Reviewerly/exHarmony huggingface dataset:
DEHM: An Improved Differential Evolution Algorithm Using Hierarchical Multistrategy in a Cybertwin 6G Network

Differential evolution (DE) algorithm can be used in edge/cloud cyberspace to find an optimal solution due to its effectiveness and robustness. With the rapid increase of the mobile traffic data and resources in a cybertwin-driven 6G network, the DE algorithm faces some problems such as premature convergence and search stagnation. To deal with the problems mentioned above, in this article, an improved DE algorithm based on hierarchical multistrategy in a cybertwin-driven 6G network (denoted by DEHM) is proposed. Based on the fitness value of the population, DEHM classifies the population into three sub-population. Regarding each sub-population, DEHM adopts different mutation strategies to achieve a tradeoff between convergence speed and population diversity. In addition, a new selection strategy is presented to ensure that the potential individual with good genes is not lost. Experimental results suggest that the DEHM algorithm surpasses other benchmark algorithms in the field of convergence speed and accuracy. The proposed DEHM is expected to be leveraged in edge/cloud cyberspace, aiming at reducing energy costs and improving resource utilization.
Evolutionary Search for Complete Neural Network Architectures With Partial Weight Sharing

Neural architecture search (NAS) provides an automatic solution in designing network architectures. Unfortunately, the direct search for complete task-dependent network architectures is laborious since training and evaluating complete neural architectures over a large search space are computationally prohibitive. Recently, one-shot NAS (OSNAS) has attracted great attention in the NAS community because it significantly speeds up the candidate architecture evaluation procedure through weight sharing. However, the full weight sharing training paradigm in OSNAS may result in strong interference across candidate architectures and mislead the architecture search. To alleviate the problem, we propose a partial weight sharing OSNAS framework that directly evolves complete neural network architectures. In particular, we suggest a novel node representation scheme that randomly activates a subset of nodes of the one-shot model in each generation to reduce the weight coupling in the one-shot model. During the evolutionary search, a tailored crossover operator randomly samples the nodes from two parent individuals or a single parent to construct new candidate architectures, thus effectively constraining the degree of weight sharing. Furthermore, we introduce a new mutation operator that replaces the chosen nodes of the one-shot model with randomly generated nodes to enhance the exploratory capability. Finally, we encode a set of pyramidal convolution operations in the search space, enabling the evolved neural networks to capture different levels of details in the images. The proposed method is examined and compared with 26 state-of-the-art algorithms on ten image classification tasks, including CIFAR series, CINIC10, ImageNet, and MedMNIST series. The experimental results demonstrate that the proposed method can computationally much more efficiently find neural architectures that achieve comparable classification accuracy to the state-of-the-art designs.
Detection of web service anti-patterns using weighted extreme learning machine

'Anti-Pattern' is a term often used by software engineers and practitioners nowadays. An anti-pattern is a supplement of the design pattern. Similar to design patterns, an anti-pattern is a template and a repeatable way of solving a specific problem, but in a non-optimal and ineffective way. Therefore, there is a requirement for the timely identification and modification of anti-patterns to increase software systems performance and efficiency. Anti-pattern detection using the source code metric can be used as an initial step in the software development life cycle, both to reduce the maintenance of the software system and to improve the quality of the software. The work in this paper empirically investigates the effectiveness of two machine learning algorithms, i.e., Extreme Learning Machine (ELM) and Weighted Extreme Learning Machine (WELM), with four different kernels in detecting web service anti-patterns. This work also investigates the application of different aggregation techniques and data sampling techniques to handle imbalanced data in predicting five different anti-patterns. The inference of this research is studied over 226 WSDL (Web Service Description Language) files. The experimental findings reveal that the model developed by WELM has superior prediction accuracy compared to the model developed by ELM.
Entanglement-Assisted Absorption Spectroscopy by Hong-Ou-Mandel Interference

Absorption spectroscopy has long been hailed as an absolute necessity for detecting the properties of complex chemical and biological samples. While conventional spectroscopy using classical light is severely limited by shot noise and lacks robustness against experimental imperfections, quantum light offers an avenue to perform absorption spectroscopy with provable advantages in the technical operations and the measurement precision. Here, we present an experimental approach to implement entanglement-based absorption spectroscopy with the assistance of Hong-Ou-Mandel interference. Since the temporal interference fringe is determined by the spectrum pattern, a maximum-likelihood decision is sufficient for revealing the relevant quantum information about the absorptive properties of the test samples. These experimental results may significantly facilitate the use of quantum interferometric spectroscopy to practical applications, which may be particularly relevant for photon-sensitive biological and chemical samples.
Action Recognition Framework in Traffic Scene for Autonomous Driving System

For the autonomous driving system, accurately recognizing the actions of different roles in the traffic scene is the prerequisite for realizing this kind of human-vehicle information interaction. In this paper, we propose a complete framework based on 3D human pose estimation to recognize the actions of different roles on the road. The main objects recognized include traffic police, cyclists, and some passersby in need. We perform action recognition based on a dynamic adaptive graph convolutional network, which can realize the action recognition of objects based on 3D human pose. In addition to the action recognition module, we have optimized both the object detection module and the human pose estimation module in the framework so that the framework can handle multiple objects at the same time, which can be closer to the real traffic scene. To realize complex and changeable human action recognition, we built a multi-view camera system to collect responsible 3D human pose datasets containing traffic police gestures, cyclist gestures, and pedestrians’ body movements. In the experiments, compared to other state-of-the-art researches, the proposed framework can achieve comparable results with the same dataset. Satisfactory performance has also been obtained on the real data we collected, which can handle a variety of different action recognition tasks at the same time.
Active Gradual Domain Adaptation: Dataset and Approach

Adapting deep neural networks to the changing environments is critical in practical utility, especially for online web applications, where the data distribution changes gradually due to the evolving environments. For instance, the web photos of cellphones change gradually over years due to appearance changes. This paper deals with such a problem via active gradual domain adaptation, where the learner continually and actively selects the most informative labels from the target to enhance labeling efficiency and utilizes both labeled and unlabeled samples to improve the model adaptation under gradual domain drift. We propose the active gradual self-training (AGST) algorithm with novel designs of active pseudolabeling and gradual semi-supervised domain adaptation. Specifically, AGST pseudolabels the samples with high confidence, and selects the most informative labels from the unconfident samples based on both uncertainty and diversity, and then gradually self-trains itself by confident pseudolabels and queried labels. To study the gradual domain shift problem in the web data and verify the proposed algorithm, we create a new dataset -- Evolving-Image-Search (EVIS), collected from the web search engine and covers a 12-years range. Since the appearance of the products evolves over these years, such dataset naturally contains gradual domain drift. We extensively evaluate AGST on the synthetic dataset, real-world dataset, and EVIS dataset. AGST achieves up to 62% accuracy improvement (absolute value) against unsupervised gradual self-training with only 5% additional labels, and 19% accuracy improvement against directly applying CLUE, demonstrating the effectiveness of the designs of active pseudolabel and gradual semi-supervised domain adaptation.
MoDANet: Multi-Task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation

In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the effect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation.
Graph Neural Network for Ethereum Fraud Detection

Currently, the blockchain technology has been widely applied to various industries, and has attracted wide attention. However, because of its unique anonymity, digital currency has become a haven for all kinds of cyber crimes. It has been reported that Ethereum frauds provide huge profits, and pose a serious threat to the financial security of the Ethereum network. To create a desired financial environment, an effective method is urgently needed to automatically detect and identify Ethereum frauds in the governance of the Ethereum system. In view of this, this paper proposes a method for detecting Ethereum frauds by mining Ethereum-based transaction records. Specifically, web crawlers are used to capture labeled fraudulent addresses, and then a transaction network is reconstructed based on the public transaction book. Then, an amount-based network embedding algorithm is proposed to extract node features for identifying fraudulent transactions. At last, the graph convolutional network model is used to classify addresses into legal addresses and fraudulent addresses. The experimental results show that the system for detecting fraudulent transactions can achieve the accuracy of 95%, which reflects the excellent performance of the system for detecting Ethereum fraudulent transactions.
Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection

Artiﬁcial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ 'enriched' datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme.A retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia's biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the diﬀerence in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading.This study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening.
Monocular Vision-Based Calibration Method for the Axial and Transverse Sensitivities of Low-Frequency Triaxial Vibration Sensors With the Elliptical Orbit Excitation

The low-frequency triaxial vibration sensors have been gradually applied in many engineering fields of vibration monitoring because they can measure the multidirection vibrations simultaneously. The accurate axial and transverse sensitivities, determined by the calibration method, are the prerequisite for ensuring their measurement accuracy. Currently, the laser interferometry (LI) which is based on a single component or a tricomponent linear shaker is usually applied to calibrate these sensitivities. However, the former has to require the multiple reinstallations of the sensor and the latter cannot avoid the motion coupling caused by the shaker, these inevitably increase the calibration uncertainty. In this article, we investigate a monocular vision (MV)-based two-component shaker calibration method, which determines the axial sensitivity based on the time-spatial synchronization and transverse sensitivity at the elliptical orbit excitation. The MV method is used to measure this excitation, and a plane sensitivity model is presented to describe these sensitivities. This investigated method can simultaneously reduce the uncertainties caused by the reinstallations and motion coupling to improve the calibration accuracy. Experimental results compared with the LI and Earth's gravitation method demonstrate that the investigated method obtains the satisfactory accuracies both in axial sensitivity magnitude and phase as well as transverse sensitivity magnitude and direction calibration.
Ranked Keyword Search over Encrypted Cloud Data Through Machine Learning Method

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RKS (called ML-RKS <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^{+}$</tex></formula> ) scheme by introducing a permutation matrix. ML-RKS <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^{+}$</tex></formula> prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.
Augmented Humanity: A Systematic Mapping Review

Augmented humanity (AH) is a term that has been mentioned in several research papers. However, these papers differ in their definitions of AH. The number of publications dealing with the topic of AH is represented by a growing number of publications that increase over time, being high impact factor scientific contributions. However, this terminology is used without being formally defined. The aim of this paper is to carry out a systematic mapping review of the different existing definitions of AH and its possible application areas. Publications from 2009 to 2020 were searched in Scopus, IEEE and ACM databases, using search terms "augmented human", "human augmentation" and "human 2.0". Of the 16,914 initially obtained publications, a final number of 133 was finally selected. The mapping results show a growing focus on works based on AH, with computer vision being the index term with the highest number of published articles. Other index terms are wearable computing, augmented reality, human-robot interaction, smart devices and mixed reality. In the different domains where AH is present, there are works in computer science, engineering, robotics, automation and control systems and telecommunications. This review demonstrates that it is necessary to formalize the definition of AH and also the areas of work with greater openness to the use of such concept. This is why the following definition is proposed: "Augmented humanity is a human-computer integration technology that proposes to improve capacity and productivity by changing or increasing the normal ranges of human function through the restoration or extension of human physical, intellectual and social capabilities".
Reinforcement learning-assisted autoscaling mechanisms for serverless computing platforms

Serverless computing is emerging as a cloud computing paradigm that provisions computing resources on demand, while billing is taking place based on the exact usage of the cloud resources. The responsibility for infrastructure management is undertaken by cloud providers, enabling developers to focus on the development of the business logic of their applications. For managing scalability, various autoscaling mechanisms have been proposed that try to optimize the provisioning of resources based on the posed workload. These mechanisms are configured and managed by the cloud provider, imposing non negligible administration overhead. A set of challenges are identified for introducing automation and optimizing the provisioning of resources, while in parallel respecting the agreed Service Level Agreement between cloud and application providers. To address these challenges, we have developed autoscaling mechanisms for serverless applications that are powered by Reinforcement Learning (RL) techniques. A set of RL environments and agents have been implemented (based on Q-learning, DynaQ+ and Deep Q-learning algorithms) for driving autoscaling mechanisms, able to autonomously manage dynamic workloads with Quality of Service (QoS) guarantees, while opting for efficient usage of resources. The produced environments and agents are evaluated in real and simulated environments, taking advantage of the Kubeless open-source serverless platform. The evaluation results validate the suitability of the proposed mechanisms to efficiently tackle scalability management for serverless applications. • AI-assisted autoscaling mechanisms for serverless applications. • Development of open-source RL environments and agents for autoscaling. • Evaluation results based on a real testbed over a serverless computing platform. • Simulation environment for boosting training of the RL agents.
The generalized sigmoid function and its connection with logical operators

In this study, we present the operator-dependent sigmoid function, which is derived from a universal unary operator called the kappa function. Here, we describe how the generalized sigmoid function is related to representable uninorms (i.e., Dombi's aggregative operator). Namely, we show that the inverse of a generalized sigmoid function is an additive generator of the aggregative operator. We provide the necessary and sufficient conditions for the form of the function that transforms the aggregative operator into a conjunctive or disjunctive logical operator. This transformation is also based on the generalized sigmoid function. Here, we show how conjunctive and disjunctive operators, which form a De Morgan system with a negation, can be derived from the aggregative operator. We point out that, under certain conditions, a set of generalized sigmoid functions is closed under the negation and modifier operators. Lastly, we demonstrate an important connection between the weighted aggregative operator and the generalized sigmoid function. Based on this connection, we provide a new interpretation of the feed-forward neural networks. We show that a perceptron-based neural network can be modeled using the aggregative operator and the generalized sigmoid function.