performance of the model in complex backgrounds of coal mine and complete the design of the DDEB-YOLOv5s multi-target detection network. Second, the DDEB-YOLOv5s algorithm is used as a detector and combined with the StrongSORT tracking algorithm to track critical equipment and miners in the complex context of coal mine. Experiments were conducted on the dataset of comprehensive excavation working face, and the experimental results show that the proposed DDEB-YOLOv5s has the best integrated detection performance compared with other YOLO series target detection algorithms, and its mean value of detection accuracy reaches 91.7%, which is 4.9% higher than that of the original YOLOv5s model. In addition, compared to the three tracking models, (YOLOv7-tiny)-(BoT-SORT), YOLOv5s-DeepSORT, and YOLOv8s-Bytetrack, the (DDEB-YOLOv5s)-StrongSORT model has the best tracking performance (with a mean tracking accuracy of 94.2%) and the least number of identifier switches. Finally, the real-time perception method proposed in this study for the key target of the coal mine working face can provide new technical support and effective guarantee for coal mine safety production.
Unsupervised learning of optical flow in a multi-frame dynamic environment using temporal dynamic modeling

Abstract For visual estimation of optical flow, which is crucial for various vision analyses, unsupervised learning by view synthesis has emerged as a promising alternative to supervised methods because the ground-truth flow is not readily available in many cases. However, unsupervised learning is likely to be unstable when pixel tracking is lost via occlusion and motion blur, or pixel correspondence is impaired by variations in image content and spatial structure over time. Recognizing that dynamic occlusions and object variations usually exhibit a smooth temporal transition in natural settings, we shifted our focus to model unsupervised learning optical flow from multi-frame sequences of such dynamic scenes. Specifically, we simulated various dynamic scenarios and occlusion phenomena based on Markov property, allowing the model to extract motion laws and thus gain performance in dynamic and occluded areas, which diverges from existing methods without considering temporal dynamics. In addition, we introduced a temporal dynamic model based on a well-designed spatial-temporal dual recurrent block, resulting in a lightweight model structure with fast inference speed. Assuming the temporal smoothness of optical flow, we used the prior motions of adjacent frames to supervise the occluded regions more reliably. Experiments on several optical flow benchmarks demonstrated the effectiveness of our method, as the performance is comparable to several state-of-the-art methods with advantages in memory and computational overhead.
Development of Academic Programs in the Digital Age: Practice from China

One of the main purposes of higher education is to prepare students to adapt to the needs of a changing society. On the basis of discussing the function of academic programs and the interdisciplinary revolution in the digital age, this chapter summarizes the Chinese higher education system from three main dimensions: the catalog of disciplines, the framework of "four new" disciplines and the action plan of vocational education. In response to the new requirements of academic curriculum reform in the digital age, this paper describes the concrete measures taken by China's academic program reform to cultivate compound talents and strengthen the collaboration of universities. This paper expounds the future trends in the development of higher education programs, such as blended teaching, interdisciplinary talent training, diversification, information literacy training, university cooperation, new systems and new methods of project evaluation, and puts forward operational strategies for reconstructing academic programs.
The Need to Develop Inclusive Education in China: a Literature Review

Purpose: The discourse around inclusive education has garnered significant attention within the global education industry. The educational landscape for children with disabilities has undergone a transformation, transitioning from the practise of segregating them in inclusive education to embracing their inclusion within general education schools and classrooms .By reviewing the development of inclusive education as well as the development of China's education system and inclusive education, this study finds the problems of inclusive education in China and the necessity of developing inclusive education in China. Theoretical framework: Inclusive education is designed in a number of areas such as the improvement of the educational system, educational management, teacher performance, and teacher identity. Therefore, this study will also be related to human capital theory, identity theory, and SLR theory. Design/Methodology/Approach: The research methodology was divided into three stages.The first step was to collect and review existing literature and studies. Secondly. The literature was categorised and reviewed separately. The third step is to analyse and draw conclusions. This stage includes a detailed description of the research questions and the conclusions drawn. Findings: Inclusive education is a global concern, as it is in China. Although China's education system has made significant progress in recent decades, the development of inclusive education remains imperfect, and the Chinese government still needs to formulate policies to promote inclusive education. Research, practical &amp; social implications: This study focuses entirely on analyzing the development of inclusive education in China while filling a gap in the systematic review of inclusive education in China. As China's education system continues to improve, it is crucial to study the development of inclusive education in China. Addressing this issue will help China build a more complete education system. Originality/Value: The results of this study are expected to serve as a reference for Chinese scholars studying inclusive education. In response to the results of this paper, the follow-up effects include further research based on this. In particular, the issue of educational management of inclusive education is also an area where this study may have an impact. This study is also expected to become formalised discussion material for the field of university, and in particular, educational management research.
Metaverse-infused academic libraries: a glimpse into the future

Purpose This study aims to explore the transformative potential of integrating artificial intelligence (AI) and the metaverse into academic libraries, envisioning a future where personalized, immersive and accessible user experiences redefine the traditional concept of libraries. The purpose is to analyse the synergy between AI and the metaverse in enhancing library services and user interactions. Design/methodology/approach The study uses a literature review approach to synthesize current knowledge on AI, the metaverse and their integration into academic libraries. It delves into the functionalities of AI in cataloguing, personalization and predictive analysis, coupled with the immersive experiences offered by the metaverse. The design envisions metaverse-infused academic libraries as digital spaces hosting AI-driven virtual assistants, personalized learning paths and collaborative environments. Findings The integration of AI and the metaverse in academic libraries presents opportunities for personalized learning experiences, efficient resource management and global collaboration. The findings suggest that the synergy enhances accessibility, inclusivity and efficiency in library services, albeit with challenges such as the digital divide, privacy concerns and technical complexities. Originality/value This study contributes to the discourse on the future of academic libraries by proposing a comprehensive vision of metaverse-infused libraries empowered by AI. It underscores the originality and value of the integration, emphasizing the potential for personalized, interactive and globally accessible learning and research environments.
Billiard based optimization with deep learning driven anomaly detection in internet of things assisted sustainable smart cities

Internet of Things (IoT) technology involves a network of interconnected devices and sensors that gather and exchange information. In smart cities, IoT devices were utilized in several fields including energy, transportation, waste management, healthcare, etc., to improve the overall quality of life and sustainability of the populace. But, as the usage of IoT increases, the cybersecurity and data privacy become a concern of safety. An anomaly detection system helps to identify possible data breaches or cyber-attacks by identifying abnormal data patterns. Deep learning (DL) driven anomaly detection has emerged as an effective and powerful method for identifying abnormal behaviours or patterns in the data domain. This technique leverages the abilities of a deep neural network for automated learning of complex patterns and representations from data, which make it better for anomaly detection task where irregularities cannot be easily defined by handcrafted rules. This paper establishes a new Billiard Based Optimization with Deep Learning Driven Anomaly Detection and Classification (BBODL-ADC) technique in IoT-assisted Sustainable Smart Cities. The goal of the BBODL-ADC technique lies in the proper recognition and classification of anomalies in the IoT-assisted smart city. To obtain that, the BBODL-ADC system applies a binary pigeon optimization algorithm (BPEO) algorithm for the effectual selection of features. Besides, the BBODL-ADC technique utilizes Elman recurrent neural network (ERNN) approach for the recognition and classification of anomalies. Moreover, the BBO system can be used for better parameters chosen by the ERNN algorithm. The stimulation value of the BBODL-ADC algorithm can be executed benchmark database. The achieved outcomes demonstrate the remarkable outcome of the BBODL-ADC methodology of 95.69% and 99.21% compared to existing models under dataset-1 and dataset-2.
Transformer With Linear-Window Attention for Feature Matching

A transformer can capture long-term dependencies through an attention mechanism, and hence, can be applied to various vision tasks. However, its secondary computational complexity is a major obstacle in vision tasks that require accurate predictions. To address this limitation, this study introduces linear-window attention (LWA), a new attention model for a vision transformer. The transformer computes self-attention that is restricted to nonoverlapping local windows and represented as a linear dot product of kernel feature mappings. Furthermore, the computational complexity of each window is reduced to linear from quadratic using the constraint property of matrix products. In addition, we applied the LWA to feature matching to construct a coarse-to-fine-level detector-free feature matching method, called transformer with linear-window attention for feature matching TRLWAM. At the coarse level, we extracted the dense pixel-level matches, and at the fine level, we obtained the final matching results via multi-head multilayer perceptron refinement. We demonstrated the effectiveness of LWA through Replace experiments. The results showed that the TRLWAM could extract dense matches from low-texture or repetitive pattern regions in indoor environments, and exhibited excellent results with a low computational cost for MegaDepth and HPatches datasets. We believe the proposed LWA can provide new conceptions for transformer applications in visual tasks.
Unveiling the Positive and Negative Effects of Blockchain Technologies on Environmental Sustainability in Practice

Blockchain is defined as a technology that allows users belonging to the same community to validate, store, and synchronize the contents of one or more transactions within a distributed ledger. Besides its primary contribution to enhance bitcoin diffusion, the technology is suitable to meet in non-financial applications, of both the public and private sectors. Blockchain technology appears to be an innovative and successful solution able to revolutionize the production systems and the execution of logistics activities in the agri-food sector, in the construction and energy sectors. Throughout a number of empirical applications of blockchain technologies in non-financial sectors, Chap. 4 provides an overview of both positive and negative effects of this Industry 4.0 technology on environmental related SDGs.
Measurements of Class Design Quality

In the past two decades, Object Oriented (OO) programming has become a foundational component of CS2 courses in university software development programs. Though these courses teach OO design philosophies and techniques, there is not a generally accepted standard as to what constitutes good OO design. In contrast, we have excellent metrics for algorithm design and efficiency, modularization design, and overall code quality. The purpose of this paper is to identify the attributes and metrics of class design quality (as opposed to function or inheritance design quality) for the purpose of helping practitioners, educators, and students become better designers of classes.