Reviewerly/exHarmony huggingface dataset:
Performance Analysis and Improved Algorithm of Gaussian Optimal Filtering for Underwater Target Tracking

In order to accurately predict the state of underwater dynamic targets, the performance analysis of Gaussian optimal filtering and its improved algorithm are proposed in this paper. The state estimation performance of Gaussian Hermite Kalman filter (GHKF) and Gauss Hermite Rauch-Tung-Striebel smoother (GHRTS) is analyzed in detail, and the idea combined with particle filter algorithm-Gaussian Hermite Kalman particle filter (GHKF-PF) and Gauss Hermite Rauch-Tung-Striebel smoother particle filter (GHRTS-PF) is proposed. This analysis is done through the variance of Gaussian noise and the change of Gaussian mixture noise. This performance-based research is carried out under the background of bearing-only tracking (BOT) phenomenon. All the experiments are to find out the RMSE between the real state and the predicted state of the object. The numerical results based on independent Monte Carlo simulation show that under the condition of white Gaussian noise, GHRTS has better performance than GHKF, GHKF-PF is better than GHKF, GHRTS-PF and GHRTS, and GHRTS-PF is better than GHKF-PF. However, under Gaussian mixture noise, the performance of GHRTS is lower than that of GHKF, and the performance of GHKF-PF is better than that of GHKF, GHRTS-PF, which is better than that of GHRTS,GHRTS-PF and lower than that of GHKF-PF.
Keyphrase Extraction from Twitter Data—A Supervised Deep Learning Approach

A successful keyphrase extractor extracts exclusive, relevant keyphrases that capture the summary about the topic of the data and help in a fast information process. There are various methods previously available for keyphrase extraction, especially from Twitter data. Despite their availability, performance enhancement of these methods is a challenging problem. Also extracting keyphrases from twitter can help in extracting an idea from tweets related to specific social issue, disaster, user reviews about a product which in turn help in various applications which use keyphrases in awareness making, law abiding and recommendation applications. Hence, focusing on enhancing the performance of the existing models and to extract relevant keyphrases, we propose a supervised keyphrase extraction model using Extractive BERT Summarizer (BERT SUM) with Bidirectional Long Short-Term Memory (Bi-LSTM) using Global vectors (GloVe) word embedding from the Twitter tweets. We implement the model on the real-time Twitter data of 6000 tweets. We use BERT SUM for summarizing the tweets and Bi-LSTM for classification and extraction of keywords. To evaluate the performance of the proposed architecture the performance metrics precision, recall and F1-score are used, which are accepted commonly by the previous keyphrase extraction works. The results from the experiment showed that the proposed architecture outperforms the already available state-of-the-art keyphrase extraction methods.
Implanting Domain Knowledge into Feature Selection for Effective Outlier Detection in Network Traffic Data

Feature selection is vital to outlier detection for network traffic data containing many noise features. Existing methods often perform on pure categorical or numerical data, which cannot directly work on network traffic data containing categorical and numerical features. Meanwhile, these methods do not utilize domain knowledge summarized based on characteristics of existing outliers, leading to unsatisfying performance. This paper proposes a novel outlier detection method based on feature selection in network traffic data, termed ODNTD, which adopts domain knowledge in feature selection. ODNTD includes a three-stage process of decomposition-aggregation-decomposition. In decomposition, categorical and numerical data are separately outlier scored using value frequency and deep autoencoder. In aggregation, the two scores are merged by a dynamic aggregation strategy, and a set of outlier candidates is identified by a thresholding function. The re-decomposition performs coarse-grained and fine-grained selection in respective feature spaces of outlier candidates by machine learning with domain knowledge to obtain feature subsets. ODNTD repeats the stages until an empirical error no longer decreases. Summarily, ODNTD iteratively exchanges information between categorical and numerical data and implants domain knowledge into selection. Experiments show ODNTD averagely improves AUC than nine state-of-the-art competitors by 46.18% and reduces features by 52% on five real network traffic datasets.
Research on Teaching Reform of Mathematics Courses for Computer Majors in Local Colleges under Emerging Engineering Education

To develop Emerging engineering education talent with a variety of analytical ability, application ability, innovation ability as a starting point, combined with the actual situation of the School of Computer and Information Engineering of Fuyang Normal University, the teaching reform of the mathematics course for computer majors was discussed from five aspects: perfecting curriculum construction, optimizing teaching content, implementing mixed teaching, strengthening practical teaching and fair examination of results. Improve the quality of students and teaching quality, which further improves the quality of students and the quality of teaching.
Design and error analysis of Gabor-type diffractive lens

Gabor zone plate (GZP) is typical amplitude-type diffractive lenses for high-resolution imaging. Compared with Fresnel zone plates (FZP), GZP can suppress all higher-order foci and shows better focusing performance. However, the focusing efficiency of GZP is very low and the cosine variation of its transmittance is difficult to satisfy. In order to improve these disadvantages, many kinds of improved Gabor zone plates are designed. However, the focusing efficiency of these Gabor zone plates are ~10%, which is still too low. A phase-type diffractive lens based on the Gabor zone plate is designed to improve the focusing efficiency of GZP, in which the radial distribution of microstructure varies in a cosine pattern. Simulation results show that the focusing efficiency of the Gabor-type diffractive lens (GDL) can reach ~22%, which is much higher than that of GZP. In addition, we also analyze the effect of errors of laser direct writing on the focusing efficiency of GDL and Fresnel-type diffractive lens (FDL). The results show that the focusing efficiency of GDL shows higher tolerance for the error of laser direct writing than that of FDL.
Design and Implementation of the E-mall Management System Based on SSM

Abstract: The electronic mall is an electronic trading platform for large commodities built with e-commerce software. Its main function is to sell products to customers accurately and quickly through this platform. Online Marketing has become an irreplaceable and important means in product sales. This article designs and implements an electronic mall management system. It integrates functions such as product classification, product browsing, shopping cart function, order management, and announcement management. The system provides services for registered members, non-registered members, product managers, system administrators, and big data analysts.
MMA-Net: A MultiModal-Attention-Based Deep Neural Network for Web Services Classification

Recently, machine learning has been widely used for services classification that plays a crucial role in services discovery, selection, and composition. The current methods mostly rely on only one data modality (e.g. services description) for web services classification but fail to fully exploit other readily available data modalities (e.g. services names, and URL). In this paper, a novel MultiModal-Attention-based deep neural network (MMA-Net) is proposed to facilitate the web services classification task via effective feature learning from multiple readily available data modalities. Specifically, a new multimodal feature learning module is introduced to achieve effective message passing and information exchanging among multiple modalities. We conduct experiments on the real-world web services dataset using various evaluation metrics, and the results show that our framework achieves the state-of-the-art results.
A Method to Comprehend Feature Dependencies Based on Semi-Static Structures

To understand why features of existing software can depend on each other is important for correct addition of a new feature to the software. Although some work has been done to detect feature dependency, it is not clear how effective such existing approaches are when they are applied to feature dependency comprehension because they are aimed at detection of runtime dependency between features. Therefore in this paper, we present an extraction method of source code that can be used to comprehend feature dependency. The method can extract a wider range of source code than existing techniques of feature dependency detection by using delta extraction. We conducted a controlled experiment with 20 professional programmers and confirmed that the difference of the extracted range has a positive effect on feature dependency comprehension. To figure out an internal mechanism to enable feature dependency, we also defined semi-static parts of object graphs that can be used to make features depend on each other. Finally, we confirmed that semi-static structures are actually used in feature dependencies in three open source programs and can be effectively extracted by our method.
The implementation of group investigation learning model to equip students to think critically in addressing the hoax content of disaster on the internet

Abstract Nowadays, internet access can give not only positive influence but also the negative one. One of the negative influences is hoax news about natural disaster. This research aims to implement Group Investigation learning model in order to develop students’ critical thinking skills, so students can respond carefully on hoax news of natural disaster. The method in this research was Classroom Action Research (CAR). The result shows that; (1) the knowledge students have of the potential for natural disasters, does not guarantee that they can understand whether the disaster information is a hoax or not; (2) information about disasters needs to be investigated in groups for people of the same age as middle school students; (3) Students’ confidence in a news is directly proportional to their actions to share a content through social media; (4) disaster information is something that needs to be considered in disaster learning.
Classification Tree Models in the Precision Teaching of Foreign Languages

Precision teaching involves the observation, description, documenting and analysis of the process of teaching and learning through the utilization of measuring and drawing tools. However, in the precision teaching of foreign languages, the embedding of techniques for data surveying, mapping and visualization is still inadequate, lacking the data decision tools with high operationality. Targeted at the above-mentioned practical issues, this article summarizes the traits and training sets and builds the learner, learning process and teaching process classification tree models applicable in the precision teaching of foreign languages, expounding the corresponding functions and application, to provide the feasible data visualization and data decision tools for the precision teaching of foreign languages.
Statistical Modeling Using Bounded Asymmetric Gaussian Mixtures: Application to Human Action and Gender Recognition

To determine the structure of high dimensional data without knowing the number of clusters nor the importance of the involved features, we propose an unsupervised feature selection framework using the bounded asymmetric Gaussian mixture model (BAGMM-FS). The bounded asymmetric Gaussian distribution has an asymmetric shape and bounded range, making it a good choice for modeling real-world data. We propose a parameter learning approach based on the expectation-maximization (EM) algorithm, and we approach the model selection task using the minimum message length (MML) criterion. The validation involves several human-related recognition challenges, such as human activity categorization and human gender recognition. It's examined from all experiments and results that BAGMM-FS has good modeling capabilities and outperforms other comparable mixture models, especially for high dimensional complex datasets.
All-logical processing based on plasmonic coding metamaterials and multi-objective optimization

Plasmonic devices are considered as a potential platform to realize highly integrated photonic circuits. Optical logic gates are fundamental computing and light-controlling elements in optical circuits and optical computing, and many plasmonic logic gates have been studied. However, these devices can only realize single or several specific logical operations. In this work, we propose a multiport plasmonic system to realize all-logical processing based on coding metamaterials (CMs) and inverse design. We utilize nondominated sorting genetic algorithm-II to optimize the distributions of CMs. After optimization, the simulation results exhibit that all types of logic gates (AND, OR, NOT, NAND, NOR, XOR and XNOR) can be obtained with the operating wavelength of 1.31&mu;m and a small footprint (0.8&times;1.1 &mu;m<sup>2</sup>). Moreover, the extinction ratio between logical "1" and "0" states exceeds 20 dB for OR, NOT, XNOR and XOR logic gates.
Points-of-Interest Recommendation Algorithms for a COVID-19 Restrictions Scenario in the Catering Industry

The COVID-19 pandemic has affected many areas of day-to-day life, including tourism and restaurants. Many countries imposed restrictions on restaurants during the COVID19 period. Many restaurants closed, and others switched to delivery and take-out services. These restrictions affect both the catering system as a whole and smart catering systems, such as recommender systems and user experience aggregators. The main purpose of the article is to assess the impact of COVID-19 on these digital components in different countries, depending on the COVID-19 strategy. In particular, the author’s contribution is as follows: (1) assessing the stability of recommendation algorithms depending on the country’s COVID-19 elimination strategy, (2) identifying factors associated with changes in user behavior during the COVID-19 pandemic, (3) using these factors to improve the recommendation system, (4) answering the counter-question of whether the actual quarantine compliance can be determined using these data. As a result of the experiments, we have identified a change in the accuracy of recommendation algorithms both during and after the lockdown. We also obtained factors for changing user behavior and made assumptions about quarantine compliance in various countries using user experience data. The proposed contextual method has shown increased efficiency during the COVID-19 period.
Config 2.0

Distributed stream processing systems (DSPSs) have enabled us to build scalable, fast, and reactive applications. As faults are common in any distributed system, DSPSs allow for recovering from faults using various techniques, for example, snapshotting their state on a key-value (KV) store and then replaying the events in case of a failure. The performance of the recovery process is dependent on the workload, configuration of the DSPS and performance of the KV store. Performance tuning of a KV store is a notoriously difficult job. In this thesis proposal, we propose a reinforcement learning (RL) based agent which configures the DSPS and the KV store under varying workloads to minimize the impact of a failure on the performance.
A Method to Cloud Computing Resources Requirement Prediction on SaaS Application

Nowadays, it is more important than ever before to accurately predict the cloud computing resource requirement on Software as Service (SaaS) applications. However, the extant methods for requirement prediction are largely designed from the perspective of cloud resource providers, which just results in the insufficient scalability of the models. To solve the insufficiency, a novel method described as Extended Coupled Hidden Markov Model (ECHMM) is proposed in this paper. Based on the historical monitoring data, this paper pioneers to take into account two factors of software evolution and resources correlations in the prediction. Combined with the characteristics of SaaS, it is thus possible for the construction of the relationship between multiple observation states and hidden states. Baum-Welch algorithm is used for the training of the model parameters and the obtaining of the optimal solution. And on a typical SaaS application platform, a cloud computing resource prediction experiment is carried out. The experiment shows that a fuller range of cloud computing resources: the prediction result of the model is closer to the real value. The prediction errors of CPU., memory and network resources are reduced by 2%, 1.7% and 4.5% respectively. And the model can catch an “abnormal” state and get it corrected in time for the increase of scalability.
Learning to Fit Morphable Models

Fitting parametric models of human bodies, hands or faces to sparse input signals in an accurate, robust, and fast manner has the promise of significantly improving immersion in AR and VR scenarios. A common first step in systems that tackle these problems is to regress the parameters of the parametric model directly from the input data. This approach is fast, robust, and is a good starting point for an iterative minimization algorithm. The latter searches for the minimum of an energy function, typically composed of a data term and priors that encode our knowledge about the problem's structure. While this is undoubtedly a very successful recipe, priors are often hand defined heuristics and finding the right balance between the different terms to achieve high quality results is a non-trivial task. Furthermore, converting and optimizing these systems to run in a performant way requires custom implementations that demand significant time investments from both engineers and domain experts. In this work, we build upon recent advances in learned optimization and propose an update rule inspired by the classic Levenberg-Marquardt algorithm. We show the effectiveness of the proposed neural optimizer on three problems, 3D body estimation from a head-mounted device, 3D body estimation from sparse 2D keypoints and face surface estimation from dense 2D landmarks. Our method can easily be applied to new model fitting problems and offers a competitive alternative to well-tuned 'traditional' model fitting pipelines, both in terms of accuracy and speed.
Infrared image detail enhancement based on guided filtering with APHE

The original image bit width of a cooled thermal imaging camera is 14Bit. 14Bit has a wider grayscale range, higher accuracy, and more image details compared to 8Bit, but the maximum grayscale range that can be displayed by a general display is 8Bit, so the original image data of 14Bit needs to be compressed effectively. In this paper, we propose an image compression and display algorithm based on guided image filtering (GIF). First, the image is layered using guided image filtering, i.e., the base layer and the detail layer. Then the adaptive platform histogram equalization (APHE) process is applied to the base layer to improve the contrast of the image; the adaptive detail enhancement is applied to the detail layer to enhance the details while reducing the noise. Finally, the images processed separately after layering are linearly fused to achieve dynamic compression and detail enhancement of high Bit images. Through simulation and comparison experiments with commonly used algorithms, as well as comprehensive comparison of visual effects and quantitative evaluation parameters, the proposed algorithm in this paper achieves significant improvements in both performance and effectiveness.
Research and Practice of Program Design Ability Prediction Based on SVR

Early warning of students' academic conditions play an important role in the entire teaching practice. Nowadays, most students learn programming through the programming platform to practice problems, and then improve their programming level. Various submission records on the platform can obtain a variety of characteristic data through secondary processing; SVR algorithm is used to analyze the data characteristics of test scores, find out the correlation between scores and teaching links, provide teaching guidance for teachers, and remind students to improve the quality of learning in advance.
Guidelines for the design of digital knowledge: Empowering Bangladeshi communities to improve their water safety

Bangladesh has the largest proportion of people exposed to arsenic contamination in water. Studies have shown that awareness campaigns have positively influenced communities regarding their choice of safe water sources. In order to reach and engage communities in adopting safe water supply systems, there is scope in using smartphones to help in monitoring, operation and maintenance and bringing awareness around water safety. The Tapp-BDP app is being developed to tackle this. Its desired functionalities are: water quality field testing, service delivery, payment, interaction, and a knowledge domain on water safety. Specific to the knowledge domain of the app, the challenge is to digitally convey a complex topic in an understandable manner to improve trust within the water supply system and to empower the stakeholders to take charge of their own water supply. ICT provides the opportunity to quickly convey knowledge in new, attractive ways, but is not always inclusive to all. Through the investigation of the complex water safety context, the attributes of the target group as well as using smartphones as a communication channel the outcomes of this project resulted in a set of guidelines that support the development of digital knowledge.
Used Mobile Phone Recognition Method Based on Differential Evolution - Deep Forest

With the continuous development of the mobile phone industry, used mobile phone (UMP) recycling has become a hot topic. At present, few experts research UMP recognization (UMPR) methods based on UMP recycling equipment. Deep forest (DF) identification model of UMPs for intelligent recycling equipment has been proposed, which has many parameters to be manually adjusted. Aim at the above problems, this paper combines differential evolution (DE) algorithm and DF model to propose a method for identifying UMPs. First, the candidate multi-scale feature parameters and DF parameters are given out. Next, we use DE to find the optimal parameters with DF accuracy as the evaluation criterion. Finally, we feed the obtained optimal parameters into the DF model to construct the final UMPR model. Simulation results based on mobile phone pictures of the Ministry of Industry and Information Technology prove the effectiveness of the method.
Automatically Producing Semantically Tagged Bilingual Terminologies

Abstract Even though many NLP resources and tools claim to be domain independent, their application to specific tasks is restricted to some specific domain, otherwise their performance degrade notably. As the accuracy of NLP resources drops heavily when applied in environments different from which they were built a tuning to the new environment is needed. This paper proposes a method for automatically compile terminologies from potentially any domain. The proposed method takes as reference the set of domains defined by Magnini, the Multilingual Central Repository (a resource based on WordNet 3.0) together with DBpedia , an open knowledge source that had proven to be reliable for restricted domains. Using the method described in this article, we have produced a big set of reliable terminologies for 164 domains and 2 languages totalling 635,527 terms. The proposed method has been applied to English and Spanish languages but it is potentially applicable to any language that has its own a DBpedia evolved enough. The obtained results have been intensively evaluated in several ways.