Reviewerly/exHarmony huggingface dataset:
MADC: Multi-scale Attention-based Deep Clustering for Workload Prediction

For data centers, workload prediction is important to ensure the service quality and reliability. However, large-scale workload prediction on services is very challenging due to the large cost of model selection, parameter tuning and model training. To reduced this cost, cluster-based prediction is widely proposed. We can group the similar workloads into a small number of clusters and then train the prediction models for per cluster. Many algorithms have been proposed for clustering but they do not solve the challenges of pattern changes and amplitude differences encountered in the production environment. To cope with such challenges, we propose a novel unsupervised workload clustering algorithm Multi-scale Attention-based Deep Clustering (MADC) which can extract features of different time scales. With the help of multi-scale attention mechanism integrated with deep learning, our model can handle workload with pattern changes and amplitude differences, which makes it more powerful and generic than existing clustering algorithms. The MADC model has been deployed in a production system at a large Internet game company. Our evaluation using popular UCR datasets and the trace data from Tencent Games shows that MADC outperforms not only traditional methods but other deep clustering methods in temporal domain. What’s more, MADC can help improve the accuracy of cluster-based prediction models significantly.
Formation of Reading Interest in Younger Schoolchildren through the Innovative Technologies of Book Trailer and Virtual Book Exhibition

The purpose of the study is to substantiate the use of new technologies, such as book trailer and virtual book exhibition, in literature education of younger schoolchildren. The paper considers the issue of the decrease in reading interest among modern younger schoolchildren and determines the need to search for new forms and methods in educational activities to familiarise children with literature reading. Scientific novelty of the study lies in revealing the essence of the book trailer and virtual book exhibition technologies as effective means of forming reading interest in younger schoolchildren. As a result of the experimental study, the authors have analysed the level of reading interest in younger schoolchildren, have proposed ways of using the book trailer and virtual book exhibition technologies in class and during extracurricular activities, which will increase pupils’ reading interest, actualise their cognitive activity, form meta-subject competencies and develop computer skills in educational and research activities.
Alleviating Imbalance in Synchronous Distributed Training of Deep Neural Networks

Deep neural networks achieve huge success in computer vision, natural language process and other fields. As larger networks and larger datasets bring better performance, the training time grows rapidly. Distributed training uses parallelization strategy to accelerate the training of deep neural network. In the existing distributed training framework of deep neural network, the parameter server architecture has poor scalability and is not suitable for large-scale distributed training. Ring/HD All Reduce algorithm perform well in scalability under ideal circumstances. However, in many realistic scenarios, imbalance will occur frequently in the distributed training system, which is mainly the imbalance of compute ability and network. Due to the structure characteristics of synchronous distributed training and All Reduce algorithm, imbalance will bring serious impact on performance. At present, there is a lack of research on both compute ability imbalance and network imbalance, especially dynamic imbalance. To alleviate this problem, we analyze All Reduce algorithm first, and propose RingDL, which optimizes the performance of distributed training system by alleviating imbalance through customizable dynamic workloads strategy and link strategy. The experimental results show that RingDL significantly reduces idle time in computing phase and synchronous phase, improving the overall performance.
Toward classical emulation of quantum states with coherent mixtures

We propose a classical emulation methodology to emulate quantum phenomena arising from any non-classical quantum state using only a finite set of coherent states or their statistical mixtures. This allows us to successfully reproduce well-known quantum effects using resources that can be much more feasibly generated in the laboratory. Our results shed new light on an alternative operational meaning to non-classicality. We present a simple procedure to experimentally carry out quantum-state emulation with coherent states that also applies to any general set of classical states that are easier to generate, and demonstrate its capabilities in observing the Hong-Ou-Mandel effect, violating Bell inequalities and witnessing quantum non-classicality.
Investigating cluster validation metrics for optimal number of clusters determination

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term “optimal” is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms.
Review of Modern Blockchain Platforms

Developers who are seriously thinking about developing products that use blockchain need to be serious about choosing a blockchain platform. Blockchain allows you to create a link between manufacturing, logistics and finance. This means that the choice of a blockchain platform must meet a number of criteria, one of which is security. The paper discusses online and local blockchain platforms, as well as the types of blockchain and consensus algorithms that are used in modern blockchain platforms. The paper compares modern blockchain platforms in terms of security, usability and ease of use.
Diagnosis Detection Support based on Time Series Similarity of Patients Physiological Parameters

Facilitate clinicians in their complex decision- making processes allows to improve patient outcomes along disease-specific pathways and healthcare delivery. This could be realized by enhancing medical decisions with targeted clinical knowledge, patient information, and other health data. Nowadays, a key paradigm in healthcare, designed to be a direct aid to clinical-decision making, is the Clinical Decision Support System (CDSS). Along this line, the work presented in the paper focuses on detecting patients diagnosis, proposing a learning methodology that, on the basis of the current patient status, clinical history, diagnostic and results from their pathological reports, provides insights for clinicians in the diagnosis and therapy processes. The patients physiological signals have been modeled as time series and the similarity among them has been exploited. The main idea is that patients with similar patterns of vital signs are affected by the same or similar health problems and, therefore, may have the same or very close diagnoses. The diagnosis detection method is formulated as a classification problem, combining time series similarity and an ad-hoc multi- label k-nearest neighbor approach (ML-KNN). The proposed classifier exploits the semantic similarity of diagnoses catched through sentence embedding. Results, performed over a real- world clinical dataset, show that the proposed approach is able to successfully detect diagnoses with a precision up to about 75%.
Research on Hole Sequence Planning of Three-Arm Rock Drilling Rig

The three-arm rock drilling rig can efficiently carry out tunnel excavation, which has major advantages over traditional manual blasting. The hole sequence planning of the three-arm rock drilling rig can reduce working time and reduce energy consumption. In this paper, based on the ant colony algorithm, the construction hole sequence of the three-arm rock drilling rig was researched, and an efficient construction sequence is obtained and applied to actual tunnel engineering.
CTDA methodology

Abstract The methodological characteristics of the chronotopologic data analysis chain are discussed. Various kinds of knowledge are considered and properly classified, and several illustrative examples in applied sciences are presented. Big data and data-driven analyses are critically reviewed, and their implementation carefully assessed. Data scale types are classifications considered in property- and attribute-oriented settings. Classical statistics inadequacies are pointed out and the need of a chronotopology-dependent statistics is outlined. The chronotopologic visualization thinking mode and techniques are briefly reviewed.
Designing a Database for Storing the Results of Experiments on Conducting a Batch Process of Isoprene Polymerization

The article proposes an approach to organizing a database for storing and processing the results of experiments on conducting isoprene polymerization in the presence of microheterogeneous catalytic systems in a batch mode. The proposed approach can be applied to systematic work with data from both production and computational experiments. The implementation approach will automate the process of recording data in a database that will facilitate their structuring.
L-VDA: A Lightweight Deep Learning Algorithm for Vehicle Detection

As the key technology of Advanced Driving Assistance System (ADAS), Forward Collision Warning (FCW) enables drivers to perceive the danger in advance through targeted warning at a reasonable time, so as to reduce or avoid the occurrence of collision accidents. However, the high computational complexity of the existing FCW system seriously affects real-time vehicle detection and further leads to traffic safety hazards. In lieu of this, this paper proposes a lightweight vehicle detection algorithm i.e., L-VDA, which uses deep separable convolution to minimize the total number of parameters required to train the model while ensuring detection accuracy. Specifically, this paper firstly proposes a lightweight backbone network i.e., My-MobileNet for multi-scale feature extraction. Then, Spatial Pyramid Pooling (SPP) and Path Aggregation Network (PANet) are used for further feature fusion. Moreover, the priori box is obtained by clustering method and Non-maximum Suppression (NMS) algorithm to get more accurate vehicle detection results. Finally, by training on a variety of data sets such as KITTI data set, UA-DETRAC data set and the data set collected in Xi'an, the robustness of the detection algorithm is optimized. Moreover, experiment results indicate that the proposed L-VDA can achieve low computation complexity while ensuring detection accuracy compared with existing strategies such as YOLOv4 and YOLOv4+MobileNet. Especially, when L-VDA is used to detect the KITTI data set, the weight of the model is only 26.8M, which was reduced by nine times compared with YOLOv4, and the average precision reaches 95.2%.
18 Technologies that delight: co-designing augmented reality digital companions for children being admitted to hospital

<h3>Introduction</h3> Hospitalisation is a stressful experience for children. While parents and hospital staff are critical in helping to alleviate fear and manage expectations, here we present a Mobile Augmented Reality (MAR) App running on a smartphone to help manage this rollercoaster of emotions by overlaying digital content onto the physical world. <h3>Method</h3> Co-designed with Hospital Guides and the Young Persons Advisory Group (YPAG), we developed an interactive MAR prototype to guide children on their hospital admission. Called ‘GOSH garden’, we visualise a series of four short, contextualised, interlinked moments with four unique plant characters and a wayfinding butterfly. The AR experience begins from when children first receive their admissions letter at home, to arriving at the hospital, waiting for an appointment, and their first encounter with a clinician. <h3>Results</h3> Unity3D engine was used as a development platform for fast prototyping using scene building tools and good support for AR libraries, such as AR Core and Vuforia which were used for marker detection and extended tracking. By using smartphone device rear-facing cameras, this allows users to observe the scene ‘through’ their device. Our initial user feedback from 10 YPAG members supported plant characters that celebrate imperfections, recommended for more greenery and interactive play, and revealed that MAR companions have the potential to make children entering hospital feel more comfortable with the uncertainty that lies ahead. This led to six new voiceovers narrated by YPAG members. <h3>Conclusion</h3> The persuasive strength of interactive narratives has been shown to foster engagement, enjoyment, and creativity in children with increased benefits to traditional storytelling. We propose that a series of moments for positive distraction, that use interactive media, is one technological approach and a ‘gentle’ kind of user distraction that can be designed to help children as they enter the unknown world of a hospital.
Effect machine learning techniques for analyzing and filtering spam Mails problems

The increase in spam and unwanted emails and messages has increased dramatically over the few past years to overcome this problem the need to develop more reliable and more powerful anti-spam filters. Modern machine learning techniques are applied to identify and filter spam messages. The research provide a symbolic review of different automated, learning-based spam filtering methods. This test provides details of the key concepts in spam filtering, testing, productivity, and search directions. The first discussion behind the study detect the application of machine learning approaches to the spam filtering methods for leading Internet service providers, such as spam filters in Gmail, Yahoo and Outlook. In this paper we provide the experiments about the normal spam filtering process and different work of researchers in finding spam using efficient ML techniques. Our research compares strengths and faults in current machine learning methods and clear research issues in spam filtering. We recommend in-depth and in-depth learning as future technologies that can accurately address the threat of spam. The necessity of effective spam filters increases. In this research, we also applied an efficient spam filter approaches to spam emails and messages based on Machine learning techniques such as Neural networks to remove the unwanted messages or raw data. The Neural Network Filtering feature evaluates the likelihood of different words appearing in Legitimate and spam mails and classifies them according to these possibilities.
Spotlight Type Pointing Environment for Librarian

In coronavirus [Covid-19] catastrophe, we don't do "closed spaces", "crowded places" and "close-contact settings" for preventing COVID-19 and stopping its spread. A librarian guides a book to a user under "close-contact settings". We propose spotlight type pointing method called "Scale Gesture Spotlighting" for supporting guiding the book. We experimented to 8 participants and recorded guide time by "Without Support", "Pointing and Calling" and proposed method. Average time of guide by proposed method was shortest at 8.43 seconds as analysis result.
Predictive Model for Regional Elections Results based on Candidate Profiles

User-generated contents from Twitter have been utilized to do sentiment analysis for predicting the presidential election result. Researchers successfully proposed methods based on Text Mining and Machine Learning approach to create sentiment analysis model as basis for prediction. However, Twitter-based prediction is difficult to be utilized in regional election, as massive tweets usually posted regarding elections held in provinces, cities, or large districts only. Moreover, Twitter-based prediction must deal with unstructured data, fake/ bot account, wrong information, mixed of languages, nonstandard writing style, and even subjectivity when labeling the dataset. Therefore, this work proposed an alternative prediction model for regional election result based on candidate's profile which is officially published by General Election Commission of the Republic of Indonesia. There are four main tasks in this work, i.e., data collection, data preprocessing, feature engineering, and data classification using C4.5 decision tree algorithm. As the result, the predictive model achieved accuracy of 72.96% after doing post and pre-prunning procedures. This work also contributes to generating a new dataset for predicting the result of regional election in Indonesia which contains related features that affect the winning of candidates.
Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust

Zanthoxylum bungeanum Maxim, generally called prickly ash, is widely grown in China. Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum. Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience. Due to the complex colors and shapes of rust areas, the accuracy of manual recognition is low and difficult to be quantified. In recent years, the application of artificial intelligence technology in the agricultural field has gradually increased. In this paper, based on the DeepLabV2 model, we proposed a Zanthoxylum rust image segmentation model based on the FASPP module and enhanced features of rust areas. This paper constructed a fine-grained Zanthoxylum rust image dataset. In this dataset, the Zanthoxylum rust image was segmented and labeled according to leaves, spore piles, and brown lesions. The experimental results showed that the Zanthoxylum rust image segmentation method proposed in this paper was effective. The segmentation accuracy rates of leaves, spore piles and brown lesions reached 99.66%, 85.16% and 82.47% respectively. MPA reached 91.80%, and MIoU reached 84.99%. At the same time, the proposed image segmentation model also had good efficiency, which can process 22 images per minute. This article provides an intelligent method for efficiently and accurately recognizing the degree of infection of Zanthoxylum rust.
Kernelised correlation filters target tracking fused multi‐feature based on the unmanned aerial vehicle platform

As unmanned aerial vehicle (UAV) emerged as a flexible acquisition system that is widely used in military and civilian fields, efficient target tracking algorithm is in urgent need for UAV-based computer vision. Although research studies have been reported on typical interferences in the tracking process such as scale change, occlusion, distortion etc., some issues still exist for the target tracking algorithm based on UAV vision. This study exploited the features hidden in different colour spaces, and proposed a multi-feature multi-filter fusion tracking method that combines the HSV (hue, saturation and value) colour space with the histogram of oriented gradient (HOG) feature. The HSV colour space is proved to be able to discriminate objects under different conditions. The HOG of each HSV channel is utilised to train Kernelised correlation filters (KCF), respectively. The final tracking result is the candidate result with the biggest peak sidelobe ratio (PSR). Computer simulations proved that the fusion strategy proposed in this study can effectively improve the tracking performance of the tracker especially when the image sequences are interfered by deformation, occlusion, low resolution, etc. The performance of the tracker is also tested on UAV.
The Earth Remote Sensing Method via Quantum and Optical Systems

The aim of this paper is to develop a method and to investigate the possibility and efficiency of the use of ground-based quantum and optical systems, such as light laser beacons (LLB) and on-board optical facilities, for the Earth navigation information processing. The interconnect Earth’s orbital artificial satellite (EOAS) and LLB system was considered. Light laser beacons are the benchmarks accurately referenced to the geocentric coordinate system equipped with an emitter with an area of just 2 mm. Such natural reference points are almost impossible to find on the Earth. Another advantage is that the referencing of EOAS to LLB could be performed at night time as well. The coordinate referencing technology of the Earth’s surface images using LLB is performed by analyzing the geometric correspondence between an image and a cartographic grid based on the determination of coordinates for the points of the same name and the construction of a regression model. It should be noted that space images in the initial form contain significant geometric and brightness distortions. Therefore, while processing, geometric corrections and transformation of images into the accepted cartographic projections are performed, that result in bringing the images to the standard form. In this paper, Earth remote sensing methods using LLB as well as the ones for referencing images of the Earth’s surface to LLB and electronic maps are analyzed and evaluated from the technical point of view. The paper also describes the authors’ method of referencing images of the Earth to the stellar field using LLB as the transition benchmarks between two images of Earth and one of the stellar sky.
Using Formative Assessment Methods in the Process of German Language Teaching in the Fifth Forms of General Academic School

The study aims to prove by experiment the effectiveness of using formative assessment in the process of German language teaching in the fifth forms of general academic school in the context of the Federal State Education Standard. The paper analyses the advantages of using formative assessment in the process of German language teaching, describes the results of the experimental work on testing additional formative assessment methods in the fifth forms. Scientific novelty of the study lies in elaborating the nomenclature for the process of assessing learning outcomes of general academic school students. As a result, the effectiveness of using formative assessment methods in the process of German language teaching in the fifthforms of general academic school has been proved.
Hardware Design Space Exploration of a Selection of NIST Lightweight Cryptography Candidates

Round 2 of the NIST lightweight cryptography standardization project lasted till 29 March, 2021 and resulted in the selection of 10 candidates as the finalists.