Reviewerly/exHarmony huggingface dataset:
Online Learning Technologies

The paper describes the technology for creating an online course using the Learme online courses platform, ArticulateStoryline software. Organization of an online course, a brief description of the implementation of an online course based on elearning standards. The article reflects the results of the design and implementation of the online course “Web-programming” based on the e-learning standard.Development of an intelligent training course using data mining algorithms. Intelligent algorithms (data mining algorithms) underlie the development of intelligent technologies in scientific and electronic educational processes.
PANDORA: Continuous Mining Software Repository and Dataset Generation

During the mining software repository activities, a huge amount of data gathered from different sources is analyzed. Different tools have been developed for collecting and aggregating data from repositories, but they do not easily allow researchers to develop new extractors, to integrate the data collected from other platforms, and in particular from platforms that delete the data periodically. Moreover, mining software repository studies are commonly performed on old versions of software projects and their results are not commonly periodically updated. As a result of the non-continuously updated studies, practitioners often do not trust results from empirical studies. In order to overcome the aforementioned issues, in this paper, we present Pandora, a tool that automatically and continuously mines data from different existing tools and online platforms and enables to run and continuously update the results of mining software repository studies. To evaluate the applicability of our tool, we currently analyzed 365 projects (developed in different languages), continuously collecting data from December 2020 to May 2021 and running an example study, investigating the build-stability of SonarQube rules. Link to dashboard: http://sqa.rd.tuni.fi/superset/dashboard/1 Link to source code: https://github.com/clowee/PANDORA Link to 5-minutes video: https://youtu.be/CuVO9YGJ59I
An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function (RBF)-neural network (NN) is proposed in this paper. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance signals. Results show that effective weak signals can still be obtained when SNR = -10 dB, verifying the effectiveness of the algorithm.
Segmenting Brain Tumors in Multi-modal MRI Scans Using a 3D SegNet Architecture

Gliomas are the most common type of primary brain tumor, and high-grade gliomas are typically treated using a combination of chemotherapy, radiation therapy, and surgical excision. For the latter two therapy options, precise knowledge about the location of the tumor and its components is required, which can be obtained using MRI scans. Manually labeling the tumor area in those 3-dimensional images is a tedious and time-consuming task, hence major efforts have been made to provide automated segmentation. We present our solution to the BraTS 2021 challenge Task1, where we segment gliomas in MRI scans using a SegNet-based approach, achieving competitive and stable performance across tumor types and components. Compared to previous solutions using UNet architectures, our model achieves improved segmentation of the peritumoral edema and comparable performance for the other classes while reducing the number of parameters.
A Multi-Objective Optimization Scheduling Method Based on the Genetic Algorithm in Cloud Computing

For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing. This paper propose a resource cost model that defines the demand of tasks on resources with more details. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan, wall clock time , execution time and the costs as constraints of the optimization problem. This paper proposed a multi-objective improved genetic algorithm (MOIGA) to address multi-objective task scheduling problems. The experiment results showed that the MOIGA algorithm minimizes makespan, wall clock time, execution time and cost when compared with First Come First Serve (FCFS), Round Robin (RR) and Shortest Job First (SJF).
Implication of multifractal analysis for quantitative evaluation of mineral resources in the Central Kunlun area, Xinjiang, China

Extensive research has been conducted to evaluate mineral resource potential based on geochemical data, but this work is still challenging due to the existence of multiple evaluation solutions based on different methods. In this paper, we combine the multifractal analysis method with typical multivariate statistical methods to analyse the spatial characteristics of geochemical stream sediment data, aiming to quantitatively study the ore-forming potential of the elements in the Central Kunlun area of Xinjiang, China. An R -type cluster analysis, Pearson correlation analysis, and principal component analysis are used to explore the correlations among the 12 target elements. The multifractal model is constructed using the method of moments to analyse the spatial distribution patterns of the elements, and corresponding multifractal parameters are extracted to quantitatively describe their ore-forming strengths in the study area. The results show that Co, V, Ti, Fe 2 O 3 , MgO and Cu compose a group of elements closely related to the regional geological background, while Pb, Zn, Bi, Sn, Au and Ba are potential metallogenic elements with relatively high ore-forming strengths and favourable ore-forming potential. Multifractal theory further validates and evaluates the favourable ore-forming element group obtained through conventional geochemical multivariate statistical methods, thus providing a new idea for small-scale geochemical prospecting. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
Resolving-power domination number of probabilistic neural networks

Power utilities must track their power networks to respond to changing demand and availability conditions to ensure effective and efficient operation. As a result, several power companies continuously employ phase measuring units (PMUs) to continuously check their power networks. Supervising an electric power system with the fewest possible measurement equipment is precisely the vertex covering graph-theoretic problems otherwise a variation of the dominating set problem, in which a set D is defined as a power dominating set (PDS) of a graph if it supervises every vertex and edge in the system with a couple of rules. If the distance vector eccentrically characterizes each node in G with respect to the nodes in R, then the subset R of V (G) is a resolving set of G. The problem of finding power dominating set and resolving set problems are proven to be NP-complete in general. The finite subset R of V (G) is said to be resolving-power dominating set (RPDS) if it is both resolving and power dominating set, which is another NP-complete problem. The ηp (G) is the minimal cardinality of an RPDS of a graph G. A neural network is a collection of algorithms that tries to figure out the underlying correlations in a set of data by employing a method that replicates how the human brain functions. Various neural networks have seen rapid progress in multiple fields of study during the last few decades, including neurochemistry, artificial intelligence, automatic control, and informational sciences. Probabilistic neural networks (PNNs) offer a scalable alternative to traditional back-propagation neural networks in classification and pattern recognition applications. They do not necessitate the massive forward and backward calculations that ordinary neural networks entail. This paper investigates the resolving-power domination number of probabilistic neural networks.
Trigger is Non-central: Jointly event extraction via label-aware representations with multi-task learning

Event extraction (EE) occupies an important position in information extraction. Recently, deep neural network methods have been demonstrated to learn potential features well. However, existing networks for EE suffer from the following challenges: (1) Event argument extraction relies heavily on the classification of event triggers. (2) Previous works fail to exploit the predefined label representations for EE. (3) The interactive information between candidate arguments has not been fully exploited. To address the above mentioned problems, in this paper, we propose an advanced multi-task learning framework, named TNC, based on a fresh concept proposed by us: Trigger is Non-Central, in which event argument extraction no longer depends on the event triggers but is performed synchronously with it. Our TNC extracts multiple event triggers and arguments simultaneously by adopting label representations and an auxiliary task, named Sentence Event Identification (SEI), which is devised to extract the event types contained in a sentence. In addition, we design a special symbol to merge the representation of candidate arguments over the Transformer encoder. We experiment on the widely used ACE 2005 corpora and TAC-KBP 2015, and the experimental results have proved that our model achieves state-of-the-art compared to other models, with higher effectiveness and adaptability.
Global-Aware Prototypical Network for Few-Shot Encrypted Traffic Classification

Performing encrypted traffic classification under a few-shot scenario is vital because of labor-intensive labeling and intrinsically rare samples. Most existing methods apply metric learning to solve the problem of few-shot encrypted traffic classification. However, those methods only involve local information of traffic inputs to distinguish different traffic types, which weakens classification performance. In this paper, we devise Global-aware Prototypical Network (GP-Net) for few-shot encrypted traffic classification by aggregating the global information of the traffic inputs. Specifically, GP-Net firstly captures the relations between any two bytes of payload sequence, regardless of the spatial distance, and then utilizes the byte-wise relationships to aggregate the global information of traffic inputs. Moreover, we model the position information of bytes in payload sequence by leveraging the relative position mechanism, which enhances the express ability of GP-Net. We conduct extensive experiments on the real-world traffic dataset to evaluate the effectiveness of GP-Net. The experimental results demonstrate that GP-Net achieves high performance when recognizing a new traffic type even when the number of traffic samples is less than 20, outperforming state-of-the-art (SOTA) few-shot encrypted traffic classification methods.
A Sparse Convolution Neural Network Accelerator for 3D/4D Point-Cloud Image Recognition on Low Power Mobile Device with Hopping-Index Rule Book for Efficient Coordinate Management

This work presents the first 3D/4D sparse CNN (SCNN) accelerator for point cloud image recognition on low power devices. A special hopping-index rule book method and efficient data search technique were developed to mitigate the overhead of coordinate management for SCNN. A 65nm test chip for 3D/4D images was demonstrated with 7.09–13.6 TOPS/W power efficiency and state-of-the-art frame rate.
DeepFaceVideoEditing

Sketches, which are simple and concise, have been used in recent deep image synthesis methods to allow intuitive generation and editing of facial images. However, it is nontrivial to extend such methods to video editing due to various challenges, ranging from appropriate manipulation propagation and fusion of multiple editing operations to ensure temporal coherence and visual quality. To address these issues, we propose a novel sketch-based facial video editing framework, in which we represent editing manipulations in latent space and propose specific propagation and fusion modules to generate high-quality video editing results based on StyleGAN3. Specifically, we first design an optimization approach to represent sketch editing manipulations by editing vectors, which are propagated to the whole video sequence using a proper strategy to cope with different editing needs. Specifically, input editing operations are classified into two categories: temporally consistent editing and temporally variant editing. The former (e.g., change of face shape) is applied to the whole video sequence directly, while the latter (e.g., change of facial expression or dynamics) is propagated with the guidance of expression or only affects adjacent frames in a given time window. Since users often perform different editing operations in multiple frames, we further present a region-aware fusion approach to fuse diverse editing effects. Our method supports video editing on facial structure and expression movement by sketch, which cannot be achieved by previous works. Both qualitative and quantitative evaluations show the superior editing ability of our system to existing and alternative solutions.
Soccer Player Video Target Tracking Based on Deep Learning

Soccer player video target tracking is a very challenging task, which has good practical and commercial value. Traditional soccer game target tracking relies on athletes to carry a recording chip to achieve target tracking, but the cost is very high. With the rapid development of photography technology and deep learning technology, athletes’ target tracking is realized through soccer game video. Deep learning technology is applied to computer vision detection and tracking. How to realize soccer players’ video target tracking under deep learning is a challenging lesson. To solve this problem, this paper takes the video target tracking of football players as the research object, collects the game images of the stadium through multiple cameras, realizes the long-term accurate tracking of multiple players, and establishes a multicamera multitarget tracking system. The KCF algorithm and the improved KCF algorithm formed by replacing the hog feature of the KCF algorithm with the depth convolution neural network are used to compare and analyze the impact of different target tracking ranges and target numbers on the target tracking accuracy of the system, so as to accurately obtain the motion trajectory of football players. The results show that the image data of football matches are collected independently by multiple cameras, and the data of multiple cameras are collected to generate each target motion datum. The KCF algorithm of multicamera multitarget tracking has good robustness and real-time for long-term accurate tracking of football players; the KCF algorithm and the improved KCF algorithm have high accuracy in target tracking. With the increase of tracking frame range, the accuracy of target tracking of the two algorithms is improved. At the same time, multitarget tracking helps to improve the antiocclusion ability of the system. The research results have important practical significance and good application prospects for the analysis technology of video content of football matches.
Network Security Node-Edge Scoring System Using Attack Graph Based on Vulnerability Correlation

As network technology has advanced, and as larger and larger quantities of data are being collected, networks are becoming increasingly complex. Various vulnerabilities are being identified in such networks, and related attacks are continuously occurring. To solve these problems and improve the overall quality of network security, a network risk scoring technique using attack graphs and vulnerability information must be used. This technology calculates the degree of risk by collecting information and related vulnerabilities in the nodes and the edges existing in the network-based attack graph, and then determining the degree of risk in a specific network location or the degree of risk occurring when a specific route is passed within the network. However, in most previous research, the risk of the entire route has been calculated and evaluated based on node information, rather than edge information. Since these methods do not include correlations between nodes, it is relatively difficult to evaluate the risk. Therefore, in this paper, we propose a vulnerability Correlation and Attack Graph-based node-edge Scoring System (VCAG-SS) that can accurately measure the risk of a specific route. The proposed method uses the Common Vulnerability Scoring System (CVSS) along with node and edge information. Performing the previously proposed arithmetic evaluation of confidentiality, integrity, and availability (CIA) and analyzing the correlation of vulnerabilities between each node make it possible to calculate the attack priority. In the experiment, the risk scores of nodes and edges and the risk of each attack route were calculated. Moreover, the most threatening attack route was found by comparing the attack route risk. This confirmed that the proposed method calculated the risk of the network attack route and was able to effectively select the network route by providing the network route priority according to the risk score.
Optimizing small channel 3D convolution on GPU with tensor core

In many scenarios, particularly scientific AI applications, algorithm engineers widely adopt more complex convolution, e.g. 3D CNN, to improve the accuracy. Scientific AI applications with 3D-CNN, which tends to train with volumetric datasets, substantially increase the size of the input, which in turn potentially restricts the channel sizes (e.g. less than 64) under the constraints of limited device memory capacity. Since existing convolution implementations tend to split and parallelize computing the small channel convolution from channel dimension, they usually cannot fully exploit the performance of GPU accelerator, in particular that configured with the emerging tensor core. In this work, we target on enhancing the performance of small channel 3D convolution on the GPU platform configured with tensor cores. Our analysis shows that the channel size of convolution has a great effect on the performance of existing convolution implementations, that are memory-bound on tensor core. By leveraging the memory hierarchy characteristics and the WMMA API of tensor core, we propose and implement holistic optimizations for both promoting the data access efficiency and intensifying the utilization of computing units. Experiments show that our implementation can obtain 1.1x–5.4x speedup comparing to the cuDNN's implementations for the 3D convolutions on different GPU platforms. We also evaluate our implementations on two practical scientific AI applications and observe up to 1.7x and 2.0x overall speedups compared with using cuDNN on V100 GPU.
Unsupervised domain adaptation method for segmenting cross-sectional CCA images

Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data.In this work, we present a model-based, unsupervised domain adaptation method that consists of two stages. In the first stage, the network is trained on simulated ultrasound images, which have an accurate ground truth. In the second stage, the network continues training on in-vivo data in an unsupervised way, therefore not requiring the data to be labelled. Rather than using an adversarial neural network, prior knowledge on the elliptical shape of the segmentation mask is used to detect unexpected outputs.The segmentation performance was quantified using manually segmented images as ground truth. Due to the proposed domain adaptation method, the median Dice similarity coefficient increased from 0 to 0.951, outperforming a domain adversarial neural network (median Dice 0.922) and a state-of-the-art Star-Kalman algorithm that was specifically designed for this dataset (median Dice 0.942).The results show that it is feasible to first train a neural network on simulated data, and then apply model-based domain adaptation to further improve segmentation performance by training on unlabeled in-vivo data. This overcomes the limitation of conventional deep learning approaches to require large amounts of manually labeled in-vivo data. Since the proposed domain adaptation method only requires prior knowledge on the shape of the segmentation mask, performance can be explored in various domains and applications in future research.
Privileged multi-task learning for attribute-aware aesthetic assessment

Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo’s aesthetic quality. However, the aesthetic attributes have not been largely and sufficiently exploited for photo aesthetic assessment. In this paper, we propose a novel approach to photo aesthetic assessment with the help of aesthetic attributes. The aesthetic attributes are used as privileged information (PI), which is often available during training phase but unavailable in prediction phase due to the high collection expense. The proposed framework consists of a deep multi-task network as generator and a fully connected network as discriminator. Deep multi-task network learns the aesthetic attributes and score simultaneously to capture their dependencies and extract better feature representations. Specifically, we use ranking constraint in the label space, similarity constraint and prior probabilities loss in the privileged information space to make the output of multi-task network converge to that of ground truth. Adversarial loss is used to identify and distinguish the predicted privileged information of a deep multi-task network from the ground truth PI distribution. Experimental results on two benchmark databases demonstrate the superiority of the proposed method to state-of-the-art.