Reviewerly/exHarmony huggingface dataset:
Detection of tectonic and volcanic deformation as anomalies in InSAR: deep-learning tailored to differential data

There are now more interferograms being generated from global satellite radar datasets than can be assessed by hand.The reliable, automatic detection of true displacement from these data is therefore critical, both for monitoring deformation related to geohazards and understanding solid earth processes.We discuss improvements to an unsupervised, event agnostic method for automatically detecting deformation in unwrapped interferograms.We use an anomaly detection framework that recognises any deformation as "anomalies" by learning the 'typical' spatio-temporal pattern of atmospheric and other noise in sequences of interferograms.Here, we present developments to our prototype model, ALADDIn (Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR) using (1) a self-attention training technique to exploit redundancy in interferogram networks to capture the temporal structure of signals and (2) the addition of synthetic data for training.We evaluate the impact of these developments using two geophysical scenarios: (1) the detection of the same M w 5.7 earthquake used to test our original model (20.03.2019, south-west Turkey), (2) the persistent uplift of Domoyu volcano (17.05.2017(17.05. to 14.12.2018,Argentina), Argentina).We make a quantitative evaluation of the performance of our method using synthetic test data and find that for peak displacements exceeding a few cm and of length-scale greater than a few hundred metres, overall detection accuracy is 80 to 90%.
Analysis on the Employment Situation and Countermeasures of Sino-foreign Cooperative College Graduates in Central and Western China - Based on the Analysis of the School Running Projects of C University

Under the background of the new era, how to serve the "the Belt and Road" strategy and how to train and deliver talents for Sino foreign cooperation in running schools in central and western universities has become a matter of social concern. In addition, under the influence of COVID-19, the employment space of graduates from Sino foreign cooperative education projects has been severely compressed, and the contradiction between talent supply and demand has intensified. Based on the data of the School of International Education of University C from 2015 to 2021, the analysis of employment status shows that the employment rate of graduates has fluctuated due to the impact of external environment in the past seven years. The analysis of the four aspects of employment region, employment type, employment market, and employment quality shows that the comprehensive quality of students needs to be improved, employment information service needs to be strengthened, employment assistance and guidance needs to be improved, and the participation of grassroots employment projects is not high, It is expected to promote the employment of graduates of Sino foreign cooperation projects in colleges and universities in the central and western regions through the establishment of the employability enhancement project, the improvement of employment information service quality, the strengthening of employment assistance and guidance, and the vigorous publicity of grass-roots employment projects.
INCREASING THE EFFICIENCY OF NEURAL NETWORKS IN RECOGNITION PROBLEMS

The article describes the issues of increasing the efficiency of neural networks in terms of their design and coding of input and output signals.The application of multiple signal coding using extrapolation of the input parameters is described on the example of a system of recognition character sequences on images of arbitrary size with a complex background.An effective combination of multiple positional and configuration-competitive coding for various types of signals makes it possible to achieve performance rates of the building number recognition algorithm of up to 74 images per second in the adaptive learning mode and 218 images per second in the recognition only mode.The paper also outlines general recommendations for signal coding in artificial intelligence systems based on neural networks.
Research on Financial Sharing Construction of J Enterprise Group

In recent years, with the epidemic raging, more and more enterprises are trying to help enterprises reduce costs and increase efficiency through the use of technology to achieve the purpose of helping enterprises to rescue. Especially under the background of China's economic development changing from "high growth" to "high quality", how to make full use of "Internet +", "Internet of things +", "big data" and other digital technologies to shape the digital construction of enterprise group finance has become a major topic for managers. As a sword of financial reform driven by digital technology, financial sharing service is widely concerned by managers and financial personnel. Based on the construction practice of the financial sharing center of Guangxi Communications Investment Group, this paper deeply dissects the internal structure, construction path and transformation effect of the construction of the financial sharing center, and looks forward to the future development direction of the financial sharing center of the enterprise group, with a view to providing reference for similar group companies to build financial sharing centers.
A Multi-modal Feature Fusion-based Approach for Mobile Application Classification and Recommendation

&lt;p&gt;With the rapid growth of the number and type of mobile applications, it becomes challenging to accurately classify and recommend mobile applications according to users&amp;rsquo; individual requirements. The existing mobile application classification and recommendation methods, for one thing, do not take into account the correlation between large-scale data and model. For another, they also do not fully exploit the multi-modal, fine-grained interaction features with high-order and low-order in mobile application. To tackle this problem, we propose a mobile application classification and recommendation method based on multi-modal feature fusion. The method firstly extracts the image and description features of the mobile application using an &amp;ldquo;involution residual network + pre-trained language representation&amp;rdquo; model (i.e. the TRedBert model). Afterwards, these features are fused by using the attention mechanism in the transformer model. Then, the method classifies the mobile applications based on the fused features through a softmax classifier. Finally, the method extracts the high-order and low-order embedding features of the mobile app with a bi-linear feature interaction model (FiBiNET) based on the classification results of the mobile app, by combining the Hadamard product and inner product to achieve fine-grained high-order and low-order feature interaction, to update the mobile app representation and complete the recommendation task. The multiple sets of comparison experiments are performed on Kaggle&amp;rsquo;s real dataset, i.e., 365K IOS Apps Dataset. And the experimental results demonstrated that the proposed approach outperforms other methods in terms of Macro F1, Accuracy, AUC and Logloss.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
IAMPDNet: Instance-aware and Multi-part Decoupled Network for Joint Detection and Embedding

&lt;p&gt;Video surveillance applications play an important role in smart cities. Recently, intelligent video surveillance methods have been widely investigated to address large-scale video data, among which multi-object tracking (MOT) is the most popular method, which aims to track every object appearing in the video for monitoring. MOT is essential for deep intelligent perception. Considering inference speed, joint detection and embedding (JDE) has become a new paradigm for MOT. JDE is to obtain detection results and object features through one forward propagation. However, most JDE models lack instance awareness ability and multi-part feature extraction ability, which may lead to the lack of discrimination of extracted instance features. To address these problems, in this paper we propose an instance-aware and multi-part decoupled network (IAMPDNet), which can perceive all instances in the environment and extract multi-part features from the instances. Specifically, our IAMPDNet consists of three key modules: a complementary attention module used to perceive all instances in the environment, a feature extraction module used to decouple multi-part features from the instances, and an adaptive aggregation module used to fuse multi-level features of instances. Extensive experiments on multiple MOT benchmarks demonstrate that our IAMPDNet achieves higher tracking accuracy and lower identity switches against recent MOT methods.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Memory-Aware Spark Cache Replacement Strategy

&lt;p&gt;Spark is currently the most widely used distributed computing framework, and its key data abstraction concept, Resilient Distributed Dataset (RDD), brings significant performance improvements in big data computing. In application scenarios, Spark jobs often need to replace RDDs due to insufficient memory. Spark uses the Least Recently Used (LRU) algorithm by default as the cache replacement strategy. This algorithm only considers the most recent use time of RDDs as the replacement basis. This characteristic may cause the RDDs that need to be reused to be evicted when performing cache replacement, resulting in a decrease in Spark performance. In response to the above problems, this paper proposes a memory-aware Spark cache replacement strategy, which comprehensively considers the cluster memory usage, RDD size, RDD dependencies, usage times and other information when performing cache replacement and selects the RDDs to be evicted. Furthermore, this paper designs extensive corresponding experiments to test and analyze the performance of the memory-aware Spark cache replacement strategy. The experimental data show that the proposed strategy can improve the performance by up to 13% compared with the LRU algorithm in different scenarios.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Time-based Calibration: A Way to Ensure that Stitched Images are Captured Simultaneously

&lt;p&gt;With the rapid development of modern science and technology, people&amp;rsquo;s demand for such information as images and videos is also growing, and the requirements for image and video quality, clarity and view range are also increasing. Therefore, the construction of high-resolution, wide-view panoramic video has also gradually become a hot research topic. Video stitching technology is an increasingly popular research direction in the field of graphics, and the approach addresses the problem of a limited range of views due to a single device capture. Many researchers have proposed various algorithms for video stitching and achieved good stitching results, but the research on time synchronization calibration of different video sources is not yet well developed. This thesis proposes a multi-source video frames calibration technique based on external information sources for solving the problems of ghosting and cutting when stitching with different video sources. The proposed method calibrates the video stitching by introducing an information source and calculating the time difference of different devices. The error of the calibrated video stitching is less than 33 ms, which can guarantee the quality of the spliced video.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The effect of puzzle game-based training in coronary artery bypass graft surgery on the level of knowledge and cognitive function of operating room technology students: A quasi-experimental study

Abstract Background: Since traditional training methods have not been able to provide effective training to operating room students and due to the advancement of training technology,high quality learning requires an advanced training program and a change in the training approach towards empowering students with a self-directed method,seems necessary. Objectives :determining the effect of puzzle game-based training in CABG surgery on the level of knowledge and cognitive function of operating room technology students. Methods and materials: This study was conducted as a quasi-experimental single-group pre-test-post-test,during which,after designing an online puzzle game including various stages of surgery(from the preparation of the patient to surgical sutures and the necessary equipment to perform each stage),18 persons from the third-year operating room technology students who met the inclusion criteria entered the study and participated in the knowledge and cognitive function test, whose validity and reliability were measured, before the intervention and 14 days after the intervention(using a puzzle game).Data were analyzed using descriptive and Wilcoxon statistical tests. Results: After 2 Participants were excluded,16 persons(93.80 percent)of the students present in the study were woman and their average scores of the end of semester theoretical course of cardiac surgery technology were 15.19±2.30.The average scores of knowledge and cognitive function of students in the post-intervention phase were significantly higher than the pre-intervention phase(P&lt;0.0001). Conclusion: The use of puzzle game for teaching CABG surgery is effective in improving the knowledge and cognitive function of students and it can be used as a supplementary training tool to prepare students for operating room technology before entering the clinical environment,so that can have a more effective presence in the CABG surgery room.
Incorporating Multi-hypotheses as Soft-Templates in Neural Headline Generation

Abstract Neural models are widely being applied to headline generation. Template-based methods are one promising direction to overcome the shortcomings of the neural headline generation (NHG) model in generating duplicate or extra words. Previous work often retrieves related headlines from the training data and adopts them as the soft template. In this paper, we propose an alternative approach: we exploit the NHG model uncertainty to generate multiple diverse hypotheses and use these as soft-template to improve the model performance. Due to the computation resource limitation, we train our models on Alibaba Cloud, which is one of the largest public cloud providers in the world and operates in various countries. Experiments on English headline generation tasks show that incorporating multi-hypotheses as soft templates provides a significant performance boost over the strong baselines.
A collaborative path planning method for mobile cable-driven parallel robots in a constrained environment with considering kinematic stability

Abstract Mobile cable-driven parallel robot (MCDPR) is a variant of cable-driven parallel robots (CDPRs) by mounting several mobile bases to replace the conventional fixed frame. The novel modification of adding mobile bases leads MCDPRs being highly flexible and have great potential for complex environments. However, the issue of coupled mobile bases introduces actuated kinematic redundancies which present challenges for path planning. In this paper, we propose a collaborative path planning method for MCDPRs, and it allows the robot to deal with complex internal and external constraints in a high-dimensional state space efficiently. The proposed method quickly generates feasible paths for coupled mobile bases using the adaptive goal-biased rapidly exploring random tree (RRT) method, in which the adaptive sampling method is developed to enhance efficiency. Based on the feasible path of the mobile base, we proposed a grid-based search method to determine the position of the end-effector with considering the stability and kinematic performances. Furthermore, the planned paths are post-processed with the cubic splines to obtain continuous profiles for the robot. Finally, the proposed method is validated through the dynamic simulation software (CoppeliaSim) and experiments based on a MCDPR prototype with an eight-cable-driven parallel robot mounted on four mobile bases.
Retracted: Research on a Good Life and Coordinated Development Based on Intelligent Communication and k-Means Algorithm

[This retracts the article DOI: 10.1155/2022/4525551.].
An Overview of Virtual Reality

Virtual Reality (VR) has existed for many years; however, it has only recently gained wide spread popularity and commercial use. This change comes from the innovations in head mounted displays (HMDs) and from the work of many software engineers making quality user experiences (UX). In this work we present a brief history, current research areas, and areas for improvement in virtual reality
Preparation of Human-Testing Experiment to Determine Key Features for Augmented Reality Applications to Succeed

The evolution of mobile technology has placed augmented reality (AR) into the hands of previously inaccessible users. AR, which previously required specialized hardware devices, is now capable of running on most smartphones and tablets. Due to the abundance of AR capable devices, the medium is being explored by developers to create novel applications for various purposes, such as entertainment and education. With an ever-growing supply of AR applications, only a minority ever flourish. Through a thorough investigation of fields currently using AR, this paper hypothesizes the following are key features to a successful AR application: safety in the real world, visualization of information, affordances of virtual objects, and the use of a real-world environment. To design quality experiments proficient in evaluating effectiveness of the hypothesized features, a deep dive into exemplary experiment design and subsequent pitfalls was conducted. With this information, this paper presents the methodology of the proposed experiments that yield quantitative feedback for each feature, as well as safety and privacy forms for participants. Once conducted, these experiments will yield results that may impact the future of AR application development.
EA-NET: A Hybrid and Ensemble Multi-Level Approach For Robust Anomaly Detection

In the current world, the applications of anomaly detection range from fraud detection to diagnosis in the medical area. Most of the current methodologies are applicable only when a particular dataset pertains to certain assumptions and a distinct domain. Such assumptions require prior knowledge of the dataset. The training development cycle time to find the best single model is time-consuming and challenging. Unsupervised anomaly detection methods do not use the target label for training. However, they result in high false positive rates. In this paper, we address the problem of the ensemble anomaly detection approach that generalizes well across multiple domains. We design a multi- level hybrid approach. At the first level, we train several weak classifiers (weak one class classifiers). Next, we utilize deep learning-based AutoEncoder to reduce the dimension of the dataset. These are the two sets of hybrid features. Next, different one-class classifiers have their strength and limitations. Thus, we propose an adaptive weightage approach that gives the weight to each classifier. Next, this input is passed to the second level. At this level, we have a deep neural network that learns the patterns of the dataset and generates an adaptive dynamic threshold to discriminate the input feature as an anomaly or benign. The major benefit of this approach is the low false-positive rate. The training time is reduced due to the reduction of the input feature dimensions at the first level.
Creativity in the 4th Industrial Curricula with Artificially Intelligent Technologies

Artificial intelligence has become pervasive in the Fourth Industrial Revolution (4IR) and is challenging traditional notions of what it means to be creative. This paper suggests newer and nuanced ways of conceiving creativity in the 4IR era dominated by artificially intelligent systems that are capable of creative outputs. An integrative literature review of the notion of creativity is done to ascertain what it entails to be creative in the 4IR learning context. The Actor-Network Theory is used to sustain a proposed framework for understanding creativity in the 4IR skills development context. It is argued that skills development in the 4IR is happening in an era where humanity is deeply entangled with intelligent machines. A proposal is put suggesting that creativity in learning contexts should go beyond the confines of the mind contrary to traditional pedagogical thinking. Creative learning in the 4IR should be driven by thought processes, human beings’ inherent crafting capabilities, and the computing power of artificial intelligence. Consequently, the design of learning programmes and skills development in the 4IR should occur in a framework that appreciates the fact that creative abilities have moved past capabilities of the human mind. In addition to mental acts, creativity involves the human beings’ manipulative abilities as well as the computing power of artificially intelligent systems. 4IR researchers are encouraged to develop newer lenses and paradigms of understanding and defining creativity in the 4IRskills development context taking into consideration the pervasiveness and creative capabilities of artificially intelligent technologies. The age of the machines has arrived, much of what is taught, and learned, and the creative approaches used to arrive at solutions is changing together with what it means to be creative. Research effort should be directed towards developing educational methodologies that shift skills development systems, so stuck in traditional pedagogies that prime the human mind as the custodian of creativity, to philosophies and theories that are compatible with the 4IR such as post-humanism and connectivism
Graph-It: Design and implementation of a Graph Theory toolbox and visualization application

Graph-It is a Graph Theory toolbox and graph visualization desktop application for Windows, macOS, and Linux for students, teachers, and researchers, since creating graphs by hand and running graph algorithms repeatedly on the graphs can be time consuming. Users can create graphs, modify graphs, save graphs, and run various graph algorithms on the graphs and view the results. The application is built using Unity, utilizing C# for both the front end and back end. Graph-It aims to provide users an easy-to-use and efficient application for creating and running various algorithms on multiple graphs.
A Bucket-Based Data Pre-Processing Method for Encrypted Video Detection

As the number of video streaming platforms is growing, the risk factor associated with illegal and inappropriate content streaming is increasing exponentially. Therefore, mon- itoring such content is essential. Many researches have been conducted on classifying encrypted videos. However, most existing techniques only pass raw traffic data into clas- sification models, which is an ineffective way of training a model. This research proposes a bucket-based data pre-processing technique for a video identification in network traffic. The bucketed traffic is then incorporated with a fine-tuned word2vec-based neural net- work to produce an effective encrypted video classifier. Experiments are carried out with different numbers and sizes of buckets to determine the best configuration. Furthermore, previous research has overlooked the phenomenon of concept drift, which reduces the effec- tiveness of a model. This paper also compares the severity of concept drift on the proposed and previous technique. The results indicate that the model can predict new samples of videos with an overall accuracy of 81% even after 20 days of training.