Reviewerly/exHarmony huggingface dataset:
Multivariate Time Series Anomaly Detection Based on Reconstructed Differences Using Graph Attention Networks

Today, many real-world applications generate the amount of multivariate time series data. Monitoring those data and detecting some meaningful events early is important. As one of those tasks, interest in anomaly detection has grown. In recent research, some authors conducted anomaly detection in multivariate time series data by using graph attention networks to capture relationships among series and timestamps respectively. And another author suggested some connections between timestamps called Spatio-temporal connections. In this paper, we combine two ideas jointly and propose another multivariate time series anomaly detection method using series differences between adjacent timestamps. By using the proposed method, we conduct anomaly detection on two public datasets and compare the performance with other models. Also, to check for the possibility of operation on the edge environment, we measure the throughput of our proposed method in the IoT edge gateway that has restricted resources.
Fuzzy Gain Scheduling of Artificial Potential Fields for Online Path Planning and Obstacle Avoidance of an Aerial Robot

In this research, the performance of the traditional artificial potential field (TAPF) method in online path planning & obstacle avoidance of an aerial robot has been improved. In the TAPF method, the attraction coefficient of the target field and the repulsion coefficient of the obstacle fields are constant, which due to the agile dynamics of aerial robots may cause problems such as hitting an obstacle, missing the target with a long distance, and the path fluctuation when passing through narrow paths. The fuzzy artificial potential field (FAPF) method was presented to solve the mentioned problems. This method determines artificial potential field coefficients online using fuzzy logic. This fuzzy logic schedules artificial potential field coefficients by considering the distance and the line-of-sight angle between the UAV and target/obstacle. The simulation results show the performance and efficiency of the FAPF compared with the TAPF in the path planning problem.
A Deep Reinforcement Learning Framework for Multi-Stage Optimized Object Detection

Nowadays, with the significant development of artificial intelligence and especially after the emergence of deep learning, vision systems specifically the vision of robots have become more interesting. One of the most important challenges of computer vision and robotics is object detection. In fact, high computational complexity bothers object detection techniques that rely on region proposals or predefined anchors. We propose an optimized method upon a deep reinforcement learning framework along with multi-stage approaches for object detection as well as train two intelligent agents for a more accurate search of objects and localize them in images. Several candidate bounding-boxes are proposed to the algorithm in the first stage, and the perfect boxes are selected for the final boxes around the objects in the second stage using an optimal approach. Our approach was assessed using two renowned datasets in the object detection field, PASCAL VOC 2007 and PASCAL VOC 2012. According to these evaluations and experiments, the proposed method not only outperforms similar non-region proposal methods but also does well detecting small objects.
Exploring the application of interactive video for higher education in the post-epidemic era

In the post-epidemic era, how to use technology for effective online teaching interaction is still a hot topic in the academic community. The regression equation model of online teaching effect formed by the questionnaire method and processed by SPSS26.0 software is used to understand the influencing factors of the key teaching effect of the current online teaching process and to provide a basis for the technology development of interactive video. Combining the current teaching situation with literature review, we explore the appropriateness of the integration of online teaching and interactive video based on the theory of interaction influence distance, and then propose the significance of the teaching application of interactive video. The aim is to improve the form of online teaching, improve the quality of teaching, and form effective interaction between teachers and students and students in the classroom.
Teaching practice and evaluation of healing garden design based on Virtual Simulation Platform

This paper discusses the application effect of virtual simulation platform in healing garden design teaching of University environmental design major. Based on the research of relevant teaching theories, this paper compares and analyzes the students’ achievements in traditional classroom teaching and virtual simulation platform teaching, and uses online questionnaire to investigate the students’ evaluation of self- directed learning ability. The research shows that the use of virtual simulation platform teaching can significantly improve the examination results and autonomous learning ability, stimulate students’ learning interest, improve students’ autonomous learning ability, and play a positive role in promoting students’ design practice ability.
Exploration and Practice of Hybrid Teaching Reform Based on "Principles of Operating System" of Excellent Offline Open Courses

The course "Principles of Operating System" is a basic compulsory course for various majors in computer science. Taking the School of Big Data and Artificial Intelligence of Anhui Xinhua University as the background, this paper explores the teaching reform of excellent offline open courses from the aspects of curriculum construction introduction, curriculum construction objectives, teaching reform process and organization, and explains the effects achieved after the implementation of curriculum construction.
A Study on Teaching Reform Based on the Management Information System of Organic Chemistry Course

The course Organic Chemistry is an important foundation course in the subject of chemistry. However, the Organic Chemistry course has problems such as the lack of integration of ideological and political education into the classroom, the inadequate feedback system after the class and the absence of a more effective information-based teaching mode suitable for the study of Organic Chemistry. Based on this situation, this paper applies computer application technology to the teaching mode of the Organic Chemistry course and designs and develops the Organic Chemistry course management information system. The system aims to improve students' course learning efficiency, develop a strong sense of national honour, enhance teachers' classroom teaching effectiveness and promote the development of information technology for course teaching in higher education institutions.
Improving Graph Representation Learning with Distribution Preserving

Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly expressive GNN has the ability to generate discriminative graph representations. However, in the end-to-end training process for a certain graph learning task, an expressive GNN could generate graph representations overfitting the training data for the target task but losing information important for the model generalization, thus reducing the generalizability. In this paper, we propose Distribution Preserving GNN (DP-GNN), a GNN framework that can improve the generalizability of expressive GNN models by preserving several kinds of distribution information in graph representations and node representations. Besides the generalizability, by applying an expressive GNN backbone, DP-GNN can also have high expressive power. We evaluate the proposed DP-GNN framework on multiple benchmark datasets for graph classification tasks. The experimental results demonstrate that our model achieves state-of-the-art performances.
Generalized Few-Shot Node Classification

For real-world graph data, the node class distribution is inherently imbalanced and long-tailed, which naturally leads to a few-shot learning scenario with limited nodes labeled for newly emerging classes. Existing efforts are carefully designed to solve such a few-shot learning problem via data augmentation, learning transferable initialization, to name a few. However, most, if not all, of them are based on a strong assumption that all the test nodes must exclusively come from novel classes, which is impractical in real-world applications. In this paper, we study a broader and more realistic problem named generalized few-shot node classification, where the test samples can be from both novel classes and base classes. Compared with the standard fewshot node classification, this new problem imposes several unique challenges, including asymmetric classification and inconsistent preference. To counter those challenges, we propose a shot-aware graph neural network (STAGER) equipped with an uncertainty-based weight assigner module for adaptive propagation. To formulate this problem from the meta-learning perspective, we propose a new training paradigm named imbalanced episodic training to ensure the label distribution is consistent between the training and test scenarios. Experiment results on four real-world datasets demonstrate the efficacy of our model, with up to 14% accuracy improvement over baselines.
A Bimanual Flick-Based Japanese Software Keyboard Using Direct Kanji Input

Direct kanji input is a Japanese text input method that is totally different from kana-kanji conversion commonly used in Japan. Direct kanji input is said to enable the user to efficiently input kanji characters after mastering it. In this paper, we propose a bimanual flick-based Japanese software keyboard for a tablet that uses direct kanji input. Once the user masters it, the user can efficiently input kanji characters while holding a tablet with both hands. We present three kanji layouts that we designed for this software keyboard. We show the results of the three experiments that we conducted to evaluate the performance of this keyboard. In the first experiment, we compared it with exiting software keyboards. In the second experiment, we evaluated how much the user can learn it by using its learning support functions. In the third experiment, one of the authors continuously used it for 15 months.
Practical Research on Blended Teaching in Higher Vocational Institutes based on ICVE Platform : —Take Artificial Intelligence Data Processing Course as an Example

Based on ICVE platform, taking artificial intelligence data processing course as an example, this paper explores and practices blended teaching. Firstly, the course teaching design is carried out based on the working process, and then according to the requirements of the course and the characteristics of higher vocational institute students, the teaching practice of online and offline, theory and practice is carried out through three parts: before class , in class and after class practice. The survey results show that the blended teaching model has a positive impact on improving higher vocational institute students' learning interest and effect.
Analysis on the Synergistic Effect of Cultural Courses in Talent Cultivation in Applied Engineering Colleges : ——Taking Mining Engineering Major in Xinjiang Institute of Engineering as an Example

It is traditionally believed that the role of cultural courses in application-oriented engineering colleges is weak. Based on the comprehensive analysis of the course learning and employment data of mining engineering major in Xinjiang Institute of Engineering during the 2016-2021 academic year, this paper clarifies the synergistic effect of cultural courses in talent cultivation in application-oriented engineering colleges. It’s confirmed that the elements of red culture can play a comprehensive role in talent cultivation by integrating into the curriculum construction of humanities and social sciences.
Few-shot Anomaly Detection and Classification Through Reinforced Data Selection

Due to the scarcity of anomalies, deep anomaly detection models are predominately trained in an unsupervised or semi-supervised manner depending on the availability of a small number of labeled samples. Currently, most unsupervised approaches detect anomalies by identifying the deviate patterns, and some semi-supervised studies also use labeled anomalies to improve performance. However, few studies have focused on how to take advantage of potential anomalies in an easily obtained and large-scale unlabeled dataset. Meanwhile, in a semi-supervised setting, although we assume having a small number of labeled anomalies, the task of anomaly classification is under-exploited. In this work, considering the problem of anomaly detection and classification by giving limited labeled samples as well as a large number of unlabeled samples, we propose a few-shot anomaly detection and classification model through reinforced data selection (FADS), a novel framework that iteratively improves the performance of anomaly detection and classification by exploring the unlabeled dataset to augment the training set. Experimental results show that FADS is able to improve the performance of anomaly detection and classification with only a few labeled samples initially.
Robust Unsupervised Domain Adaptation from A Corrupted Source

Unsupervised Domain Adaptation (UDA) provides a promising solution for learning without supervision, which transfers knowledge from relevant source domains with accessible labeled training data. Existing UDA solutions hinge on clean training data with a short-tail distribution from the source domain, which can be fragile when the source domain data is corrupted either inherently or via adversarial attacks. In this work, we propose an effective framework to address the challenges of UDA from corrupted source domains in a principled manner. Specifically, we perform knowledge ensemble from multiple domain-invariant models that are learned on random partitions of training data. To further address the distribution shift from the source to the target domain, we refine each of the learned models via mutual information maximization, which adaptively obtains the predictive information of the target domain with high confidence. Extensive empirical studies demonstrate that the proposed approach is robust against various types of poisoned data attacks while achieving high asymptotic performance on the target domain.
Imagery Analysis and Virtual Visualization of Chinese Folk Song Hua'er

As an intangible cultural heritage, Hua’er is a type of folk song which is popular in Northwest China. Traditional researches of Chinese folk songs mainly use qualitative methods, which lack a quantitative perspective. In this paper, digital and computational methods are introduced into the research of Hua’er. Firstly, data mining methods are employed in analyzing the imagery of Hua’er in this research, and it is found that male and female images are often associated with different types of images. Secondly, a characteristic digital resource library of Hua’er has been constructed via virtual reality methods. This work is of great significance for the preservation and dissemination of the folk song Hua’er.
Self-explaining Hierarchical Model for Intraoperative Time Series

Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivariate time series, prohibiting the effective learning of accurate models. The large gaps associated with clinical events and protocols are usually ignored. Moreover, deep models generally lack transparency. Nevertheless, the interpretability is crucial to assist clinicians in planning for and delivering postoperative care and timely interventions. Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series. We further develop an explanation module for the hierarchical model to interpret the predictions by providing contributions of intraoperative data in a fine-grained manner. Experiments on a large dataset of 111,888 surgeries with multiple outcomes and an external high-resolution ICU dataset show that our model can achieve strong predictive performance (i.e., high accuracy) and offer robust interpretations (i.e., high transparency) for predicted outcomes based on intraoperative time series.
An Empirical Analysis on Just-In-Time Defect Prediction Models for Self-driving Software Systems

Just-in-time (JIT) defect prediction has been used to predict whether a code change is defective or not. Existing JIT prediction has been applied to different kind of open-source software platform for cloud computing, but JIT defect prediction has never been applied in self-driving software. Unlike other software systems, self-driving system is an AI-enabled system and is a representative system to which edge cloud service is applied. Therefore, we aim to identify whether the existing JIT defect prediction models for traditional software systems also work well for self-driving software. To this end, we collect and label the dataset of open-source self-driving software project using SZZ (Śliwerski, Zimmermann and Zeller) algorithm. And we select four traditional machine learning methods and state-of-the-art research (i.e., JIT-Line) as our baselines and compare their prediction performance. Our experimental results show that JITLine and logistic regression produce superior performance, however, there exists a room to be improved. Through XAI (Explainable AI) analysis it turned out that the prediction performance is mainly affected by experience and history-related features among change-level metrics. Our study is expected to provide important insight for practitioners and subsequent researchers performing defect prediction in AI-enabled system.
Rethinking RNN-Based Video Object Segmentation

Video Object Segmentation is a fundamental task in computer vision that aims at pixel-wise tracking of one or multiple foreground objects within a video sequence. This task is challenging due to real-world requirements such as handling unconstrained object and camera motion, occlusion, fast motion, and motion blur. Recently, methods utilizing RNNs have been successful in accurately and efficiently segmenting the target objects as RNNs can effectively memorize the object of interest and compute the spatiotemporal features which are useful in processing the visual sequential data. However, they have limitations such as lower segmentation accuracy in longer sequences. In this paper, we expand our previous work to develop a hybrid architecture that successfully eliminates some of these challenges by employing additional correspondence matching information, followed by extensively exploring the impact of various architectural designs. Our experiment results on YouTubeVOS dataset confirm the efficacy of our proposed architecture by obtaining an improvement of about 12pp on YoutTubeVOS compared to RNN-based baselines without a considerable increase in the computational costs.
Estimate Height Weight and Body Mass Index From Face Image Using Machine Learning

Human body photographs contain encodings for gender, weight, and other biometric data in addition to pupil color. When considering all of this information, body weight is a reliable indicator of health problems. Body measurements including height, weight, and body mass index (BMI) are important because they can be used for a variety of purposes like surveillance, re-identification, picture retrieval systems, and healthcare. The majority of earlier research on automated height, weight, and BMI measurement has made use of 3D whole body videos and 2D and full body 2D pictures. A person's weight in proportion to their height is gauged by their BMI. It serves more as a gauge for calculating total body fat. Because it is frequently used to evaluate health issues, BMI is important. Our chances of living a longer, healthier life are said to be increased by having a healthy body mass index (BMI).The BMI range may be identified and classified using picture analysis, which can help individuals forecast credibility, manage their BMI, and lead healthier lives. There hasn't been much discussion on face estimation. Being overweight has been linked to obesity, diabetes, and cardiovascular disease. Weight is also an important health indicator. Using the methods Linear Regression, Ridge Linear Regression, Random Forest Regressor, and Kernel Ridge Regression, CNN algorithm, and Resnet-50 Architecture, we test the feasibility of estimating height, weight, and BMI from single-shot face photos. In order to estimate height, weight, and BMI, we will evaluate these regression models and select the one with the highest test score.
Beyond usage – Selection strategies for EBA programs

Evidence-Based Acquisition (EBA) is a monograph-purchasing model in which a library licenses access to a large corpus of ebooks for a set period of time (typically a year). At the end of the term, the library acquires a number of permanent titles with a set dollar amount. Much has been published about choosing an EBA provider, with the focus primarily on usage during the full-access period. Little has been written about the decision-making process for permanent access monograph purchase. Our analysis of usage data at J. Paul Leonard Library has shown that year-to-year usage of monographs in an EBA program fluctuates markedly, so usage during one year is not a strong indicator of long-term value. Other factors are worth considering. This case report will document the library's overall EBA efforts and the development of a system whereby selectors advised on the purchase of ebooks in five different EBA programs, and the criteria they used in their selection.
Research on The Evaluation of Students’ Online Learning : A case of college students in H Province Based On SPSS 26.0

According to the regression equation model of college students’ online learning engagement developed by the author in June 2021, to analyze questionnaire survey of H Province college students who are carrying out online learning with SPSS26.0. The results show that the overall acceptance of online learning mode by students is relatively high, with more than two-thirds of students having a high recognition of online learning mode. But the students are not very satisfied with the current content and teaching methods online.
A Distributed Framework for Predictive Analytics Using Big Data and MapReduce Parallel Programming

With the advancement of Internet technologies and the rapid increase of World Wide Web applications, there has been tremendous growth in the volume of digital data. This takes the digital world into a new era of big data. Various existing data processing technologies are not consistent and scalable in handling the complexity as well as the large-size datasets. Recently, there are many distributed data processing, and programming models have been proposed and implemented to handle big data applications. The open-source-implemented MapReduce programming model in Apache Hadoop is the foremost model for data exhaustive and also computational-intensive applications due to its inherent characteristics of scalability, fault tolerance, and simplicity. In this research article, a new approach for the prediction of target labels in big data applications is developed using a multiple linear regression algorithm and MapReduce programming model, named as MR-MLR. This approach promises optimum values for MAE, RMSE, and determination coefficient (R2) and thus shows its effectiveness in predictions in big data applications.