Reviewerly/exHarmony huggingface dataset:
DGSCR: Double-Target Gesture Separation and Classification Recognition Based on Deep Learning and Millimeter-Wave Radar

In the field of human–computer interaction, millimeter-wave radar has attracted considerable attention as a contactless and private approach to hand gesture recognition. However, single-target gesture recognition scenes are generally simplistic and not representative of real human–computer interactions. Therefore, this research examines the feasibility of using a single millimeter-wave radar to recognize hand gestures in double-target scenes by combining radar theory with deep learning. First, a dynamic range angle image (DRAI) of the double-target gesture is composed using the weights and DRAIs of two single-target gestures. Thus, a gesture separation network is studied to separate double-target gestures into two single-target gestures. Then, a convolutional neural network and long short-term memory (CNN + LSTM) model is applied to classification recognition. Finally, experiments are conducted to show that the proposed double-target gesture separation and classification recognition (DGSCR) system has a high recognition accuracy for gestures in new environments and positions. The viability of this method is verified using a public dataset. The CNN + LSTM model is validated using test set, which shows that the maximum accuracy across different positions is 99%. Moreover, the average accuracy after separation across the different environments for two targets in staggered arrangements is 93%. Furthermore, when facing unknown gestures in all samples with double targets, the gesture separation network also has good adaptability, with an average accuracy of 81.6%.
MRSA-Net: Anomaly Detection of Metro Stations Based on Residual Attention Network With Path Aggregation and Efficient Representation

Anomalous objects in the gaps between train doors and platform screen doors may threaten passenger safety or even cause serious accidents, but anomaly detection in this risk space is rarely explored. Anomaly detection aims to simultaneously predict the category and localization of anomalies. In this article, we propose a novel metro risk space anomaly detection network (MRSA-Net) consisting of three parts: residual attention module, involution-based path aggregation module, and generalized focal loss (GFocal) head module. The residual attention module is an improved attention mechanism that combines transformer attention with only key content factors and modulated deformable convolution. It significantly reduces floating-point operations (FLOPs) by sharing the computational burden between these two components. The involution-based path aggregation module is designed to address the issue of visual element loss in different spatial positions during information propagation. The GFocal head module includes two representations: the classification–intersection-over-union (IoU) joint representation and the general distribution representation. The former is introduced to address the issue of inconsistent usage of classification scores and IoU scores during training and inference, while the latter is introduced to handle anomalous objects with blurry manual labels caused by insufficient lighting conditions. We conduct extensive experiments on the metro risk space anomaly (MRSA) dataset and the large-scale COCO dataset, and the average precision (AP) can reach 86.8% and 43.8%, respectively, which outperforms previous methods.
Soft and resilient: When embroidery encounters narrating the Nanjing Massacre in the Memorial Hall

ABSTRACTThe paper engages directly with ‘Xuefang Liang’s Embroidery Art Exhibition’ held at the Memorial Hall of the Victims in Nanjing Massacre by Japanese Invaders as a departure point for investigating the narrative presentation of the Nanjing Massacre through artistic practice. The exhibition uses embroidery images to tell the stories of foreigners who established the Nanking Safety Zone to protect and help refugees during the Nanjing Massacre. The materiality and cultural significance of the embroidery art is used to enrich and better interpret the meaning-making space of the trauma narrative. Meanwhile, embroidery exhibits, as the material expression of the human factors in intangible cultural heritage, are critical to generating emotions from intimate contact between objects and people. Further, the visitors’ prior sensory memories facilitate the embodiment of emotions, which in turn provides ways to strengthen the visitors’ ability to derive meanings from museum engagement.KEYWORDS: Embroiderythe Nanjing Massacretraumatic narrativesensory memories AcknowledgementsThanks to Yi Wu from The Memorial Hall of the Victims in Nanjing Massacre by Japanese Invaders for grateful help. I also thank sincerely anonymous reviewers and editors for their comments and feedback.Disclosure statementNo potential conflict of interest was reported by the authors.Notes1. http://www.19371213.com.cn/en/about/museum/202007/t20200710_2236058.html2. https://mp.weixin.qq.com/s/U5Zqr4tlD1ZNqkrN5Hpbkg Accessed 14 May 2020.3. https://mp.weixin.qq.com/s/olmfpNrEPT1Ql8PmB7QH_w Accessed 16 May 2020.4. https://mp.weixin.qq.com/s/uoUo8Loa-L_nLjlEWU1q9Q Accessed 17 May 2020.5. https://m.weibo.cn/status/4744422443717663?sourceType=weixin&from=10C8295010&wm=9006_2001&featurecode=newtitleAdditional informationNotes on contributorsHongfang SunHongfang Sun (2467571036@qq.com) is a PhD candidate at Department of Archaeology, Cultural Heritage and Museology, Zhejiang University. She is interested in difficult heritage and museums, exploring the relationship between memory and emotion in visitors’ engagement.Jianqiang YanJianqiang Yan (yanjq9911@163.com) is a professor at Department of Archaeology, Cultural Heritage and Museology, Zhejiang University. He researches in Museology.
Nonparametric Bayesian modeling on infinite mixture Student <i>t</i> copulas

AbstractThis article introduces an infinite mixture Student t copula model using a nonparametric Bayesian approach. We establish a corresponding Markov chain Monte Carlo sampler for this model. In contrast to the normal mixture model, our proposed model is more suitable for data exhibiting tail dependence, which is frequently encountered in financial risk management. We evaluated the proposed algorithm through theoretical simulations and real data analysis. Parameter estimation results from the simulations demonstrate that our approach is competitive when compared to the standard maximum likelihood estimation method. The analysis of real financial data supports the validity of our approach and highlights the importance of applying a t copula in the presence of heavy tails.Keywords: CopulaDependence modelingInfinite mixture modelMarkov chain Monte CarloNonparametric Bayesian Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementPlease contact the corresponding author should you require access to the dataset or R code associated with the proposed algorithm. The author will be happy to provide these materials upon request.
Unsupervised Interpolation Recovery Method for Spectrum Anomaly Detection and Localization

With the growing efficiency of the use of unlicensed spectrum, the challenge of ensuring spectrum security has become increasingly daunting. Spectrum managers aim to accurately and efficiently detect and recognize anomaly behaviors in the spectrum. In this study, we propose a novel framework for spectrum anomaly detection and localization by spectrum interpolation recovery. Spectrum interpolation recovery refers to the recovery of the rest of the spectrum distribution based on a part of the spectrum distribution, which is achieved through a masked autoencoder (MAE) model with a core of multi-head self-attention (MHSA) mechanism. The spectrum interpolation recovery method restores the region where the masked abnormal signals are present, yielding anomaly-free results, with the difference between the restored and the masked representing the anomaly signals. The proposed method has been demonstrated to effectively reduce model-induced over-recovery of anomalous signals and dilute large-scale generation errors caused by anomalies, thereby improving the detection and localization performance of anomaly signals, and improving the area under the receiver operating characteristic curve (AUC) and the area under the precision–recall curve (AUPRC) by 0.0382 (3.68%) and 0.1992 (68.90%), respectively. On a designed dataset containing 3 variables of interference-to-signal ratio (ISR), signal-to-noise ratio (SNR), and anomaly type, the total recall of anomaly detection and localization at a 5% false alarm rate reached 0.8799 and 0.5536, respectively. Furthermore, a comparative study among different methods demonstrates the effectiveness and rationality of the proposed method.
Markov Decision Process Framework for Control-Based Reinforcement Learning

For many years, reinforcement learning (RL) has proven to be very successful in solving a wide variety of learning and decision making under uncertainty (DMuU) problems, including those related to game playing and robotic control. Many different RL approaches, with varying levels of success, have been developed to address these problems.
Entanglement Management through Swapping over Quantum Internets

Quantum Internet has the potential to support a wide range of applications in quantum communication and quantum computing by generating, distributing, and processing quantum information. Generating a long-distance quantum entanglement is one of the most essential functions of a quantum Internet to facilitate these applications. However, entanglement is a probabilistic process, and its success rate drops significantly as distance increases. Entanglement swapping is an efficient technique used to address this challenge. How to efficiently manage the entanglement through swapping is a fundamental yet challenging problem. In this paper, we will consider two swapping methods: (1) BSM: a classic entanglement-swapping method based on Bell State measurements that fuse two successful quantum links, (2) nfusion: a more general and efficient swapping method based on Greenberger-Horne-Zeilinger measurements, capable of fusing n successful quantum links. Our goal is to maximize the entanglement rate for multiple quantum-user pairs over the quantum Internet with an arbitrary topology. We propose efficient entanglement management algorithms that utilized the unique properties of BSM and n-fusion. Evaluation results highlight that our approach outperforms existing routing protocols.
Quantum Switch Scheduling for Information Qubits

This paper studies the problem of designing a scheduling policy for a quantum switch that teleports information qubits. The problem is analogous to scheduling photons in highspeed optical switches, with the difference that an optical switch is modeled as a bipartite graph instead of a complete graph. The paper's contributions are to model the problem of designing a scheduling policy as decomposing a symmetric doubly stochastic matrix, and to show that we can use Birkhoff's algorithm to obtain such a decomposition.
Quantum Best Arm Identification

Recent progress on building quantum computers [1] envisages wide applications of quantum algorithms in the near future. With the advantage of quantum computer, one can speed up not only fundamental algorithms, e.g., unstructured search [6] and factoring [11], but recent machine learning algorithms [3] as well. In this paper, we study the quantum speedup on a canonical task of reinforcement learning-best arm identification in multi-armed bandits.
Noise estimation in an entanglement distillation protocol

Estimating noise processes is an essential step for practical quantum information processing. Standard estimation tools require consuming valuable quantum resources. Here we ask the question of whether the noise affecting entangled states can be learned solely from the measurement statistics obtained during a distillation protocol. As a first step, we consider states of the Werner form and find that the Werner parameter can be estimated efficiently from the measurement statistics of an idealized distillation protocol. Our proposed estimation method can find application in scenarios where distillation is an unavoidable step.
Towards Future-Based Explanations for Deep RL Network Controllers

Lack of explainability is hindering the practical adoption of high-performance Deep Reinforcement Learning (DRL) controllers. Prior work focused on explaining the controller by identifying salient features of the controller's input. However, these feature-based methods focus solely on inputs and do not fully explain the controller's policy. In this paper, we put forward future-based explainers as an essential tool for providing insights into the controller's decision-making process and, thereby, facilitating the practical deployment of DRL controllers. We highlight two applications of futurebased explainers in the networking domain: online safety assurance and guided controller design. Finally, we provide a roadmap for the practical development and deployment of future-based explainers for DRL network controllers.
Performance evaluation teaching in the age of cloud computing

Cloud computing has been one of the most significant developments in computer science of the last two decades, fostering sharp changes in performance engineering practices across the computing industry and, at the same time, profoundly steering research trends in academia. A distinctive trait of this paradigm is that cloud engineers can programmatically control application performance, raising an expectation for computing graduates who find employment in software and system development to have basic performance engineering skills. This, in my view, calls for a broader and deeper education on software and system performance topics as part of the computing curriculum, while at the same time requiring a rethink of the syllabus of a classic performance evaluation module. This abstract presents my personal experience in doing so, including a discussion on the educational strengths and weaknesses of performance engineering emerging from cloud computing practice.
Tackling Deployability Challenges in ML-Powered Networks

Following the success of Machine Learning (ML) in various fields such as natural language processing, computer vision and computational biology, there has been a growing interest in incorporating ML into the networking domain [5, 6, 14, 4, 9]. Today, ML-based algorithms for prominent networking problems such as congestion control, resource management and routing, perform very well when their training environment is faithful to the operational environment, achieving state-of-the-art results when compared to traditional algorithms. However, the adaptation of these algorithms to function in production environments has not been straightforward, as real-world networks may differ greatly from the data used for training, leading to a drop in performance when unleashed into the wild.
GHZ distillation protocols in the presence of decoherence

In this paper, we introduce a novel heuristic approach designed to optimize the performance of Greenberger-Horne- Zeilinger (GHZ) creation and distillation protocols under decoherence. Our methodology converts these protocols into a practical set of instructions, demonstrating, through simulations, the production of higher-quality GHZ states than previously known protocols. This advancement contributes to the field of distributed quantum computing by addressing the need for high-quality entanglement required for operations between different quantum computers.
THE APPLICATION OF BLOCKCHAIN TECHNOLOGY IN PROJECT MANAGEMENT

Blockchain plays a significant role in contemporary industries. This paper aims to explain the nature of blockchain technology and its major features and application in project management. The qualitative research methodology is applied to discuss the concept of smart contracts and explain the way they are implemented in project management as well as the advantages they offer to retain employees and meet the interest of all stakeholders involved in the project. The study contains a literature review on the major benefits of blockchain applications in project management. As a result of the literature review, the paper reveals that blockchain offers a wide range of opportunities for project managers as it enables improved control of resources and finances, reduction of expenses, easier reporting, standardized terms of work, and retention of employees. It was also found that such blockchain features as distribution, connectedness, confirmation, and verifiability result in independence and security of work in project management.
Research on The Construction of Ontology-based Music Works Knowledge Base

The article formally explores the construction and reasoning of music domain ontology based on ontological methodology, proposes an improved ontology construction method, defines an ontology knowledge representation model, builds up an ontology of music works and its inference rules, achieves ontology-driven knowledge representation, storage, query and reasoning of music works, and makes some quest research for the construction and application of domain ontology.
Anomaly Detection Using LSTM-Autoencoder in Smart Grid: A Federated Learning Approach

ABSTRACT. Anomaly detection is critical in industrial systems such as smart grid systems to guarantee their safe and effective operation. The smart grid stations contain sensitive data, and they are concerned about sharing it with a third-party server to establish a centralized anomaly detection system. Federated Learning (FL) is a feasible solution to these problems for enhancing anomaly detection in smart grid systems. This study describes a method for developing an unsupervised anomaly detection based on FL system using a synthetic dataset based on real-world grid system behavior. The paper investigates the usage of FL's long short-term memory autoencoder (LSTM-AE) for anomaly detection. For more accurate identification, this research explores the performance of integrating LSTM-AE with one-class support vector machine (OC-SVM) and isolation forest (IF) and compares their results with a threshold-based anomaly detection approach. Moreover, an approach is described for generating synthetic anomalies with different levels of difficulty to evaluate the robustness of the anomaly detection FL model. The FL models results are compared with the centralized version of the models as a baseline and the results show that FL models outperformed the centralized approach by detecting higher outlier data by achieving 99% F1-Score.
Interpretable Models For ML-Based Classification of Obesity

Artificial Intelligence is revolutionizing the healthcare business, thanks to the rising availability of structured and unstructured data and the rapid advancement of analytical methodologies. With artificial intelligence's expanding importance in healthcare, there are growing issues about a lack of transparency and explainability, as well as potential bias in model predictions. The goal of this paper is to use interpretable ML to provide a better understanding of the lifestyle factors that influence the model's predictions of the weight levels, as well as to identify the most critical features for the classification task. We aim to create more accurate and effective predictive models for weight management, which could eventually help individuals make more informed decisions about their diet and lifestyle choices. Our machine learning model got an accuracy of 76% using XG-boost and Random Forest and we tried to interpret the result using a model-agnostic method including, the permutation importance method and partial dependence plot. The results showed that the number of main meals, frequent consumption of vegetables, and time using technology are the most important features. Further explanation was performed using a partial dependence plot which interprets the relationship between these important features and the model behavior.
Investigating the Performance of Data Complexity &amp; Instance Hardness Measures as A Meta-Feature in Overlapping Classes Problem

Since the meta-learning recommendation's quality depends on the meta-features decision quality, a common problem in meta-learning is establishing a (good) collection of meta-features that best represent the dataset properties. Therefore, many meta-feature measures/methods have been proposed during the last decade to describe the characteristics of the data. However, little attention has been paid to validating the meta-feature decisions in reflecting the actual data properties. In particular, if the meta-feature analysis is negatively affected by complex data characteristics, such as class overlap due to the distortion imposed by the noisy features at the decision boundary of the classes and thereby produces biased meta-learning recommendations that do not match the actual data characteristics (either by overestimating or underestimating the complexity). Hence, this issue is crucial to ensure the success of the meta-learning model since the learning algorithm selection decision is based on meta-feature analysis. Based on that, in this work, we aim to investigate this by assessing the performance of Complexity Measures (global/data-level measures) & Instance Hardness Measures (local/instance-level measures) as a meta-feature in reflecting the actual data complexity associated with the high-class overlapping problem. The reason for focusing on the overlapping classes problem is that several studies have proven that this data issue significantly contributes to degrading prediction accuracy, with which most real-world datasets are associated. On the other hand, the motivation for using the above measures among different meta-feature methods proposed in the literature is that since this study aims to focus on the overlapping classes problem, the above measures are mainly proposed to estimate the data complexity according to the geometrical descriptions focusing on the class overlap imposed by feature values, in which match the data problem that the study interested to investigate.
Research on Basin Geomorphic Survey Based on Digital Geographic Information System Technology

Human society is developing rapidly and the demand for the earth's resources is increasing. At the same time, global environmental quality issues are becoming increasingly serious. China is a vast country with abundant water resources, and sustainable development strategy has become a national strategic goal. Therefore, in the process of geological exploration, we need to use advanced digital technology to establish a complete, efficient and sustainable development path. The so-called Geographic Information System (GIS) is a system formed under the role of information technology, computer networks, professional theoretical knowledge in surveying and mapping, referred to as GIS, which can be applied to achieve efficient collection, processing and storage of geospatial information, etc. The potential application value is large and has good market application prospects. At the same time, GIS has the characteristics of high mapping efficiency, high accuracy and good applicability in practice, and can provide professional support for basin geomorphology investigation and research. Therefore, in the process of basin geomorphology survey and research, the efficient use of GIS should be paid attention to. Based on the theoretical research of spatial information technology (DSP) and GIS technology, this paper first gives a brief introduction to digital GIS, then analyses and discusses relevant algorithms and technologies, proposes a series of software system design and optimisation and GIS networked management platform construction, realises data collection and storage of simulated topographic landscapes, and finally discusses and studies the digital GIS-based The specific application of digital GIS-based geological landscape survey is discussed and studied.