Reviewerly/exHarmony huggingface dataset:
Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.
An Intelligent Question-Answering Model over Educational Knowledge Graph for Sustainable Urban Living

With the development of education informatization and the accumulation of massive educational resources and teaching data in urban environments, educational knowledge graphs that provide good conditions for developing data-driven intelligent education have been proposed. Based on such educational knowledge graphs, the question-answering method can provide students with immediate coaching and significantly increase their learning interest and productivity. However, there is little research on knowledge graph question-answering focused on the educational field. Students tend to consult complex questions that require reasoning; however, the existing QA system cannot satisfy their complex information needs. To help improve sustainable learning efficiency, we propose a novel intelligent question-answering model applied in smart cities, which can reason over the educational knowledge graph to locate the answers to given questions. Our approach uses a highly expressive bilinear graph neural network technology to perform forward reasoning, utilizing the contextual information between graph nodes to improve reasoning ability. On this basis, we propose two-teacher knowledge distillation. We construct two distinct teacher networks by combining forward and backward reasoning, then incorporate the intermediate supervision signals from the two networks to guide the reasoning process, thereby mitigating the phenomenon of spurious path reasoning. Extensive experiments on the MOOC Q&amp;A dataset prove the effectiveness of our approach.
Trajectory Planning of USV: On-Line Computation of the Double S Trajectory Based on Multi-Scale A* Algorithm with Reeds–Shepp Curves

Trajectory planning aims to provide a time-related control target that contains the concerned states. For an underactuated surface vehicle (USV), planning challenges include limitations on curvature, speed, acceleration, and jerk. These challenges are relevant for the precise control of USVs. To solve these problems, an on-line double S method and multi-scale A* trajectory planning algorithm is proposed by integrating Reeds–Shepp curves (RSC), where a quad-tree-based graph is used for path planning and collision detection. Simulations illustrate that the proposed method has a better performance than the smooth rapid-exploration random tree (smooth-RRT) method and jump point search (JPS) method, and that path length and the state limitations are satisfied.
A Multi-Objective Mission Planning Method for AUV Target Search

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in an underwater space is the focus of the paper. First, the task assignment problem is defined as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two competitive and non-commensurable optimization goals, the total sailing distance and the turning angle generated by an AUV to completely traverse threat points in the planned order, are taken into account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed, are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP. With the help of the fuzzy set, the ants that have moved along better paths are screened and the pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile pheromones on preferred paths are produced as the number of iterations of the ACO algorithm increases, thus providing an effective way for the algorithm to escape from a local minimum in the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and advantages of the proposed algorithm and the influence of critical parameters is also analyzed and demonstrated.
Exploring science pedagogy on the web 2.0/mobile border: Teachers’ views of a mobile wiki-based inquiry approach

This paper reports on teachers’ experiences of a 10-week, wiki-based, predominantly mobile science education intervention for secondary school students. It was designed to shift traditional pedagogical approaches towards more inquiry-based approaches by combining the strengths of wikis and mobile smart devices. The four participating Macau science teachers, with shared responsibility for 250 students, noted greater student engagement and reported some shifts towards more constructivist pedagogy and more active student learning. Evidence emerged of the value of personalization, collaboration, online feedback, and authentic learning, but the lack of mobile optimization of the wiki platform negatively impacted the degree of personalization, collaboration and authenticity. Lessons were learned about the need for educational technology interventions to carefully consider the intersection between software and hardware; the reasons for the recent decline of wikis as educational tools; and the ongoing educational value of web 2.0-style collaboration in our increasingly mobile era.
Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.
Role of Attention and Design Cues for Influencing Cyber-Sextortion Using Social Engineering and Phishing Attacks

Cyber sextortion attacks are security and privacy threats delivered to victims online, to distribute sexual material in order to force the victim to act against their will. This continues to be an under-addressed concern in society. This study investigated social engineering and phishing email design and influence techniques in susceptibility to cyber sextortion attacks. Using a quantitative methodology, a survey measured susceptibility to cyber sextortion with a focus on four different email design cues. One-way repeated measures ANOVA, post hoc comparison tests, Friedman nonparametric test, and Spearman correlation tests were conducted with results indicating that attention to email source and title/subject line significantly increased individuals’ susceptibility, while attention to grammar and spelling, and urgency cues, had lesser influence. As such, the influence of these message-related factors should be considered when implementing effective security controls to mitigate the risks and vulnerabilities to cyber sextortion attacks.
First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high.
An improved generative adversarial networks for remote sensing image super-resolution reconstruction via multi-scale residual block

Existing image super-resolution algorithms still suffer from the problems of not extracting rich image features and losing realistic high-frequency details. In order to solve these problems, this paper proposes an improved generative adversarial network algorithm for super-resolution reconstruction of remote sensing images by multi-scale residual blocks. The original generative adversarial network (GAN) structure is improved and multi-scale residual blocks are introduced in the generator to fuse features at different scales. After extracting the parallel information of multi-scale features, information is exchanged between multi-resolution information streams to obtain contextual information through spatial and channel attention mechanisms, and multi-scale features are fused according to the attention mechanism. In the discriminator, the concept of relative average GAN (RaGAN) is introduced, and the loss function of the network is redesigned so that the discriminator can predict relative probabilities instead of absolute probabilities thus enabling clear learning of edge and texture details. Experimental results show that the proposed method in this paper significantly outperforms state-of-the-art (SOTA) methods in terms of both subjective and objective metrics.In three test datasets, compared with SOTA methods, the Peak Signal to Noise Ratio(PSNR) is improved by a maximum of 1.18 dB, 0.84 dB and 1.29 dB respectively, and the Structural Similarity Index (SSIM) is improved by 0.0264, 0.0077 and 0.0109 respectively in scale of 2, 3 and 4 times images super-resolution.The model proposed in this paper effectively improved the super-resolution re-construction results of remote sensing images.
Traffic Sign Detection and Classification on the Austrian Highway Traffic Sign Data Set

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated data set of images of almost all traffic signs on Austrian highways in 2014, and corresponding images of full traffic scenes they are contained in. Altogether, the data set consists of almost 7500 scene images with more than 28,000 detailed annotations of more than 100 distinct traffic sign classes. It covers diverse environments, ranging from urban to rural and mountainous areas, and includes many images recorded in tunnels. We further evaluate state-of-the-art traffic sign detectors and classifiers on ATSD to establish baselines for future experiments. The data set and our baseline models are freely available online.
Self-Perceived Job Performance as a Mediator of the Effects of Academic Satisfaction and Quality on Loyalty

Globally, there is increased customer mobility and competition within the higher education sector. As such, university management and administration practices should consider academic satisfaction, quality and loyalty as important factors to influence graduate job performance. The study was conducted to see if self-perceived job performance had a role in mediating the effect of academic satisfaction and perceived academic quality on academic loyalty. Data was collected from 714 respondents using a cross-sectional survey. The covariance-based structural equation modelling was used to test the hypotheses. According to the study results, self-perceived job performance partially mediates the effect of both academic satisfaction and academic quality on academic loyalty. The study findings emphasise the importance of graduate quality and satisfaction in influencing loyalty. Thus, the higher education sector should take cognisance of self-perceived job performance as this also influences academic loyalty.
Learning to Retrieve User Behaviors for Click-through Rate Estimation

Click-through rate (CTR) estimation plays a crucial role in modern online personalization services. It is essential to capture users’ drifting interests by modeling sequential user behaviors to build an accurate CTR estimation model. However, as the users accumulate a large amount of behavioral data on the online platforms, the current CTR models have to truncate user behavior sequences and utilize the most recent behaviors, which leads to a problem that sequential patterns such as periodicity or long-term dependency are not contained in the recent behaviors but in far back history. However, it is non-trivial to model the entire user sequence by directly using it for two reasons. Firstly, the very long input sequences will make online inference time and system load infeasible. Secondly, the very long sequences contain much noise, thus making it difficult for CTR models to capture useful patterns effectively. To tackle this issue, we consider it from the input data perspective instead of designing more sophisticated yet complex models. As the entire user behavior sequence contains much noise, it is unnecessary to input the entire sequence. Instead, we could just retrieve only a small part of it as the input to the CTR model. In this article, we propose the U ser B ehavior R etrieval (UBR) framework which aims at learning to retrieve the most informative user behaviors according to each CTR estimation request. Retrieving only a small set of behaviors could alleviate the two problems of utilizing very long sequences (i.e., inference efficiency and noisy input). The distinguishing property of UBR is that it supports arbitrary and learnable retrieval functions instead of utilizing a fixed pre-defined function, which is different from the current retrieval-based methods. Offline evaluations on three large-scale real-world datasets demonstrate the superiority and efficacy of the UBR framework. We further deploy UBR at the Huawei App Store, where it achieves 6.6% of eCPM gain in the online A/B test and now serves the main traffic in the Huawei App Store advertising scenario.
Mobile application for advertising faculty educational services

The article shows the use of augmented reality technology and the creation of a mobile application for advertising educational services of the Faculty of Information and Computer Technologies of the Zhytomyr Polytechnic State University, Ukraine. The research method is the use of augmented reality technology to create mobile applications using the Vuforia platform. The developed application is intended for use on devices running on the Android operating system. As a result of this work, a mobile software application was designed and created to promote educational services of the Faculty. Animated 3D models created using the Blender package, cards with information on each specialty and animated videos were created. There is a possibility of direct transition from the mobile application to external information resources. The proposed development can be used to promote the educational opportunities of the educational institution, increase the information value of advertising paper advertising and greater interest of entrants. Mobile application prototype tested in production environment and can be used by multiple faculties. For use by other faculties, information about the relevant specialties must be added to the program. As a result of using the developed mobile application, the number of applications from entrants to the faculty has increased by 40 percent over the past three years.
Resources of the research-pedagogical community: Administrative and mobilization approaches

The study of the research-pedagogical community’s (RPC) resources of Russian universities in the sociological perspective is determined by the new conditions for the implementation of the development strategy of the Russian higher education. An adequate assessment and the full use of the RPC’ potential, including its unused resources, are of particular importance for Russian universities experiencing serious deficits today. The article proposes a new ‘optics’ for assessing the ability of the university management to mobilize traditional and unused resources of the RPC. The article aims at describing the administrative and mobilization management approaches to the analysis of the RPC’ resources in Russian universities. The authors consider the problem at the intersection of sociology of management and sociology of higher education. The article is based on the secondary analysis of the research data collected by the authors since 2016. The authors also consider documents of strategic planning for the development of higher education and science in order to identify the priorities of the RPC’s academic development and the requirements for them in new conditions. The research is based on the analysis of the statistical data on the main indicators for the development of the Russian higher education in 2014-2021, such as databases of the Monitoring of the effectiveness of higher education organizations, statistical yearbooks ‘Indicators of Education’, data of the Monitoring of the Economics of Education of the Higher School of Economics, and other open sources. Thus, based on the theoretical and empirical research of the higher education practices, the authors define the concept and structure of the RPC’s resources; explain the need for a managerial approach in universities as aimed at mobilizing the unused RPC’s resources; reveal the limitations of the university administrative decisions in assessing and use of the RPC’s resources. The authors make three main conclusions: first, about the meaning of the concept and structure of the RPC’s resources as an important means of managerial analysis; second, about the need for a model of the mobilization university management; third, about the consequences of the dominance of the administrative approach in assessing and use of the RPC’s resources.
Performance Comparison of Backbone Networks for Multi-Tasking in Self-Driving Operations

In the era of big data, increased focus has been on improving neural network based Deep Learning models. This led to various classification networks which can be used as a backbone in multi-task learning. However, depending on the selected backbone, multi-tasking performance differs. While given backbone network shows better performance on a detection task, does not mean such performance generalizes in segmentation task as well. Detailed investigations should be conducted to achieve best inference speed-accuracy trade-off prior to implementing a single neural network, which handles multiple tasks. In this research, the performance comparison among EfficientNet, ResNet101, VGG16, ResNet50 and MobilenetV2 on the Berkeley Driving Dataset (BDD100K) for autonomous driving using multi-tasking architecture are provided. Backbones that offer best time-accuracy trade-off for multi-task learning are evaluated. Implemented architecture contains three most crucial tasks in self-driving operations, object detection, drivable area segmentation and lane detection. EfficientNet based model showed the best mAP on the object detection task, as well as on the segmentation tasks, extracting both the long and wide roads with accurate lane lines. The model with MobilenetV2 backbone however, demonstrates the fastest inference speed with relatively lower performance in all tasks.
The Improvement of 1D Gaussian Blur Filter using AVX and OpenMP

A Gaussian blur filter is applied to produce the new pixel value of an image using a Gaussian function. It is a low-pass filter and modifies the input image pixels by convolution the matrix with a Gaussian function which becomes computationally intensive work for image processing. In this paper, the examination of performance, energy/power consumption between the original 1D, 2D, and modified 1D convolution used in the Gaussian blur filter is proposed by using Advanced Vector Extension (AVX) intrinsics, loop unrolling, and OpenMP on multi-core architecture. The main contribution aims to parallelize the modified 1D convolution on parallel processing cores to enhance the performance and reduce the processing time by focusing on the Gaussian filter function for contrast the original 1D and 2D convolution. The improved performance gains approximately 1.3 times, and 6.9% in energy and 3.2% in power consumption is reduced respectively when testing on Intel core i7 multi-core while keeping up the energy awareness of different filtering kernels.