Reviewerly/exHarmony huggingface dataset:
Internal combustion engine test plan execution order optimization using Travelling Salesman Problem heuristics approach

Abstract For a given internal combustion (IC) engine stationary test plan, significant time savings during its realization could be achieved if the sequence of test points execution is adequately determined. The criterion for stabilizing the engine operating point is determined by the magnitude of the change in the most inert parameter, and in the general case, it is the temperature of the engine exhaust gases. A certain level of prior knowledge about the examined object is necessary to conduct such an analysis. If there are no results from previous tests, simulation models, or experiences, the Slow Dynamic Slope (SDS) tests are a great way to quickly gather the necessary information. The task of finding the optimal sequence for a stationary engine testing plan can be set as Travelling Salesman Problem (TSP). This paper will present the application of one of the heuristic methods for solving the TSP on the example of testing the IC engine, which is a very complex dynamic system. Following this model, it is possible to optimize the stationary test plan for any other dynamic system. The basic idea is to find such a sequence of stationary operating points, during the realization of which a minor deviation of the engine exhaust gas temperature is obtained, resulting in the operating point's shortest stabilization time.
A linguistic variable of product-related question answering review system

E-commerce platforms like Amazon, Jumia, Airbnb, Alibaba, eBay and JD.com among others play a huge role in linking sellers of products to interested buyers. Consumers generally ask questions in order to know if the product of interest will meet their needs. Relying on existing product reviews posted online by other consumers who have once purchased and used the product becomes a viable option to waiting for direct answers from the community question answer system. They are usually forced to spend time manually wading through these numerous customer reviews, a task which is cumbersome. This motivated to use Bi-directional Auto-regressive Transformer (BART) model for both question-answerability classification and answer generation. A dataset provided by Gupta et al and conducted on Amazon Mechanical Turk was used for the experiment. It was observed that BART outperformed the heuristic baselines on all the metrics considered with existing models of long short term memory, convolutional neural network and bidirectional encoder representative from transformers. Based on the BART performance, a fuzzy based rule was introduced with system parameters defined for the classification of product-related question answering review into linguistic variables for good purchase decision making. The classification is in line with well-formed responses to product-related questions based on existing customer reviews using BART heuristic baselines.
Estimation and order selection for multivariate exponential power mixture models

Finite mixture model is a promising statistical model in investigating the heterogeneity of population. For multivariate non-Gaussian density estimation and approximation, in this paper, we consider to use multivariate exponential power mixture models. We propose the penalized-likelihood method with a generalized EM algorithm to estimate locations, scale matrices, shape parameters, and mixing probabilities. Order selection is achieved simultaneously. Properties of the estimated order have been derived. Although we mainly focus on the unconstrained scale matrix type in multivariate exponential power mixture models, three more parsimonious types of scale matrix have also been considered. Performance based on simulation and real data analysis implies the parsimony of the exponential power mixture models, and verifies the consistency of order selection.
IMPROVING ECONOMIC AND TECHNICAL EFFICIENCY OF PROCEDURAL PROGRAMMING IN COMPILABLE, STATICALLY TYPED C-LIKE LANGUAGES ACCORDING TO THE SPEED OF THE PROCESS AND THE STABILITY OF THE PROGRAM IMPLEMENTATION

In the modern IT industry, there is a trend towards languages with high performance. The products implemented through them become more efficient and energy-saving. However, these languages have a number of disadvantages based on their technical implementation. These defects create side problems over time and increase the cost of implementing, securing, configuring, and extending the functionality of the product. Thus, the product becomes more expensive from a financial point of view, but more competitive. In order to further increase the competitiveness of products, to neutralize side problems, to reduce the cost, companies listed in international indexes and universities with global scientific qualifications either develop a new language, or update and add new functionality to the already existing language. The article presents recommendations for compilable, statically typed, C-like languages to make their procedural programming safer. They will ensure readability and increase the speed of the process. These changes will make IT products more competitive in the free market. The advantage of recommendations is that the most time-consuming checks and decisions will be made at compile time. There are some examples of the changes in the article.
Reference set based metric learning method for person re-identification against overfitting problem

Similarity distance measurement is an important method in data classification, data recognition and other tasks, and has a very wide range of applications in machine learning, computer vision and other fields. However, there are model overfitting problems in complex data classification identification tasks in existing metric learning model. And those problems will negatively infect the accuracy and stability of the metric models. We study on person re-identification (person re-ID) task to design a robust similarity distance metric learning model based on a novel approach of overcoming over-fitting problem. The proposed method sets up a reference set based on training sample. Using the reference set and test images to form similar sample pairs, we can optimize the distribution information and projection feature. Finally, by testing on benchmark dataset, VIPeR, the experimental results validate the effectiveness of the proposed method. It achieves the best identification rates.
Numerical reasoning based on transformer

Numerical reasoning is a complex subtask in machine reading comprehension, which requires the model to complete discrete operations such as statistics and calculation in the process of reasoning answers. As far as we know, the best solution for numerical reasoning adopts GNN structure. However, the graph structure is often generated by hand-designed rules and it will cost extra computation resource. To tackle these problems, we propose a Transformer-based numerical reasoning solution which transforms the hand-designed graph structure into learnable parameters. Extensive experimental results show that compared to those GNN-based methods, our method can carry out numerical reasoning more efficiently only at the cost of a little decrease on answer quality.
Research on video anomaly detection with variational auto-encoder based on multi-level memory enhancement

Using autoencoders to reconstruct current video frames or predict future frames is a popular method in the task of video anomalous behavior detection based on weakly supervised learning. Recent studies have shown that introducing a memory module into an autoencoder can capture a limited number of normal patterns and cannot cope with new scenarios in the test set. Therefore, this paper proposes a dual-stream based multi-level memory-enhanced conditional variational autoencoder model (TS-MemCVAE), which uses RGB image and optical flow image dual-stream input, and adds memory modules at the bottle-neck. The memory module contains normal mode features of different sizes. At the same time, with the aid of optical flow information, the model can sensitively identify abnormal behaviors with large reconstruction errors. The model is divided into two parts: a multi-level memory-enhanced auto-encoder and a conditional variational autoencoder. The former is responsible for the reconstruction of the input video, and the latter is used to capture the high correlation between the reconstructed video and the optical flow image. further predictions. The model is validated on two benchmark datasets, UCSD Ped2 and CUHK Avenue, and achieves 95.83% and 84.16% on AUC, respectively, and its excellent performance proves the effectiveness of the model.
Every detection box for multiple object tracking based on GAM

Multiple object tracking (MOT) has become a very important task in the field of rail transit. Recently, the most effective method in MOT tasks is tracking by detection. Because there are many pedestrians under a single camera in the railway station, the occluded pedestrians will generate detection boxes with low-score. Once these low-score bounding boxes are mistakenly classified as background by the detector, the traditional tracking model may discard these boxes, resulting in poor tracking effect. In order to solve this problem effectively, this paper proposes a novel model with Generic Association Mechanism (GAM), which can pay attention to every detection box instead of only focusing on the highscore ones. This method utilizes the similarity between these low-score detection boxes and trajectories to restore true objects and filter out the background detection boxes. More attention can be focused on these low-score detection boxes through this method, which contribute the most to the tracking effect. This method can efficiently avoid the loss of true objects and fragmented trajectories of occluded objects. Finally, we test this proposed model with other models on several public datasets, and the results show that the proposed method achieves more significant performance improvement.
Private cloud resource prediction based on combinatorial optimization algorithm

In private cloud environments, resource usage is determined based on experience, which often leads to excessive resource allocation and low resource utilization. This paper proposes a private cloud resource consumption prediction algorithm based on combinatorial optimization algorithm. Based on the virtual machine performance data collected from the private cloud platform, the key features are selected by recursive feature elimination method (SVM-RFE), and then the model is trained by eXtrem Gradient Boosting (XGBoost) algorithm for model training to predict the resource usage. Compared with Random Forest, LSTM and other algorithms, the proposed algorithm has higher prediction accuracy and smaller prediction error. This paper adopts a data-driven approach to achieve intelligent prediction of resource usage in private cloud environments, which improves resource utilization and provides decision support for optimal allocation of resources.
A flexible and extensible approach to face extraction and swapping

The completion and detail retention of face replacement after face changing are difficult technical problems in face swapping. This paper proposes an efficient and flexible model for face swapping which can realize high-quality face swap and improve the quality of the outputs of the existing networks. The proposed face swapping network is completed by the AEI-Net. After training the AEI-Net (the AEI-Net consists of three subnets: identity encoder, multi-level attribute encoder and ADD generator), the generated image with face change effect can be obtained. In our experiment, we use the training data sets which are CelebA and FFHQ as other face swapping networks to train their model and our model and use the same test data set to evaluate it. After training, it can recover the abnormal area in a self-supervised way. The results show that compared with the state-of-the-arts, our model achieves good performance in terms of the realism of the generated face and the degree of detail reduction.
Analysis of security capability of MVX based on probabilistic attack and defense models

Multi Variant eXecution (MVX) is a security defense technique that uses software diversity to protect system from attacks. MVX improves security capability by enhancing system endogenous security compared to traditional passive defense techniques. However, the current MVX technique lacks formal theoretical analysis and cannot effectively assess the overall security of the system. To address the constraint relationship between complex attack means and dynamic defense environment, we construct a novel atomic combination attack chain model, which decomposes macro attack means into single atomic attack behaviors and provides theoretical support for analyzing the security capability of dynamic systems. Then, the defense model of the MVX system is established, and the defense model’s security capability is analyzed using the attack model. Finally, the advantages and shortcomings of the security defense capability of the MVX system are evaluated based on a typical kernel attack example, and system optimization improvement measures are proposed.
A cycle-consistent reciprocal network for visual correspondence

Visual correspondence refers to building dense correspondences between two or more images of the same category. Ideally, the predicted keypoints output by the model can be back to the source image’s keypoints through the same type of network. However, in practical situations, the predicted keypoints usually do not perfectly map back to the source image keypoints. In order to strengthen the cycle-consistency of the model, we propose a cycle-consistent reciprocal network. The network uses joint loss functions to alternately train forward and inverse models, which makes the two models subject to cycle constraints and perform better with the help of each other. Experiment results demonstrate the performance of the model is improved on three popular benchmarks and set a new state-of-the-art on the benchmark of PF-WILLOW.
An optimal LT code for network flow watermarking

Network flow watermarking has been employed to analyze whether two flows are correlated, which is often applied in non-cooperative networks. The detection performance of network flow watermarking is poor due to delay jitter, packets loss etc. In this paper, we improve the detection performance of network flow watermarking by using Luby Transform (LT) codes, which can take full advantage of network flow by automatically adapting to the length of network flow. To further improve the performance of LT codes for network flow watermarking, an optimal degree distribution is generated through a heuristic algorithm. Theoretical analysis and simulations show that our proposed method outperforms others in terms of the detection performance.
A comparative research on the effectiveness of two mobile applications for vocabulary learning: BaiCiZhan and Bubeidanci

In recent years, with the development of science and technology, more and more language teachers and researchers pay attention to the application of mobile devices in language learning. However, few studies have explored the effectiveness of specific vocabulary learning applications in the Chinese context and made suggestions for students’ choices. This research involves 16 participants using two popular Apps named BaiCiZhan and Bubeidanci respectively. They were required to do a pre-test, two immediate post-tests and a delayed post-test, to compare the difference in the effect of vocabulary retention of the two apps. The results showed that although both apps significantly improved the retention effect immediately after learning and one week after learning, there was no difference between the two apps. In addition, interviews were conducted to investigate the participants’ experience of using the two apps and combining the vocabulary learning strategies used in two apps. This study concluded that the learning style preference of learners could be seen as a factor, and the two most important vocabulary learning strategies respectively used by the two apps which are using pictures and using various context sentences both have their own advantages and disadvantages, which makes no difference in the final result of word retention between the two apps.
Chinese short text classification by combining Bert and graph convolutional network

Short Text Classification is the fundamental task in the nature language processing. There is a lack of language structure and uneven classification of data samples in short texts, which limit the development of deep learning based short text classification. To address the limitations of text sequences, we propose using a large-scale pre-trained language model Bert to obtain feature information between words and bureaus in the text, Graph Convolutional Network (GCN) with double-layer convolutional network can obtain the dependency relationships between words. We propose to combine Bert with GCN in short Chinese medical texts, where BertGCN outperforms better than other’s methods in classification accuracy.
Research on several construction strategies of multilingual resource database

In order to realize the effective use of information resources in different languages, it is necessary to establish a corresponding information resource database. Based on cross-language retrieval, this paper analyses the construction strategy of multilingual resource database, and takes language acquisition and processing as the perspective. The construction of the resource database provides a reference. In view of the construction of multilingual resource database, this paper makes an analysis from three aspects: resource collection and pre-processing, resource storage and retrieval, and multilingual translation technology, and summarizes its implementation methods. The collection, storage and retrieval of multilingual resources should adopt a distributed architecture. The resource library builds a language encoding and transcoding system for efficient collection and preprocessing of multiple languages. The translation between languages adopts machine translation technology mainly based on neural network machine translation.
Vehicle recognition and tracking method based on roadside perception

In vehicle detection and tracking methods based on roadside cameras, due to factors such as motion blur, vehicle occlusion, and target scale changes in the video, problems such as missed detection, false detection, and low positioning accuracy may occur during vehicle detection and tracking. This paper proposes to use the improved target detection model YOLOv5s as the target detector, then combined with the classical Deep SORT target tracking method, to achieve end-to-end vehicle detection and tracking. By integrating the attention mechanism with the detection network, and modifying the loss function, the ability of the model to extract features is strengthened, and the final vehicle detection accuracy is 96.7%. During tracking, the vehicle IDs are reduced to 28 times and the operating speed reaches 32 Hz.
Lightweight dynamic large convolution model for real-time human pose estimation

Although the lightweight Openpose model can perform real-time human pose estimation on the CPU, it also has the following problems: (1) The ReLU activation function destroys the narrow channel information; (2) Although the model has high inference efficiency, it also has weak feature extraction ability; (3) The model has a small effective receptive field. In order to improve the above problems, we adjusted the structure of the lightweight Openpose model, replacing the backbone of the lightweight Openpose with the backbone of MobileNetV2 to improve the destruction of the narrow channel information by the ReLU activation function. Replacing part of the backbone’s convolutional layers with dynamic depth wise separable large convolutional layers expands the effective receptive field of the model, improves feature extraction and slightly increases the computational cost. Our Lightweight dynamic large convolution model achieves an Average Precision (AP) value of 0.40 on the COCO2017 validation set.
Research on USV path planning method based on CW-RNN framework

This paper presents an Unmanned Surface Vehicle (USV) path planning algorithm based on a Clockwork-RNN (CW-RNN) framework. Compared with RNN, CW-RNN divides the hidden state matrix into several small modules and uses a method similar to clock frequency mask to divide the memory of RNN into several parts, so that each part of CW-RNN memory matrix can process data at different times and enhance the memory effect. The existence of ocean current will cause drifting motion in the navigation track of unmanned ship, and make the ship deviate from the planned path and direction. In view of this interference factor, ocean current vectors of different sizes and directions are added in the simulation environment to make the environmental model closer to the actual sea surface. As the environment becomes more complex, reinforcement learning takes a long time to train in the complex environment and is not easy to converge. Therefore, this paper combines reinforcement learning method with the traditional path planning method Dijkstra algorithm, inputs the local map information detected by unmanned ship into Dijkstra algorithm to give task sub targets, and uses these sub targets to guide unmanned ship and improve the search efficiency of neural network. Finally, the simulation results and analysis show that the training algorithm is effective in the current environment.
Design of WeChat teaching information retrieval platform based on DCMH

Deep Semantic Multimodal Hashing (DCMH) is a cross-modal hash retrieval method based on a deep learning framework that combines deep feature learning and hash learning. The method uses the extracted text and image data features as hash codes to form an end-to-end network of directly discrete optimized binary hash codes. In the era of new media, the WeChat public platform is one of the most important channels for teaching information services in colleges and universities, so that information services can directly point to the needs of teachers and students. Using the WeChat public account as the front end , based on DCMH, a method of extracting fine-grained features on images and texts was designed, and it set up feature learning and label prediction for multi-modal data, built a multi-modal retrieval platform based on WeChat public account, and designed DCMH-based feature training data mining and public account front multimodal data interaction model diagram, keeping up with the digital age to meet the development requirements, to achieve a major change in teaching management, and improve the efficiency and level of service.
Research on MR navigation design based on double diamond model: a case of South China Botanical Garden

The South China Botanical Garden is the largest South Subtropical Botanical Garden in China, but it is still in traditional browsing mode. In the botanical garden, there are problems such as confusion of route guidance, single botanical form, and dullness. The rapid development of MR (Mixed Reality) technology brings new opportunities to the tourism industry, and MR technology can bring more interactive and interesting experiences to tourists. Therefore, based on the method of the double diamond model, this study designs a MR navigation system for the South China Botanical Garden to optimize visitors’ experience and improve the service quality of the botanical garden. The research results have reference value for the digital transformation of services in large botanical garden and the improvement of the service quality.