Reviewerly/exHarmony huggingface dataset:
Multi-interest Network Based on Double Attention for Click-Through Rate Prediction

Whether in the field of personalized advertising or recommender systems, click through rate (CTR) prediction is a very important task. In recent years, Alibaba Group has done a lot of advanced research on the prediction of click through rate, and proposed a technical route includes kinds of deep learning models. For example, in the deep interest network (DIN) proposed by the Alibaba Group, the sequence of users' browsing behaviors is used to express their interest features, and this sequence is made up of items clicked by users. Usually, the item is mapped into a static vector, but the fixed length embedded vector is difficult to express the user's dynamic interest features. In order to solve this problem, Alibaba Group introduced the attention mechanism in the field of natural language processing (NLP) into deep interest network (DIN), and designed a unique activation unit to extract the important informations in the user's historical behavior sequence, and use these important informations to express user's dynamic interest features. In this paper, we propose a novel deep learning model: Multi-Interest Network Based on Double Attention for Click-Through Rate Prediction (DAMIN), which based on Deep Interest Network (DIN) and combined with multi-head attention mechanism. In the deep interest network (DIN), the attention weight between the candidate item vector and the item vector of the user's historical behavior sequence is learned by fully connected neural network. Different from deep interest network (DIN), we design a new method, which uses the reciprocal of Euclidean Distance to represent the attention weight between two item vectors. Then, the item vectors in user's historical behavior sequence are weighted by the attention weights and meanwhile the candidate item vectors are also weighted by the attention weights. In the next, we can obtain new item vectors by add the weighted item vectors of user's historical behavior sequence and weighted candidate item vectors, and those new item vectors are used to represent user's dynamic interest feature vectors. In the end, the user's dynamic interest features are send into the three multi-head attention layers, which can extract users' various interest features. We have conducted a lot of experiments on three real-world datasets of Amazon and the results show that the model proposed by this paper acquires a better performance than some classical models. Compared with DIN, the model proposed in this paper improves the average of AUC by 4%–5%, which proves that the model proposed in this paper is effective. In addition, a large number of ablation experiments have been carried out to prove that each module of the proposed model is effective.
Prediction in the Presence of Response-Dependent Missing Labels

In various settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label is an unknown function of the data. For example, satellites used to detect forest fires cannot sense fires below a certain size threshold. In such cases, training datasets consist of positive and pseudo-negative observations (true negatives or undetected positives with small magnitudes). We develop a new methodology and non-convex algorithm which jointly estimates the magnitude and occurrence of events, utilizing prior knowledge of the detection mechanism. We provide conditions under which our model is identifiable. We prove that even though our approach leads to a non-convex objective, any local minimizer has an optimal statistical error (up to a log term) and the projected gradient descent algorithm has geometric convergence rates. We demonstrate on both synthetic data and a California wildfire dataset that our method outperforms existing state-of-the-art approaches.
Research and Application of Chinese Loanwords Based on Multiple Corpora

According to the statistics of the Language Application Research Institute of the State Language Commission, there are more than 1000 new Chinese words in Chinese language every year, among which there are many loanwords. This papetakes Chinese loanwords as the object of study and analysis, aiming to further promote the development of Chinese foreign exchange and inheritance through the research and application of Chinese loanwords in multiple corpora and expanding the comparative study of Chinese and Japanese homographs.
An analysis of learner’s strategies in Spanish-Korean translations : Focusing on relative clauses in news texts.

This study aims to examine how structural differences between Spanish and Korean in relative clauses affect the achievement of the translation goal of communicative translation. For this purpose, relative clauses of Spanish news texts used in actual translation class for graduate students (ST) and their translations in Korean (TT) were collected. For the analysis, the clauses were divided according to cases such as subjective, objective, oblique, possessive and appositive cases, and categorized into two types: restrictive and non-restrictive clauses, given that the two languages differ in the use of relative clauses. The analysis reveals that translation learners adopt distinct strategies for different types and cases of relative clauses by using connective endings, altering parts of speech in target language or switching the position of antecedent, etc. in accordance with the principle of conciseness of news texts. On the other hand, findings show that translations taking into account the differences between Spanish and Korean turn out to be more TL reader friendly, thus more communicative. The study concludes that when translating Spanish relative clauses into Korean, learners should consider the heterogeneity between the two languages, understand the function of the text to be translated and review the translation strategy from the perspective of the TL reader.
Blockchain in Supply Chain: Great Potentiality for Perfecting Logistics Information Transmission

In modern society with the rapid development of technology, the application of blockchain shows great potential. Currently, the supply chain logistics model has a series of problems in information transmission, which cannot meet the increasing demand of the industry. However, blockchain technology can effectively make up for these defects. Blockchain which based on de-centralization technology ensures the security and reliability of information, thus greatly improving the transparency and efficiency of the supply chain. In the future, the combination of blockchain technology and IoT can also further boost the development of the supply chain. However, in the current practical production, there are still some problems between the blockchain and the supply chain, making the degree of integration of the two in the application layer is not high. This paper aims to discuss the application potential and current challenges of blockchain technology in supply chain logistics in the future and to provide ideas for the application research of intelligent logistics system based on blockchain.
Detection of characters ink dot defects on the surface of industrial products

In order to solve the problem of long time-consuming and poor adaptive ability of the current character printing quality inspection system, a character quality detection system based on machine vision is proposed for the inkjet printing method. Prior knowledge is used to divide the entire character area into individual character areas. Then template matching is carried out for each single character area. The template matching result is further operated to find the inner area of the character.The inner area of the character refers to the area that should be printed by inkjet printing. The dot area detection method is used for the inner area of the character to detect whether there are missing ink dots or blurred ink dots, as well as inaccurate ink dots. The detection system has good performance under uneven illumination and machine jitter conditions in actual production line. The detection speed of a single character can reach within 15ms, and the detection speed of a single sample can reach within 300ms. After experimentation, the comprehensive detection success rate of the three defects is 86.68%, and the false detection rate reaches 0.71%.
Visual Saliency via Selecting and Reweighting Features in Hierarchical Fusion Network

Recently, computational models based on deep neural networks have made impressive progress in predicting the visual saliency of human beings. Relying on the powerful capability of some pre-trained networks, various models can extract diverse deep features. However, they are unaware of the problem of feature selection and reweighting when predicting saliency. This situation gives rise to features describing scene distractors potentially also contributing to the saliency maps. In this paper, we propose a feature selection and reweighting module (FSRM) for deep saliency prediction models. Through the FSRM, we wish to highlight the saliency-related features in a manner similar to channel attention and simultaneously exclude distractor features by reducing the channel number of deep features. Specifically, in the FSRM, we obtain an importance descriptor of feature channels, where some saliency knowledge including the center prior and rarity is encoded. Furthermore, the number of feature channels is reduced via a transformation matrix derived from the importance descriptor. To predict the saliency, the FSRM is embedded in a hierarchical fusion network that makes use of multi-level features. Experiments and ablation studies show the effectiveness and generalization capability of the FSRM in the saliency prediction.
Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM

Human activity recognition has been applied in various areas of life by utilizing the gyroscope and accelerometer sensors embedded in smartphones. One of the functions of recognizing human activities is by understanding the pattern of human activity, thereby minimizing the possibility of unexpected incidents. This study classified of human activity recognition through CNN-LSTM on the UCI HAR dataset by applying the divide and conquer algorithm. This study additionally employs tuning hyperparameter to obtain the best accuracy value from the parameters and the proposed architecture. From the test results with the CNN-LSTM method, the accuracy rate for dynamic activity is 99.35%, for static activity is 96.08%, and the combination of the two models is 97.62%.
Website-Based Data Processing for Pregnant Women with the "Sejiwa Dengan Jempol" Innovation Program at the Jabon Health Center

For pregnant women, patient data is very important for the puskesmas, the patient data as a reference for each pregnancy consultation. such information regarding pregnancy data must be designed in a way that makes it easier to find patient data during pregnancy consultations for specific purposes. sejiwa information system, which is a system that can provide information during pregnancy consultation, nutrition for mothers and children in an agency company. the sejiwa information system is expected to make it easier for the community when pregnancy occurs and does not experience stunting, one of which is by presenting complete patient information and regarding the ongoing sejiwa with jempol innovation program at the jabon health center so that it can be reviewed at any time.
Handling leukaemia imbalanced data using synthetic minority oversampling technique (SMOTE)

Abstract High dimensional data always lead to overfitting in the prediction model. There are many feature selection methods used to reduce dimensionality. However, previous studies in this area of research have reported that an imbalanced class raises another issue in the prediction model. The existence of the imbalanced class can lead to low accuracy in the minority class. Therefore, high dimensional data with imbalanced class not only increase the computational cost but also reduce the accuracy of the prediction model. Handling imbalanced class in high dimensional data is still not widely reported in the literature. The objective of the study is to increase the performance of the prediction model. We increased the sample size using the Synthetic Minority Oversampling Technique (SMOTE) and performing the dimension reduction using minimum redundancy and maximum relevance criteria. The support vector machine (SVM) classifier was used to build the prediction model. The leukaemia dataset was used in this study due to its high dimensionality and imbalanced class. Consistent with the literature, the result shows that the performance of the shortlisted features is better than those without undergoing the SMOTE. In conclusion, a better classification result can be achieved when high dimensional feature selection coupled with the oversampling method. However, there are certain drawbacks associated with the use of a constant amount of synthesis of SMOTE, further study on different amounts of synthesis might provide different performances.
Learning Domain Invariant Representation via Self-Rugularization

Unsupervised domain adaptation often gives impressive solutions to handle domain shift of data. Most of current approaches assume that unlabeled target data to train is abundant. This assumption is not always true in practices. To tackle this issue, we propose a general solution to solve the domain gap minimization problem without any target data. Our method consists of two regularization steps. The first step is a pixel regularization by arbitrary style transfer. Recently, some methods bring style transfer algorithms to domain adaptation and domain generalization process. They use style transfer algorithms to remove texture bias in source domain data. We also use style transfer algorithms for removing texture bias, but our method depends on neither domain adaptation nor domain generalization paradigm. The second regularization step is a feature regularization by feature alignment. Adding a feature alignment loss term to the model loss, the model learns domain invariant representation more efficiently. We evaluate our regularization methods from several experiments both on small dataset and large dataset. From the experiments, we show that our model can learn domain invariant representation as much as unsupervised domain adaptation methods.
A Machine Vision Based Medicine Package Printing Three Date Marks Detection Scheme

To solve the problem of misprinting or obscure of Three Date Marks (TDMS) including production date, batch number and the validity on the medicine packaging, a delay-based misplaced difference scheme for medical information detection is presented. Firstly, two medical images are Successively captured by a machine vision detection system, and a character image is obtained through misplaced subtraction operation; Then, a convolution kernel is designed according to the size of characters to widen the strokes, and a specific operation with corrosion and dilation is further utilized to remove speckle noise to enhance the quality of object characters. Finally, a modified weighted median filter is employed to further improve the recognition accuracy. Experimental results show that the detection precision and efficiency satisfy the requirements of medical packaging triple tags detection well.
A Multi-level Watermarking Algorithm for RasterGeographic Data Based on Watermark Information Segmentation Mechanism

Abstract This paper presents a multi-level digital watermarking algorithm suitable for raster geographic data. First, the new problems caused by the multi-level distribution of raster geographic data and its impact on watermark embedding/detection are analyzed; and then, a watermark information segmentation mechanism is designed, the multi-level watermark embedding strategy is established on this basis, thereby building the multi-level digital watermarking algorithm of raster geographic data based on the watermark segmentation mechanism with the traditional robust digital watermarking algorithm as the prototype; finally, the experimental comparison and analysis of the multi-level watermarking algorithm and the prototype algorithm are made. Results show that the proposed multi-level digital watermarking algorithm not only maintains the performance of the prototype algorithm, but also effectively solves new problems caused by the multi-level circulation of raster geographic data, such as multi-copyright protection and multi-user tracking, and has a high practical value.
Size of Local Finite Field Kakeya Sets

Let $$\mathbb {F}$$ be a finite field consisting of $$q$$ elements and let $$n \ge 1$$ be an integer. In this paper, we study the size of local Kakeya sets with respect to subsets of $$\mathbb {F}^{n}$$ and obtain upper and lower bounds for the minimum size of a (local) Kakeya set with respect to an arbitrary set $${\mathcal T} \subseteq \mathbb {F}^{n}$$ .
Body Schema Calibration for VR: Research on the Characteristics in Wide Range Included Direction of Contraction Regarding Forearm

It is difficult for humans to manipulate their virtual body in the immersive virtual system when its size is different from the real body. The authors assumed that this problem is caused by misfit of the body schema and have been working on the research of body schema calibration, which is the method to update the body schema to fit the virtual body. However, in the previous research, the survey of the basic characteristics of updating body schema has been limited to a narrow range in the extension direction. Therefore, in this paper, we have dealt with updating body schema in wider range in both extension and contraction direction, in order to clarify not only the characteristics of updating in the contraction direction but the limits of update. The results showed symmetry characteristics of body schema update in both directions, while the update effect deteriorated in case of extreme changes.
Computer-Aided Detection of Malignant Mass in Mammogram Using U-Net Architecture

X-ray screening image of the breast is called a mammogram and is used to diagnose breast illness like cancer. In this pioneer inquiry on screening mammograms, the normal and abnormal mammogram images have been analyzed. And an automatic CAD model is designed to aid radiologists by reducing manual empower. Computer-Aided Detection (CAD) is considered as a promising framework to diagnosis malignant mass; however, modern-day algorithms are hindered through excessive false predictions like calcium decomposition. Through this work, we implied a topical system for malignant mass recognition. As a substitute for locating candidate mass points and characterizing them, we discover anomaly at once at the entire mammogram slice by applying U-Net architecture. This is the deep learning approach where the discriminant features are identified without manual introspection. This prescribed model has experimented with the images acquired from the online Mammographic Image Analysis Society (MIAS) Digital Mammogram database and Texoma Medical Center (TMC) and it yields an 83% sensitivity rate for MIAS database and an 87% sensitive rate for the TMC database. The ultimate goal is to improve the prediction rate with discrimination between benign and malignant mass categories in well advance.
DIAGNOSIS OF CREATIVE THINKING IN MIDDLE SCHOOL STUDENTS AND ITS ASSISTANCE IN THE PROCESS OF PERFORMING CREATIVE ACTIVITIES

It is often claimed that it is impossible to teach a person to think creatively, but this is not at all the case. Both children and adults can become true “creatives”; the main thing is to achieve a certain level of skill in the ability to act and think creatively. Today, there are many tests, games, exercises that allow you to develop creativity, and this is easier for children than for adults. Is it possible to identify a «gifted child»? This article presents an approximate version of testing and analyzing ready-made data on diagnosing the level of creativity of a child.
A Hybrid Semantic Matching Model for Neural Collective Entity Linking

The task of entity linking aims to correctly link mentions in a text fragment to a reference knowledge base. Most existing methods apply single neural network model to learn semantic representations on all granularities in contextual information, which neglecting the trait of different granularities. Also, these solely representation-based methods measure the semantic matching based on the abstract vector representation that frequently miss concrete matching information. To better capture contextual information, this paper proposes a new neural network model called Hybrid Semantic Matching (HSM) for the entity linking task. The model captures two different aspects of semantic information via representation and interaction-based neural semantic matching models. Furthermore, to consider the global consistency of entities, a recurrent random walk is applied to propagate entity linking evidences among related decisions. Evaluation was conducted on three publicly available standard datasets. Results show that our proposed HSM model is more effective compared with a list of baseline models.
Facial landmark detection transfer learning for a specific user in driver status monitoring systems

The wide variety of human faces make it nearly impossible to prepare a complete training data set for facial landmark detection. Because of this, the performance of facial landmark detection is unlikely to be sufficient for driver status monitoring (DSM) systems. To improve the performance for a specific person (SP) by collecting data about that person, we propose the generator and discriminator model using the Lucas-Kanade assistance (GDA) algorithm for compiling a training data set. Even when data for a specific user can be collected, another issue is how to efficiently, effectively, and quickly re-train the model using an insufficient data set. To address this problem, we propose a novel method of transfer learning in the context of composite backbone networks (GBNet). The assistant backbone of GBNet is trained on a large unspecified people (USP) data set in the source domain and transfers its representation to the lead backbone, which is trained by a small SP data set in the target domain. In addition, we design an assistance loss function with output that is not only close to the SP data set, but also consistent with a USP data set with respect to labeled images. We test the proposed method using the 300 Videos in the Wild (300VW) data set and our own data set. Furthermore, show that the proposed method improves the stability of predictions. We expect our method to contribute to the realization of stable DSM systems.