Reviewerly/exHarmony huggingface dataset:
Identifying Labor Market Competitors with Machine Learning Based on Maimai Platform

The demand for skilled labor has increased dramatically in the current knowledge-based economy, which is characterized by the growing intensity in labor market competition between firms. Therefore, it would be of special interest to identify future labor market competitors. At present, with the vast amount of textual data, the existing study focuses on constructing the human capital overlap and product overlap metrics with the text data as predictors to predict the labor market competition in the United States. Based on these metrics, this paper experiments with machine learning methods to predict Chinese labor market competition with Chinese text data. Furthermore, sentiment analysis is becoming popular and it has been used in a wide variety of fields. However, due to lack of data, there are few existing studies using sentiment analysis approach of firms’ online reviews. In response to this research gap, this paper constructs the sentiment analysis metric by mining the emotional content expressed in the firms’ online reviews on Maimai’s platform. The results show that our proposed metrics have superior predictive power over conventional measures and highlight the predictive utility of proposed sentiment analysis metric. Moreover, the nuanced two-dimensional competition analysis gives some interesting results.
DGE-GSIM: A multi-task dual graph embedding learning for graph similarity computation

Graph similarity estimation is a challenging task due to the complex graph structure. To achieve an exact similarity estimation for input graphs, two critical factors are how to learn an appropriate graph embedding and how to compute the similarity between a pair of graphs. Graph neural networks (GNN) generalize convolutional neural networks (CNN) to graph data for learning graph embeddings. However, the GNN models ignore the edge information when earning graph embeddings. To solve it, we propose a graph similarity estimation framework by taking into consideration the edge feature learning during the graph embedding learning. We name our approach as DGE-GSIM, i.e., Dual Graph Embedding for Graph SIMilarity learning. More specifically, we firstly construct an edge graph with node-edge switching, and then we propose a multi-task learning framework for learning a dual graph embedding with simultaneously considering the node and edge features with GNN. Finally, with the obtained dual graph embedding, we develop a three-way graph similarity computation from the perspectives of the graph-graph interaction, node-node interaction and node-graph interaction. It can model a comprehensive similarity for input graphs. We conduct extensive experiments on a very popular graph similarity/distance metric, GED, based on Linux dataset to demonstrate the effectiveness of the proposed method.
Sequence-to-sequence neural machine translation for English-Malay

Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a converge of below 30 epochs. Our study shows that the ReLU attention alignment improves the bilingual evaluation understudy (BLEU) translation score between score 0.26 and 1.12 across all the models as compare to the original Tanh models.
GINN

Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide variety of applications, ranging from self-driving cars to COVID-19 diagnosis. To support the computational power necessary to train a DNN, cloud environments with dedicated Graphical Processing Unit (GPU) hardware support have emerged as critical infrastructure. However, there are many integrity challenges associated with outsourcing the computation to use GPU power, due to its inherent lack of safeguards to ensure computational integrity. Various approaches have been developed to address these challenges, building on trusted execution environments (TEE). Yet, no existing approach scales up to support realistic integrity-preserving DNN model training for heavy workloads (e.g., deep architectures and millions of training examples) without sustaining a significant performance hit. To mitigate the running time difference between pure TEE (i.e., full integrity) and pure GPU (i.e., no integrity) , we combine random verification of selected computation steps with systematic adjustments of DNN hyperparameters (e.g., a narrow gradient clipping range), which limits the attacker's ability to shift the model parameters arbitrarily. Experimental analysis shows that the new approach can achieve a 2X to 20X performance improvement over a pure TEE-based solution while guaranteeing an extremely high probability of integrity (e.g., 0.999) with respect to state-of-the-art DNN backdoor attacks.
Express Construction for GANs from Latent Representation to Data Distribution

Generative Adversarial Networks (GANs) are powerful generative models for numerous tasks and datasets. However, most of the existing models suffer from mode collapse. The most recent research indicates that the reason for it is that the optimal transportation map from random noise to the data distribution is discontinuous, but deep neural networks (DNNs) can only approximate continuous ones. Instead, the latent representation is a better raw material used to construct a transportation map point to the data distribution than random noise. Because it is a low-dimensional mapping related to the data distribution, the construction procedure seems more like expansion rather than starting all over. Besides, we can also search for more transportation maps in this way with smoother transformation. Thus, we have proposed a new training methodology for GANs in this paper to search for more transportation maps and speed the training up, named Express Construction. The key idea is to train GANs with two independent phases for successively yielding latent representation and data distribution. To this end, an Auto-Encoder is trained to map the real data into the latent space, and two couples of generators and discriminators are used to produce them. To the best of our knowledge, we are the first to decompose the training procedure of GAN models into two more uncomplicated phases, thus tackling the mode collapse problem without much more computational cost. We also provide theoretical steps toward understanding the training dynamics of this procedure and prove assumptions. No extra hyper-parameters have been used in the proposed method, which indicates that Express Construction can be used to train any GAN models. Extensive experiments are conducted to verify the performance of realistic image generation and the resistance to mode collapse. The results show that the proposed method is lightweight, effective, and less prone to mode collapse.
An early warning model for student performance based on machine learning algorithms in the context of big data in education

With the development of society, environmental problems are becoming more and more important. We should develop the environment while developing the economy. People's awareness of environmental protection should also continue to improve, and begin to pay more attention to ecology. Cities all over the world should take the road of ecological development and build ecological cities. The construction of ecological city is the symbol of human civilization and the inevitable trend of urban development. This leads to a very important concept in eco-city, that is the plant wall. Based on this, the author designed a simple intelligent planting system based on SCM. The system uses MCU "Arduino Uno R3" as the main control chip. The photosensitive resistance sensor and humidity sensor are used to measure the current information and feed back to the MCU. LED lights and feeders work to realize automatic watering, lighting and feeding fish on time. Finally, the entire system can be controlled through a wireless connection to the phone. In this paper, the hardware and software parts of the system are mainly designed, and the parameters of the system are tested, which basically realize the function and improve the intelligent degree of plant planting.
An LSTM‐based approach to precise landing of a UAV on a moving platform

Abstract A machine learning‐based method for the precise landing of an unmanned aerial vehicle on a moving mobile platform is proposed. The proposed approach attempts to predict the mobile platform's future trajectory based on the past states of the mobile platform. To that end, it combines a long short‐term memory‐based neural network with a Kalman filter. Hence, it aims at combining the advantages of a machine learning method with those of a state estimation method from established control theory. Based on the predicted trajectory, the unmanned aerial vehicle attempts to land precisely on the moving mobile platform. The experiment is conducted in the Gazebo simulation platform with a quadrotor and an omnidirectional mobile robot, and the proposed method is compared with the single‐method approaches of using only either the Kalman filter or the machine learning method alone.
VR-based Context Priming to Increase Student Engagement and Academic Performance

Research suggests that virtual environments can be designed to increase engagement and performance with many cognitive tasks. This paper compares the efficacy of specifically designed 3D environments intended to prime these effects within Virtual Reality (VR). A 27-minute seminar &#x201C;The Creative Process of Making an Animated Movie&#x201D; was presented to 51 participants within three VR learning spaces: two prime and one no-prime. The prime conditions included two situated learning environments; an animation studio and a theatre with animation artifacts vs. the no-prime: theatre without artifacts. Increased academic performance was observed in both prime conditions. A UX survey was also completed.
Deep iterative residual back-projection networks for single-image super-resolution

The process of single-image super-resolution (SR) has certain limitations, such as an insufficient utilization of high-frequency information in images and a network structure that is insufficiently flexible to reconstruct the feature information of different complexities. Therefore, deep iterative residual back-projection networks are proposed. Residual learning was used to ease the difficulty in training and fully discover the feature information of the image, and a back-projection method was applied to study the interdependence between high- and low-resolution images. In addition, the network structure reconstructs smooth-feature and high-frequency information of the image separately and transmits only the residual features among all residual blocks of the network structure. The experiment results show that compared with most single-frame image SR methods, the proposed approach not only achieves a significant improvement in objective indicators, but it also provides richer texture information in the reconstructed predicted image.
Anomaly Detection and Analysis Based on Deep Autoencoder for the Storage Tank Liquid Level of Oil Depot

Abstract In the industrial control system of oil depot, the change of storage tank liquid level is closely related to the transportation process and site management of oil depot. It is of great significance to detect the abnormality of storage tank liquid level data for the safe and stable production of oil depot. A data-driven anomaly detection strategy is proposed by analyzing the non-periodic time series data of the oil storage tank liquid level. Based on the convolutional autoencoder algorithm to learn the features and patterns of a large number of samples, the strategy is carried out by reconstructing the samples and calculating the reconstruction error, which not only does not rely on the labeled samples, but also improves the detection precision. This paper chooses three algorithms of convolutional autoencoder, RNN (Recurrent Neural Network) autoencoder and LSTM (Long Short-Term memory) autoencoder for experimental analysis. Experiments were carried out on the oil tank historical data set and NAB (Numenta Anomaly Benchmark) simulation data set respectively. The results show that the accuracy of convolutional autoencoder is 98% and the F1 score is 82%, which is more practical for the scenes with real-time requirements.
Does Remote Expert Representation really matters: A comparison of Video and AR-based Guidance

This work describes a user study aimed at understanding how the remote expert representation affects the sense of social presence in scenarios of remote guidance. We compared a traditional video chat solution with an Augmented Reality (AR) annotation tool. These were selected due to ongoing research with partners from the industry sector, following the insights of a participatory design process. A well defined-problem was used, i.e., a synchronous maintenance task with 4 completion stages that required a remote expert using a computer to guide 26 on-site participants wielding a handheld device. The results of the study are described and discussed.
516 LinkedIn Marketing Strategies to Drive NJ ACTS Regulatory Core Engagement

OBJECTIVES/GOALS: Our purpose is to promote traffic toward the NJ ACTS Regulatory Cores recently launched website and increase investigator engagement through marketing strategies on LinkedIn. Landscaping to characterize the profiles of researchers on LinkedIn was also completed to estimate the feasibility of engaging with a target population on LinkedIn. METHODS/STUDY POPULATION: Insight gathering was performed to analyze what percentage of researchers possessed a LinkedIn profile and actively used their accounts. A sample population consisting of 284 NJ ACTS members were analyzed to summarize the type of researchers on LinkedIn, and their likelihood of responding to LinkedIn marketing campaigns. Efforts to launch a company LinkedIn page and collect followers were completed. Different methods of promotion were evaluated, including direct vs. mass email outreach to over 600+ researchers at Rutgers. Effectiveness of our platform was measured by comparing/overlaying Regulatory website traffic with LinkedIn traffic, as well as tracking the metrics of LinkedIn posts. RESULTS/ANTICIPATED RESULTS: Among 284 NJ ACTS members, 76% (n=215) possess a LinkedIn profile, but only 21% (n=59) are actively interacting with material on LinkedIn, such as creating, commenting, or sharing posts. Among the NJ ACTS LinkedIn users, 27% of individuals (n=57/215) responded to a direct outreach. Retention of the created organizational page was strong, as most users who visited the Regulatory Core page were likely to become followers. Massive email outreach to 600+ researchers within RBHS did not yield a strong LinkedIn following, however it did result in strong signals of website traffic during the days after the promotion was sent. Engagement with posts on LinkedIn can also be amplified and messaging proliferated when colleagues reshare Regulatory posts on their personal feeds. DISCUSSION/SIGNIFICANCE: 3/4 of academic researchers are likely to be on LinkedIn but may not be active users of the platform. The most effective outreach is through direct messaging as opposed to broader, less individualized tactics (including mass email outreach). Evidence suggests potential to utilize LinkedIn to proactively engage in regulatory-related activities.
Virtual Workspace Positioning Techniques during Teleportation for Co-located Collaboration in Virtual Reality using HMDs

In many collaborative virtual reality applications, co-located users often have their relative position in the virtual environment matching the one in the real world. The resulting spatial consistency facilitates the co-manipulation of shared tangible props and enables the users to have direct physical contact with each other. However, these applications usually exclude their individual virtual navigation capability, such as teleportation, as it may break the spatial configuration between the real and virtual world. As a result, the users can only explore the virtual environment of approximately similar size and shape compared to their physical workspace. Moreover, their individual tasks with unlimited virtual navigation capability, which often take part in a continuous workflow of a complex collaborative scenario, have to be removed due to this constraint. This work aims to help overcome these limits by allowing users to recover spatial consistency after individual teleportation in order to re-establish their position in the current context of the collaborative task. We use a virtual representation of the user&#x2019;s shared physical workspace and develop two different techniques to position it in the virtual environment. The first technique allows one user to fully position the virtual workspace, and the second approach enables concurrent positioning by equally integrating the input from all the users. We compared these two techniques in a controlled experiment in a virtual assembly task. The results show that allowing two users to manipulate the workspace significantly reduced the time they spent negotiating the position of the future workspace. However, the inevitable conflicts in simultaneous co-manipulation were also a little confusing to them.
Multi-exposure image fusion for dynamic scenes with ghosting removal

Multi-exposure image fusion algorithms in dynamic scenes usually have ghosts. Most of the existing ghost-free methods have complex calculations and the details will be lost. To solve these problems, we present a multi-exposure fusion method, which can remove ghosts and preserve more details of the image. The method detects ghost images by the improved frame difference method and calculates the early weight maps, combining the ghost detection binary image and initial weight maps to redefine weight maps. Then with the improved Laplacian pyramid algorithm, a high-quality image with more details is fused quickly. This is a significant improvement over existing techniques by objective and subjective comparisons.
Effect of Water on Flow Fluctuation in River Flow

River water fluctuations affect the amount of inundation that occurs. The study aims to obtain a cross-section of the river channel to drain runoff water so that the runoff due to maximum rainfall does not cause puddles. The Ketupak river flow cannot drain flow during the rainy season. Besides that, there is also a lot of sediment, garbage, stems and branches that interfere with the Ketupak river flow. Therefore, it is necessary to rearrange the river channel. From the results of calculations based on rainfall data, it is necessary to increase the cross-sectional area of the river.
Application of Convolutional Neural Network-Based Hierarchical Teaching Method in College English Teaching and Examination Reform

Along with the pace of educational reform in colleges and universities, a variety of new types of teaching and research approaches stand out in each subject taught in colleges and universities. For example, in college English lectures, given the practice of individualized tiered teaching, the development of relevant teaching models for students at different levels has become a new type of teaching and research developed year by year. Based on the English classroom program, teachers should make cognizance of the tiered teaching model when teaching. This paper discusses the tiered teaching method of English teaching and carries out teaching from strategies such as paying attention to students’ tiered teaching, doing well in lecture tiered teaching, developing homework tiered teaching, and paying attention to evaluation tiered teaching. In addition, the assessment system of college English courses lags behind the development of college English teaching reform and cannot play a guiding role in teaching. In response to the above-mentioned views and problems, this paper proposes a convolutional neural network-based algorithm that provides different learning styles for different students in the stratified teaching method of college English, making capable students understand what they learn in class, improving the teaching quality of high school English courses, and, at the same time, establishing a standardized and scientific course with high reliability and validity that meets the actual situation of applied technical college students. At the same time, a standardized and scientific course assessment system with high reliability and validity has been established to meet the actual needs of applied technical college students.
Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance.&#x0D; Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques.&#x0D; In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.&#x0D;