Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Iklan dan Brand Ambassador Terhadap Minat Beli Konsumen Pada Layanan Bimbel Online

This research was conducted 1) to determine the partially significant effect of advertising on consumer buying interest in online tutoring services at SMAN 3 Jember. 2) To find out the partially significant effect of brand ambassadors on consumer buying interest in online tutoring services at SMAN 3 Jember. 3) To determine the simultaneous significant effect of advertising and brand ambassadors on consumer buying interest in online tutoring services at SMAN 3 Jember. The population in this study were students of class X and XI at SMAN 3 Jember. The type of research used by the researcher is descriptive research using multiple linear regression analysis method with data processing using the SPSS 22 program. Hypothesis testing in this study uses t test and f test with a value level of a = 5%. The results of this study indicate that 1) partially advertising has a significant effect on consumer buying interest in online tutoring services at SMAN 3 Jember, while 2) brand ambassadors partially have no significant effect on consumer buying interest in online tutoring services at SMAN 3 Jember. 3) Simultaneously, advertising and brand ambassadors have a significant effect on consumer buying interest in online tutoring services at SMAN 3 Jember
Pengaruh Media Sosial, Literasi Ekonomi, dan Kelompok Teman Sebaya Terhadap Perilaku Konsumtif Mahasiswa Pendidikan Ekonomi

The progression of time, combined with technological advancements, has resulted in changes in practically every aspect of existence. Changes in community consumption patterns, particularly among students, are included. Today's students' consumption is driven by personal interests rather than essential necessity. This consumption pattern is influenced by a variety of circumstances. This study aims to determine how the relationship between social media, economic literacy and the environment of students' peers in influencing the consumptive behavior of Economic Education students at Sebelas Maret University, class of 2017-2019. Sampling in this study used a proportionate stratified random sampling technique with 155 students as samples. This study uses quantitative methods with data analysis using t-test and multiple linear regression. The findings of this study show that the factors of social media, economic literacy, and groups of friends of the same age have a positive and significant influence on consumption behavior in economic education students class 2017-2019, with a value of Fcount &gt; Ftable of 18.745 &gt; 2.66.
PENERAPAN METODE PEMBELAJARAN SQ4R UNTUK MENINGKATKAN KETERAMPILAN MEMBACA SISWA DASAR

This study aims to analyze the application of sq4r learning methods to improve the reading skills of basic students. The design of this study is PTK. Research activities are carried out in 2 (two) cycles, each cycle lasts 2 hours of lessons (2 x 45 minutes). Each cycle includes stages: 1) planning, 2) execution of actions, 3) observation and evaluation, 4) analysis and reflection. This research was carried out at SDN Negeri 1 Gadang. The class studied was class V with a total of 36 students. Based on the results of the research described, it can be concluded that by applying the stages of the Survey, Question, Read, reflect, Recite, Review (SQ4R) learning model in the learning process can increase teacher learning activities and student learning activities during the learning process in class V of SDN Negeri 1 Gadang. Changes in the learning process that occur encourage the improvement of student learning outcomes related to reading comprehension material in Indonesian subjects in class V of SDN Negeri 1 Gadang.
Dilemma, Opportunities and Countermeasures of Adult Higher Education in Colleges and Universities in China under the Background of "Double First-class" Construction

The construction of "double first-class" has brought new opportunities and challenges to the development of adult higher education in Colleges and universities in china. For the healthy development of adult higher education in Colleges and universities in china, this paper first studies the development dilemma of adult higher education. It is found that under the background of "double first-class" construction, "to go or to stay", "to scale or to quality" has become a realistic problem that must be considered in adult higher education. Then, starting from the specific deployment of the "double first-class" construction and the basic functions of colleges and universities, this paper studies the relationship between adult higher education and the double first-class construction. The results show that the relationship between the development of adult higher education and the construction of "double first-class" in Colleges and universities is closely linked and complementary. Finally, this paper expounds the ways of developing adult higher education innovatively from the aspects of development orientation, school-running concept, education and teaching.
The Application of VR Technology in the Marketing of Women’s Luxuries

At present, millennials have become the main luxury consumers. With the development of the digital economy, people’s consumption concepts and behaviors are undergoing profound changes. In the digital environment, luxury marketing approaches are facing new challenges and changes. Luxury brands not only need to use digital marketing to tell their brand stories, but also need to form an immersive experience during online marketing. Able to be multisensory, immersive and interactive, virtual reality (VR) technology has become an important innovation in the field of digital marketing. Based on the systematic literature review of digital marketing and marketing of women’s luxuries, this paper analyzes the application of VR technology in the marketing of women’s luxuries.
Research on Security Model Design Based on Computational Network and Natural Language Processing

Human logical thinking exists in the form of language, and most of the knowledge is also recorded and transmitted in the form of language. It is also an important and even core part of artificial intelligence. Communicating with computers in natural language is a long-standing pursuit of people. People can use the computer in the language they are most accustomed to and can also use it to learn more about human language abilities and intelligent mechanisms. The realization of natural language communication between humans and computers means that computers can not only understand the meaning of natural language texts but also express the intentions and thoughts given in natural language texts. This paper designs and studies a computational model for natural language processing (NLP) models for natural language processing. This paper aims to study the design of computing network security model based on natural language processing. This paper proposes three calculation models, which are based on the long-term and short-term memory neural network model (LSTM), FastText model, and text processing model (GCN) based on graph convolution neural network. Several natural language processing models are evaluated and analyzed using four indexes: accuracy, recall, exactness, and F1 vaule. Results show that the performance level of the GCN model is the best. The accuracy of the NLP recognition of this model reaches 86.66%, which is 2.93% and 1.55% higher than the accuracy of the LSTM model and the FastText model, respectively.
Amazon Platform Shopping Review Data Analysis

This paper studies the development of three products on Amazon platform. This paper analyzes the text emotion of the comments on Amazon platform, quantifies the emotional score of each comment based on LSTM, selects relevant indicators of the comment data, establishes a comprehensive evaluation model of the comprehensive praise of the comment, and recommends the products with the greatest potential success for businesses based on the comment data.
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems

Estimating a scene's depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field. This paper proposes a novel, low complexity network architecture for fast and accurate human depth estimation and segmentation in indoor environments, aiming to applications for resource-constrained platforms (including battery-powered aerial, micro-aerial, and ground vehicles) with a monocular camera being the primary perception module. Following the encoder-decoder structure, the proposed framework consists of two branches, one for depth prediction and another for semantic segmentation. Moreover, network structure optimization is employed to improve its forward inference speed. Exhaustive experiments on three self-generated datasets prove our pipeline's capability to execute in real-time, achieving higher frame rates than contemporary state-of-the-art frameworks (114.6 frames per second on an NVIDIA Jetson Nano GPU with TensorRT) while maintaining comparable accuracy.
Research on College English Teaching Under the Background of "Internet +"

In the Internet era, the development of information education has been inseparable from multimedia, communication technology, network platform and modern educational auxiliary tools. With the popularization of the Internet, the traditional teaching concepts and teaching methods of college English class are gradually changed. Compared with traditional classroom teaching, how college teachers update their teaching concepts under the background of the Internet and complete the transformation from traditional teaching methods to modern teaching methods is not only of great significance to teachers themselves, but also greatly promotes the improvement of students' English ability.
Evaluation of the Current Status of Malaria Prevalence in the Three Senatorial Zones of Rivers State, Nigeria

Purpose: The current status of malaria prevalence in three senatorial zones of rivers state was evaluated. There have been several intervention measures aimed at reducing the health menace of malaria in Rivers State by Government, international agencies and other stakeholders. This study was undertaken to assess the current status of malaria in the three senatorial zones of Rivers State with the view to maintaining the current intervention measures or otherwise.&#x0D; Methodology: A randomized block experimental design was used for this study. The research was divided into two (2) parts. The first part involved the production and distribution of self-structured questionnaire to the respondents to collect demographic information. The second part was laboratory examination which involved the screening of thick and thin blood films from participants to find the presence of the malaria parasite and calculate the prevalence. A total of 352 participants (110, 120 and 122 from Rivers West, Rivers Southeast and Rivers East senatorial districts respectively) seeking treatment at the zonal hospitals were recruited for the study. Venous blood samples were collected from the 352 participants and screened for the presence of malaria parasite using standard procedure while 352 copies of self-structured questionnaire were distributed to participants to collect data on some demographic characteristics.&#x0D; Findings: Out of the 352 participants examined, 275(78.1%) were positive for the presence of malaria parasites. Of the 275 infected persons, 94.6%, 77.5% and 63.7% were from Rivers West, Rivers East and Rivers Southeast senatorial district respectively. There was no significance difference (P&lt;0.05) in the prevalence between males (78.4%) and females (77.9%). Participants within the age range of 21-30years and 31-40years had the highest and least prevalence of 84.5% and 65.9% respectively. There was no significance difference (P&lt;0.05) in prevalence between singles (76.6%) and married (79.2%) participants. Level of education had no significance (P&lt;0.05) impact on the prevalence of malaria among the participants. The prevalence of malaria in participants with no formal, primary, secondary and tertiary education was 74.1%, 82.4%, 74.1% and 82.7% respectively.&#x0D; Unique Contribution to Theory, Policy and Practices: The study revealed that malaria is still endemic in the three senatorial districts and remains a public health concern. Deliberate efforts form government and other stakeholders is needed to improve on the present preventive measures put in place especially in the area of sanitation and health orientation.
Research on Hybrid Teaching Model Based on Human-Machine Collaboration

The information technology in education promotes the innovation of blended teaching mode with technical support. This paper constructs an online and offline hybrid teaching model based on the concept of human-computer collaboration. In addition, the experimental research method was used to investigate the effect of the hybrid teaching mode on the teaching effectiveness of the course "University Computer Fundamentals" as an example. The study found that learners who participated in the blended learning classroom performed better than traditional multimedia instruction, and that the different learning behaviors of learners correlated with their final grades. This shows that the online and offline hybrid teaching based on human-computer collaboration can improve the learning effectiveness of learners.
The Innovation path of College English Language Education Model based on New Media

The rapid development of information technology has not only changed people's way of life, but also pointed out the direction and provided support for educational reform. The integration of information technology and teaching has become an inevitable trend of educational development. English is a language subject, which requires students to not only to absorb, consolidate and apply knowledge in class, but also to independently find and solve problems in study, practice and life. In the teaching process, the connection between college English major and new media has become more and more strong, and the application of new media has promoted the pace of modern curriculum reform. Based on the background of new media, this paper briefly introduces the definition and characteristics of new media, analyzes the application value of new media in the field of education, and puts forward the corresponding suggestions for innovating the university English language education model for reference.
Problems and Countermeasures of College English Mixed Teaching Mode

. The advent of the epidemic has brought great changes to the education methods. The hybrid teaching mode of online and offline integration has become the new normal of college English education. College English is an important professional course, and its teaching goal is to cultivate students' English application ability and comprehensive language quality training. However, there are still some problems in the mixed teaching model in practice. This paper analyzes the implementation of college English mixed teaching mode at this stage, and summarizes the existing problems and the current situation of English professional development trend and the new information technology, and puts forward new ideas and methods of college English teaching to improve the existing teaching mode, so as to lay a solid foundation for the development of future mixed teaching mode, and to promote the development of Chinese English education in the new period.
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

Object detection and data association are critical components in multi-object tracking (MOT) systems. Despite the fact that the two components are dependent on each other, prior works often design detection and data association modules separately which are trained with separate objectives. As a result, one cannot back-propagate the gradients and optimize the entire MOT system, which leads to sub-optimal performance. To address this issue, recent works simultaneously optimize detection and data association modules under a joint MOT framework, which has shown improved performance in both modules. In this work, we propose a new instance of joint MOT approach based on Graph Neural Networks (GNNs). The key idea is that GNNs can model relations between variable-sized objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association. Through extensive experiments on the MOT15/16/17/20 datasets, we demonstrate the effectiveness of our GNN-based joint MOT approach and show state-of-the-art performance for both detection and MOT tasks. Our code is available at: https://github.com/yongxinw/GSDT
Fitting Elephants

Textbook wisdom advocates for smooth function fits and implies that interpolation of noisy data should lead to poor generalization. A related heuristic is that fitting parameters should be fewer than measurements (Occam's Razor). Surprisingly, contemporary machine learning (ML) approaches, cf. deep nets (DNNs), generalize well despite interpolating noisy data. This may be understood via Statistically Consistent Interpolation (SCI), i.e. data interpolation techniques that generalize optimally for big data. In this article we elucidate SCI using the weighted interpolating nearest neighbors (wiNN) algorithm, which adds singular weight functions to kNN (k-nearest neighbors). This shows that data interpolation can be a valid ML strategy for big data. SCI clarifies the relation between two ways of modeling natural phenomena: the rationalist approach (strong priors) of theoretical physics with few parameters and the empiricist (weak priors) approach of modern ML with more parameters than data. SCI shows that the purely empirical approach can successfully predict. However data interpolation does not provide theoretical insights, and the training data requirements may be prohibitive. Complex animal brains are between these extremes, with many parameters, but modest training data, and with prior structure encoded in species-specific mesoscale circuitry. Thus, modern ML provides a distinct epistemological approach different both from physical theories and animal brains.
Efficient Formulation of Collision Avoidance Constraints in Optimization Based Trajectory Planning and Control

To be applicable to real world scenarios trajectory planning schemes for mobile autonomous systems must be able to efficiently deal with obstacles in the area of operation. In the context of optimization based trajectory planning and control a number of different approaches to formulate collision avoidance constraints can be found in the literature. Here the contribution of the present work is twofold. First, the most popular methods to represent obstacles are summarized, namely the simple ellipsoidal representation, the constructive solid geometry (CSG) method as well as a direct and an indirect implementation of a signed distance based approach. The formulations are characterized with respect to the impact on the complexity of the optimization problem, as well as the ability to meet different problem requirements. Second, this work presents a novel variant of the CSG method to describe collision avoidance constraints. It is highly efficient due to a very low number of nonlinear inequality constraints required for a given number of obstacles and sample points and in contrast to the original CSG formulation allows to consider the controlled system's shape. The good performance of the proposed formulation is demonstrated by a comparison to the previously mentioned alternatives. To this end optimal trajectory planning for marine surface vessels formulated as a nonlinear programming problem is used as a benchmark example where the scenario is designed based on the maritime test field in Kiel, Germany.
Continuous-Time Bayesian Networks with Clocks

Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering. However, such models are often chosen to satisfy the Markov property to maintain tractability. One of the more popular of such memoryless models are Continuous Time Bayesian Networks (CTBNs). In this work, we lift its restriction to exponential survival times to arbitrary distributions. Current extensions achieve this via auxiliary states, which hinder tractability. To avoid that, we introduce a set of node-wise clocks to construct a collection of graph-coupled semi-Markov chains. We provide algorithms for parameter and structure inference, which make use of local dependencies and conduct experiments on synthetic data and a data-set generated through a benchmark tool for gene regulatory networks. In doing so, we point out advantages compared to current CTBN extensions.
THE ROLE OF FAMILY ENVIRONMENT IN DISTANCE LEARNING

&lt;p&gt;This paper aims to discuss the role of family environment in distance learning. It presents the research conducted regarding parents’ views on distance learning, the way they experience it, the obstacles and the assets that are associated with distance learning. School-family collaborative relations are regarded crucial in enhancing students’ learning experience. Parents can contribute to their children’s learning by cultivating a positive relationship with their children, providing advice and guidance, organizing time, recording, and encouraging participation (motivation), teaching with the aim of supporting children. Parents can also supervise the completion of tasks and seek help when needed, time management, identifying students' needs, communication, and access to useful resources. It is important that parents understand the essential role that children play online. Parents need to be trained to organize their children's time, motivate students, and provide them with learning support when needed and make sure their child is learning. Finally, recommendations are presented about supporting students’ online learning by their parents.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0976/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition

Two-dimensional singular decomposition (2DSVD) has been widely used for image processing tasks, such as image reconstruction, classification, and clustering. However, traditional 2DSVD algorithm is based on the mean square error (MSE) loss, which is sensitive to outliers. To overcome this problem, we propose a robust 2DSVD framework based on a generalized kernel risk sensitive loss (GKRSL-2DSVD) which is more robust to noise and and outliers. Since the proposed objective function is non-convex, a majorization-minimization algorithm is developed to efficiently solve it with guaranteed convergence. The proposed framework has inherent properties of processing non-centered data, rotational invariant, being easily extended to higher order spaces. Experimental results on public databases demonstrate that the performance of the proposed method on different applications significantly outperforms that of all the benchmarks.