Reviewerly/exHarmony huggingface dataset:
Simple Yet Effective Approach to Repetitive Behavior Classification based on Siamese Network

Conventional studies dealing with repetition detection have mainly focused on tasks of temporal localization or counting the number of repetitions in videos. However, direct discrimination between repetitive and non-repetitive behaviors in videos, called repetitive behavior classification (RBC), has attracted less attention despite its great potential and advantages of: 1) filling the demands in the fields such as classification of repetitive behaviors in children with autism spectrum disorder (ASD) and helping to alleviate manual and time-consuming diagnostic procedures, 2) directly learning representation of differences between repetition and non-repetition patterns along the temporal dimension, and 3) being an effective alternative to the existing repetition counting and temporal segmentation tasks that are struggling with insufficient data and laborious manual annotation effort. In this paper, to the best of our knowledge, we firstly cast the problem of the RBC using deep learning frameworks. For this, we propose a simple yet effective add-on network, SiRepNet, that exploits the Siamese network structure to learn the inherent properties of repetitive behaviors. We also composed the RBC dataset by re-purposing and re-organizing Kinetics and Countix datasets for training our method. To validate our ideas, we carried out extensive experiments on RBC datasets, which showed performance improvement over state-of-the-art video classification algorithms by simply attaching our scheme to them for the RBC task.
Social Challenges and Design. Exploiting Digital Technologies to Trigger a Behavioural Transition Towards a Sustainable Food Life Cycle

Today digital technologies offer an unprecedented opportunity to facilitate dynamic and engaging interactions among people, promote conversations around crucial issues - such as climate change - guide joint action, and trigger innovation. In this paper, we aim to discuss how design can act as a lever towards adopting a more sustainable behaviour in food consumption, exploiting the potential of digital technologies. The paper first outlines the scope of interest: climate change is a global concern, and sustainability is a key topic on the European agenda. To ensure systematic changes towards a circular economy and achieve a low emission pathway, active public participation and trust in transition are essential. We reflect on this challenge by drawing on a research initiative called FOODIE, “FOOD Impact for Earth,” envisioned by an international, multidisciplinary, and heterogeneous consortium. Under such an initiative, through an active confrontation among all the involved expertise, a digital system was designed aimed to activate a virtuous model to change young citizens’ food consumption behaviour actively. Besides such a system, the methods applied to test its validity with citizens are described. The paper emphasizes the value of design as a transformative tool to respond to contemporary social challenges. In addition to natural sciences and engineering, design should indeed be considered a fundamental lever to address the environmental crisis of our time.
KAFNN: A Knowledge Augmentation Framework to Graph Neural Networks

The semi-supervised node classification task is a basic problem in graph neural networks(GNNs). GNNs have shown their superiority in graph datasets over traditional neural networks such as Multilayer Perceptron. However, due to the limitation of Weisfeiler-Lehman, the existing GNNs will discard some prior knowledge, which is hard to be coped with, such as Dropout skill, etc. In this paper, we proposed a framework called KAFNN to introduce knowledge discarded obliviously to enhance data representation. KAFNN, based on the Siamese network, introduces the framework of combining GNNs and deep neural networks(DNNs) to capture the data presentation as whole as possible, which will inject more knowledge into GNNs. Extensive experiments based on seven public datasets and seven GNN models have shown that KAFNN has promoted presentation of several state-of-the-art GNN models in a competitive performance.
Design and Implementation of Taijiquan Learning System based on PHP+ MySQL

As a traditional Chinese martial arts event, Tai Chi (Also known as Taijiquan) has a unique cultural connotation and excellent fitness function. MySQL is currently the most commonly used relational database. PHP is an open source dynamic web development script language. Researching a Tai Chi learning system based on PHP+ MySQL is of great help to the promotion of Tai Chi. Based on this, this article will study the subject of the design and implementation of the Taijiquan learning system based on PHP+ MySQL. This article first understands the students' understanding of “Twenty-Four Simplified Taijiquan” through questionnaires, and then analyzes the functional requirements of the system, and then designs the functional structure of the Taijiquan learning system. This article uses PHP+ MySQL software, three-dimensional technology, etc. to design the functional modules of Tai Chi. Finally, this article examines the effectiveness of the system in Taijiquan learning from the students' perspective and the students' satisfaction with the system. The survey results show that the highest user satisfaction is the content selection of the system, accounting for 88.21%, reflecting that Taijiquan is a familiar and favorite martial arts event with a broad mass base; followed by the speed of the system, accounting for 82.93%. It shows that the system reacts in time and runs smoothly.
Aspiring teachers’ technology-enhanced learning practices within and beyond teacher education coursework: lessons for a post COVID-19 era

The shift to emergency remote teaching during the COVID-19 pandemic has further revealed a range of issues related to technology-enhanced learning (TEL). Among these were educators’ lack of prior exposure to more creative and participatory forms of TEL in credential coursework and professional development. To address the new realities of schooling, we must first understand the TEL practices that aspiring teachers have had in their teacher education courses. This study examined the TEL practices of aspiring teachers within and beyond their educational coursework prior to the COVID-19 pandemic, as well as their perceived degree of proficiency with these practices. Our survey research found that participants reported more experience and proficiency with TEL practices involving consumption-based and transactional modes of learning, and less experience and proficiency with practices related to creativity, collaboration, and knowledge generation. We discuss how these findings suggest improvements for credentialing programs in the area of TEL.
One-shot coherence distillation in superconducting circuit systems

Distilling quantum coherence is important for optimizing the performance of quantum technologies, however, it cannot always be accomplished with certainty. Then, the probabilistic distillation of quantum coherence has been developed and successfully implemented in experiments. We introduce a proposal to realize the one-shot coherence distillation in the superconducting circuit system. The target maximally coherent state can be extracted from a single copy of the prepared state by using appropriate incoherent operations and a finite error tolerance is allowed. It is easy to implement our scheme in the experiment that only a superconducting qubit is required to be the auxiliary system. To demonstrate the feasibility of our scheme, we numerically simulate the distillation process under the influence of dephasing (according to the typical experimental parameters). We find that the distillation rate of the coherence resource can be well achieved with the current experimental technique.
Explainable machine learning for sequences of demographic statuses

The article presents a case study on demographic sequences analysis through modern machine learning (ML) techniques. The studied data contains demographic and socioeconomic events, where the events are presented as sequences of statuses. The involved demographers are interested in applications of advanced ML techniques and interpretable patterns for their needs. We show how Shapley value-based explanations can be obtained for such sequential data with powerful ML approach, namely gradient boosting over decision trees. Thus, it helps to understand the critical and influencing events for a particular individual life-course sequence and explain predictions.
Evolutionary algorithm for generating optimized configuration of computational distributed cluster considering simulation environment and specified workload

Today cloud computing provides effective solution for any business infrastructure problem through quick adaptation to any conditions and requests. However, testing the intended cluster configuration in a sandbox or using cloud resources is sometimes impossible in principle. In this situation arises the problem of choosing the right configuration of the computational system, taking into account the financial and operational criteria. This research proposes using a genetic algorithm scheme to generate a computational cluster configuration and to evaluate the quality of the created solutions through a simulated computing environment with integrated models and adapted case studies. This approach provides a fast selection of configurations even for complex configurations and surpasses heuristics that implement the basic logic of experts as well.
Downtime Distribution, Risk Volatility, and Optimal Backup VMs Allocation in Cloud Services

Service Level Agreement (SLA) is an important basis to ensure the availability of cloud service. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize the risk uncertainty. Under such a mechanism, service providers look forward to determine the optimal number of backup virtual machines (VMs) to minimize operational costs. In this paper, we propose a data-driven risk measurement method under multi-stage SLA and empirically portray the likelihood and risk values of compensation events at different stages under several service periods. Furthermore, a multi-objective optimization function is established to balance the provisioning cost and risk volatility for cloud service providers with different risk preferences. This paper provides a practical method to cloud service providers by addressing the risk uncertainty in multi-stage SLA from cloud computing resource management perspective.
A Novel Distributed Differential Privacy Preserving Based on Random Forest in Data Centers

Traditional privacy-preserving technologies have been unable to provide adequate protection and are vulnerable to background knowledge. MapReduce is parallel distributed computing model which has the advantages of good scalability and high fault tolerance. In this paper, we design a differential privacy protection based on MapReduce for the security problems faced in the distributed environment. Firstly, we propose a random forest algorithm DPMRRF that satisfies differential privacy under the MapReduce framework. Secondly, we use the MapReduce framework in the Hadoop platform to make the classification results satisfy differential privacy by adding random noise values to the leaf nodes. Finally, through the design of the feature selection scheme combined with the index mechanism, three different privacy budget allocation methods are adopted, namely, equal distribution, equal distribution and equal distribution. Experiments show that the algorithm has better classification accuracy while ensuring data availability. The proposed algorithm effectively improves the classification accuracy and operating efficiency, and greatly reduces the amount of computation.
Detection and Location of High Voltage Power Switchgear Operating Equipment based on Iterative Segmentation

During the operation of high voltage switchgear in substation, partial discharge may occur. Therefore, it is necessary to develop automatic operation equipment to reduce personnel risk. For this reason, this paper proposes a detection method of key operation control components based on segmentation zone iterative detection for the operation environment of high-voltage switchgear in substation, which is used to provide basic operation guidance for the operation robot of high-voltage switchgear. This method can quickly analyze the switchgear image and find out the operable parts. At the same time, through the extraction of topological relationship and image features, the feature relation network of the operating parts is constructed. On this basis, the semantic extraction of the operating parts based on feature matching is realized, which lays the foundation for the realization of automatic mechanical operation. The method proposed in this paper is tested on the actual switch cabinet data, and the results show that the actual detection accuracy is significantly improved compared with the existing methods, and has good scalability and adaptability.
Keywords Extraction and Thesaurus Construction for Domain News

In modern information retrieval systems, the thesaurus is playing an increasingly important role. In order to better describe and analyze the domain news, this paper proposes a method of domain keyword extraction, and further constructs an effective domain thesaurus. Compared with the previous research, this paper grasps the core information in the field by extracting and combining domain keywords, and improves the domain effectiveness of the thesaurus. In addition, this paper conducts both manual analysis and automated processing to construct high-quality thesaurus, which has practical application value. The final results provide support for the process of indexing, organizing, retrieving and recommending news.
Conceptual structure of federated learning research field

Nowadays there are a great amount of data that can be used to train artificial intelligent systems for classification, or prediction purposes. Although there are tons of publicly available data, there are also very valuable data that is private, and therefore, it can not be shared without breaking the data protections laws. For example, hospital data has great value, but it involves persons, so we must try to preserve their privacy rights. Furthermore, although it could be interesting to train a model with the data of only one entity (i.e. a hospital), it could have more value to train the model with the data of several entities. But, since the data of each entity might not be shared, it is not possible to train a global model. In that sense, Federated Learning has emerged as a research field that deals with the training of complex models, without the necessity to share data, and therefore, keeping the data private. In this contribution, we present a global conceptual analysis based on co-words networks of the Federated Learning research field. To do that, the field was delimited using an advance query in Web of Science. The corpus contain a total of 2444 documents. As the main result, it should be highlighted that the Federated Learning research field is focused on six main global areas: telecommunications, privacy and security, computer architecture and data modeling, machine learning, and applications.
Multi-criteria linear optimization classifier with semantically weighted kernels for Chinese word formation pattern prediction

For many classification methods, different features in these classification models are usually organized into a flat and equal-length vector while semantically restrictive relations or nonparallel structures among them are often ignored. However, sometimes the semantic relations or structures among different attributes derived from the domain knowledge are very important, for instance, natural language processing and biological data mining, which can help to increase the performance of classification models considerably. For Chinese semantic word formation analysis with the large-scale Chinese corpus, the feature set is horizontally composed of different morpheme types, semantic types and part of speech (POS) while it vertically consists of three levels of big, medium and small classes. If a classifier model does not consider these relations or structures, then it can lead to information loss and distortion, and the degenerated classifier model can be ultimately obtained. In this paper, in view of the aforementioned limitations, by defining a new conceptual hierarchy graph, the semantic weights of different features are obtained, then semantically weighted kernels are formulated and introduced to multi-criteria linear optimization classifier (MCLOC). Thus, a new semantically weighted MCLOC (SW-MCLOC) using the linear and radius basic function (RBF) kernels is proposed and applied to Chinese word formation pattern prediction. The experimental results and comparison among MCLOC, SVM and SW-MCLOC show that our proposed model can effectively increase the separation of different word formation patterns, the predictive accuracy of target patterns and the generalization power of pattern classification on new compound words.
An Majorize-Minimize algorithm framework for large scale truncated loss classifiers

The truncated losses, the truncations of the convex losses, have been widely applied in classification tasks due to their robustness. However, the corresponding non-convex optimization problems are hard to solve efficiently. In this paper, we propose an algorithm framework for the truncated loss classifiers by finding a type of surrogate integral loss of the truncated losses. We show that the surrogate integral loss is a computationally efficient equivalent of the truncated loss. Thereafter our algorithm framework is present to solve the truncated loss model, and allows us to obtain the critical point of the truncated loss model by solving a series of the corresponding original convex loss models. In fact, for any solvable original convex loss model, the corresponding truncated loss model can be solved by our algorithm in linear and nonlinear cases. Experiments on benchmark datasets show that our algorithm framework gets high efficiency if the original convex loss models can be solved.
Augmented Reality based Decision Support System in Coal Mine

Augmented reality (AR) has been applied in many situation as the development of visualization and communication technologies. In coal mine industry, it is essential to improve the decision making process in the underground front line with the assistance of AR. In this paper, we propose a framework to implement an AR based decision support system, which includes knowledge support, real time expert connection and AR communication platform. The proposed framework has been applied in a coal mine in China and the user feedback indicate that the proposed framework can improve the communication and knowledge access efficiency in the coal mine industry.
Optimization Model for Data Learning from the Eigenvector of Bayesian Matrix

This paper presents an optimization model to complete the missing data in the energy matchup matrix which compares different players’ energy. The optimization model searched for the solutions that can make the eigenvalue of the energy matrix the biggest that should be the principle one. To test the model, we built a Bayesian matrix of energy matchup which mixes the judgments of energy in a matchup between players given by experts and the statistical data gotten from each game in different positions. The proposed method can complete the probabilities given by the Bayesian matrix. Finally, an implementation shows the effectiveness and rationality of the model.
Inter- and Intra-Graph Attention Aggregation Learning for Multi-relational GNN Spam Detection

In some illegal businesses, spammers are hired to post fake reviews on social media platforms, causing spammers to appear among normal reviewers. Studies have used graph neural networks (GNNs) to detect spammers based on community characteristics and correlations. A common problem with graph neural network construction is the failure to filter out noisy neighbors of the center node. Due to the aggregation of graph neural network message transmission, the information carried by these noisy neighbors will influence the detection process. We propose a method based on Label-aware cosine similarity for filtering camouflaged and noisy nodes from the center node in order to retain potentially similar nodes of the same type for message passing. Several studies have used multi-relational graphs to detect spammers who implement behavior features and relation camouflage. Despite improvements in the detection effect, there are still shortcomings in the design of intra-relation aggregation and inter-relation aggregation. In this paper, multi-relational graphs are examined to enhance the detection of spammers using intra-relation aggregation and interrelation aggregation. Extensive experimental results show that the proposed model outperforms the state-of-the-art GNN-based spam detection on the Yelp and Amazon datasets.
Extraction of hidden topics in urban context based on the Internet publications analysis

The problem considered in the article is the systematic lack of data on the objects of the urban environment for management and decision-making. This problem is particularly acute in the lack of data on points of attraction, informal and thematic places of interest. At the same time, this kind of information is necessary for the qualitative development of the urban environment. This article discusses an approach to creating new information resources based on the analysis of publications and messages of citizens, which can be used to effectively manage the development of the city and improve the quality of the urban environment. For example, to create new centers of attraction for citizens and tourists and effective landscaping. Currently, there is a public demand for the semantic content of the urban environment, considering historical and cultural associations, informal symbols. Traditionally, this request is met through surveys of the population in urban improvement projects. This article presents an approach to supplementing such surveys with information from Internet social networks processed by natural language analysis methods to extract hidden topics and thematic objects of the urban environment. The approach is demonstrated based on the example of the city of St. Petersburg in the Russian Federation
Decentralized Trajectory Optimization for Multiple UASs under Uncertainty of Intent and Wind

To guarantee the operational safety of unmanned aircraft systems (UASs) in short time scales, it is important to adopt the systems for planning conflict-free trajectories which are robust against the uncertainty of the tactical flight intent of neighboring UASs and the wind conditions. Thus, the present paper proposes a new decentralized trajectory optimization method that enables to track the strategic four-dimensional trajectories as much as possible while maintaining the necessary separation from the neighboring UASs under the above-stated uncertainty. The proposed method adopts a fully decentralized formulation for separation constraints which is enabled by the worst-case estimate of the uncertainty of the neighboring UAS's decision and wind. The effectiveness of the method is demonstrated through numerical simulations.