Reviewerly/exHarmony huggingface dataset:
MCCR: Learning Multi-order Convolutional Correlations for Recommendation

Graph Neural Networks (GNNs) has been widely used to address the sparsity and cold start problems in recommendation system. By propagating embeddings from multi-hop neighbor nodes among the interaction graph and update target user and item embeddings, GNNs-based methods can achieve better recommendation performance. But those methods directly concatenate the output of each layer and ignore the different influences between different layers, and they simply use the inner product of the user and item’s embeddings to calculate the similarity and make recommendation based on it, which is insufficient to reveal the complex and nonlinear interactions.In this work, we propose to learn multi-order interactions between users and items and capture correlations between different-order information. We design a new recommendation framework MCCR, which treats each layer’s output as differentorder feature, and propose a multi-order interaction module to represent feature interactions. We adopt a multi-layer 3D CNN module to learn high-order interaction signals between users and items in an explicit approach. Through extensive experiments on three real-world datasets, which shows that MCCR evidently outperforms the state-of-the-art methods consistently.
Association Analysis of Case Data Oriented to Legal Texts

With the application of data mining technology in the judicial field, people are eager to excavate the required information in a large number of legal texts, especially some valuable case-related information hidden in a large number of legal texts. Compared with the research of association mining in the criminal field, the civil field is very few. Therefore, we propose a multi-case and multi-personnel association model in the civil field. First, according to the existing legal judgment documents, we use the rule-based method to extract the case elements and personnel information, and store them in the graph database. Second, we used FFSM algorithm to mine the association of the extracted data, namely frequent subgraph mining, and compared it with traditional gSpan algorithm to verify the efficiency of the method. Last, we analyze the experimental results. Experimental results show that our model has certain value in obtaining relevant information of case data.
FeaRS: Recommending Complete Android Method Implementations

Several techniques have been proposed in the literature to support code completion, showing excellent results in predicting the next few tokens a developer is likely to type given the current context. Only recently, approaches pushing the boundaries of code completion (e.g., by presenting entire code statements) have been proposed. In this line of research, we present FeaRS, a recommender system that, given the current code a developer is writing in the IDE, recommends the next complete method to be implemented. FeaRS has been deployed to learn “implementation patterns” (i.e., groups of methods usually implemented within the same task) by continuously mining open-source Android projects. Such knowledge is leveraged to provide method recommendations when the code written by the developer in the IDE matches an “implementation pattern”. Preliminary results of FeaRS’ accuracy show its potential as well as some open challenges to overcome.
On cumulative belief entropy

Cumulative residual entropy and cumulative entropy are new methods based on the cumulative distribution of random variables and could be used to correctly measure the uncertainty of information in probability theory. However, how to determine the uncertain degree is also an open issue in Dempster-Shafer evidence theory (D-S evidence theory), even though there are different kinds of methods now, but they have not considered about the cumulative effect of the belief probability assignment (BPA), so we refer to the cumulative idea and consider about the relationship between the probability theory and evidence theory so that the structure could be transformed to belief intervals of the single subsets, then comprehensively think about the belief function Bel and the plausibility function Pl which as the limitations. As a result, a new method to ascertain the uncertain degree of the information is presented in the background of D-S evidence theory, some numerical analysis and comparative analysis are performed in order to demonstrate the new cumulative belief entropy could provide a optimistic way of measuring the uncertain degree and the results received are in line with expectations well.
Path Planning and Evaluation for Obstacle Avoidance of Manipulator Based on Improved Artificial Potential Field and Danger Field

This paper takes 6-DOF manipulator as the research object, proposes the improved Artificial Potential Field (APF) method to plan the obstacle avoidance path of the manipulator, and combines the Danger Field (DF) method to evaluate the safety of the path planned using APF. According to the characteristics of the manipulator, the kinematics model of the manipulator is analyzed, and the ball envelope algorithm is applied to simplify the physical model of the obstacle. Compared with the traditional APF method, the improved APF searches in the joint space and introduces the joint attraction potential to improve the search speed and accuracy. The problem of local minimum is dealt with by the combination of adding virtual obstacles and increasing joint attraction potential. The improved APF not only has the advantages of good real-time performance and smooth path, but also solves the problem of the traditional APF falling into a local minimum, and combines the danger field method to judge the path rationality to ensure no collision with obstacles. Through simulation verification, the proposed method realizes the obstacle avoidance path planning of the manipulator and the safety evaluation of the planned path.
A Weak Target Tracking Algorithm Implement on Networked Multi-Agent Control Framework

With the rapid development of computer science and technology, target tracking technology related to computer vision has become a hot research topic. Target tracking technology is to find the region of interest from each frame of video sequence to analyze and find the location of the target to be tracked. This paper presents a weak target tracking method under the condition of strong light spot interference. Firstly, the edge extraction method is used to locate and extract the strong spot. Secondly, the pixels of each strong spot are filled with the medium value pixels in the field, which can eliminate the strong spot. When the weak target coincides with the strong spot, it can avoid the problem of tracking target transfer the target due to the strong spot significance. Finally, to evaluate the performance of the proposed method, we carried out a large number of experiments on our networked multi-agent control framework. Through the qualitative and quantitative analyses of the experimental results, we verified that the weak targets tracking performance of the method is effective and robust.
Health conservation as an object of domestic pedagogical dissertation research: a bibliometric analysis

The relevance of the article is due to the increased interest and need of specialists of various categories in the study and implementation of health-preserving activities. The research problem lies in the formation of the readiness of specialists of various profiles to carry out health-saving activities with different categories of the population. The purpose of the research is to analyze the scientific research fund, to show the main directions of research activities on the formation of the readiness of specialists of various categories to carry out health-preserving activities. The research objective is to reveal the essence of scientific approaches to the study of aspects of health-preserving pedagogical activity and the prospects for research activities in this direction. The research methodology is composed of a set of methods of scientific knowledge: analysis, synthesis, generalization. The research results. The article examines the current state of the problem of health-preserving activity in domestic dissertation research presented in the section "Abstracts of theses" of the Unified Electronic Resource of the Russian State Library, which made it possible to fix the set semantic units of the studied scientific and pedagogical content and conduct their analysis, clarify defining various pedagogical categories, replenishing their content line and designing further relevant research. Key findings. A meaningful analysis of the thematic clusters presented in the article allows us to conclude that the process of addressing researchers to this issue is not intensive, orderly and methodologically secure and requires further comprehensive study in the theoretical and methodological field of not only pedagogical, but also psychological, sociological, biological and economic sciences.
COVID-19 Societal Response Captured by Seismic Noise in China and Italy

Seismic with frequencies above 1 Hz is often called noise and is generally correlated quite well with human activities Recently, cities in mainland China and Italy imposed restrictions on travel and day-to-day activity in response to COVID-19, which gave us an unprecedented opportunity to study the relationship between seismic above 1 Hz and human activities Using seismic records from stations in China and Italy, we show that seismic above 1 Hz was primarily generated by the local transportation systems The lockdown of the cities and the imposition of travel restrictions led to an similar to 4-12 dB decrease in seismic power in mainland China Data also show that different Chinese cities experienced distinct periods of diminished cultural noise, related to differences in local response to the epidemic In contrast, there was only similar to 1-6 dB decrease of seismic power in Italy, after the country was put under a lockdown The data indicate that traffic flow did not decrease as much in Italy and show how different cities reacted distinctly to the lockdown conditions
Composite pattern separation with CNN for multi-view structured light 3D reconstruction

Structured light(SL) is a fundamental and important method in 3D reconstruction. Multi-view SL can provide broader and more complete scene geometry than single SL, but the overlap of projected patterns is a fatal problem, and thus it is very much desired to separate signals from the composite pattern. In this paper, we propose a novel learning-based method to decompose patterns for phase-coding SL. The network has a backbone to extract common features and two branches to get the unique features and reconstruct the two components. In addition, by analyzing the process of the pattern overlapping, we propose a novel loss function and ground truth label data by guaranteeing correct phase. Moreover, we also propose a synthetic dataset for training. Experiment results demonstrate that the proposed method successfully separate the composite patterns on our data with noise and light attenuation, and outperforms traditional method such as band pass filter and principal component analysis.
Particle Network Ensemble Kalman Filter

As a typical method of data assimilation, the Ensemble Kalman filter (EnKF) generalizes the linear Kalman filter by introducing a Monte Carlo approximation to predict the means and the covariances. Towards distributed sampling, conventional EnKF usually needs a centralized server to integrate the prediction of all particles, which may cause insufficient computing power or traffic jams on the central node in the case of computing a large-scale model. In this paper, we propose a novel distributed scheme of EnKF designed for network setting of sampling, named by the Particle Network EnKF. This method does not contain any central node and every sampling particle communicates with its neighbors over a locally connected network. Different from the well-known distributed Kalman filter(DKF), this method focuses on the distribution of sampling particles rather than sensors as done by the DKF. The numerical experiments on the Lorenz-63 system show the comparable performance of the proposed Particle Network EnKF to the standard EnKF with proper communication rounds, even on a sparse particle network.
A compact multi-frame blind deconvolution algorithm with the Fourier constraints

Multi-frame blind deconvolution (MFBD) is a commonly used method of image post-processing technique to restore high-resolution image from the image observed through ground-based telescopes. During the restoration process, the frequencies with low signal-to-noise (SNR) ratio in the spectrum of the image which are called “spectrum holes” can easily lead to noise amplification effect. Hence there is a filtering idea by calculating the “spectrum holes” to impose the frequency domain constraint in MFBD. Hundreds of images observed through ground-based telescopes are often needed to obtain high quality images, which makes a considerable cost of computation. We discard the information in the images that have not been selected to improve the speed of the process. In this paper, we use the compact blind deconvolution algorithm (CMFBD). A small number of images with better quality as “control frames” is firstly selected to run the traditional MFBD, under such treatment, we can use relative less time to quickly obtain the PSFs corresponding to the “control frames”, and then, the PSFs corresponding to the “non-control frames” is obtained through the “consistency” principle which we assume object is the same in each frame. In CMFBD the non-selected data frames is used to provide an additional constraint on the PSF estimates for the selected data frames.
$$\ell _\infty $$-Robustness and Beyond: Unleashing Efficient Adversarial Training

Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output. Adversarial training is one of the most effective approaches in training robust models against such attacks. However, it is much slower than vanilla training of neural networks since it needs to construct adversarial examples for the entire training data at every iteration, hampering its effectiveness. Recently, Fast Adversarial Training (FAT) was proposed that can obtain robust models efficiently. However, the reasons behind its success are not fully understood, and more importantly, it can only train robust models for $$\ell _\infty $$ -bounded attacks as it uses FGSM during training. In this paper, by leveraging the theory of coreset selection, we show how selecting a small subset of training data provides a general, more principled approach toward reducing the time complexity of robust training. Unlike existing methods, our approach can be adapted to a wide variety of training objectives, including TRADES, $$\ell _p$$ -PGD, and Perceptual Adversarial Training (PAT). Our experimental results indicate that our approach speeds up adversarial training by 2–3 times while experiencing a slight reduction in the clean and robust accuracy.
Categorization of User Experience and Usability Issues (UXUIs) and their Prioritization w.r.t Categorization for Improving the UXU of Interactive System (IS)

Software user experience (UX) and usability (UXU) plays a vital role in the quality users perceived. The existence of user experience and usability issues (UXUIs) affects the software quality perceived by users. A variety of classification of UXUIs for improving the UXU of IS indicates that there is still no consensus in the respected area. The purpose of this paper is to present the findings of a literature review, with the aim of demonstrating all relevant UXUIs classification that have been introduced so far. This comprehensive view, represented by a knowledge map with time-frame, on the one hand, provides an in-depth understanding of the observed evolution and, on the other, serves as a guide for researchers, experts, and interactive application designers and developers as they work toward designing, developing, and evaluating interactive systems (IS) and its associated interfaces. This, in turn, may assist with sustained engagement among users.
Study on Unsupervised Community Detection Algorithm Based on GCN Model

At present feature information on non-Euclidean distance data can be effectively extracted by the graph convolutional neural network. This paper provides an unsupervised community detection algorithm based on graph convolutional network model for application. The algorithm first selects some nodes in the graph to add artificial flags to simulate the input signal, which can satisfy the requirements of the propagation rule of the graph convolutional network. Then the algorithm would pass the flags to the neighboring nodes through the modified graph convolutional network propagation rule. While comparing the different flags on the same node and getting the attribution result of the nodes, the community partitioning results can be obtained by optimizing the result. Finally, the real-world data is used to test this algorithm and to evaluate by comparing with some classic community detection algorithms. The experimental results show that the algorithm provided by the paper can achieve a better result completely and correctly. in different types of graph data sets.
The Application of PDA in Online and Offline Mixed Teaching of College English

Language teaching and learning has entered the digital era with the wide application of information technology in the field of education. The learning software based on mobile client is the online teaching platform of superstar company. Using mobile client to combine online and offline teaching enabled students can better invest in learning, rather than indulge in games or the Internet.
Statistical Language Models for Spelling Error Detection with Web Search New Word Acquisition

This paper proposed a statistical language models with internet based new word acquisition for spelling error detection and correction. The statistical language models are composed of off-line and online n-grams. The content in internet is regarded as a large dynamic knowledge resource that is updated by the online users over the world. Due to the word various in internet with rapid renew capability; it is useful for new lexicon learning. The proposed approach can achieve significant improvement in spelling checking. Combining these research manners, the proposed approach is able to achieve the goals of confirming, improving the detection rate of typos. For evaluation, a news dataset with ten categories is gathered as test data. Experimental results show that the proposed approach combining internet web searching outperforms traditional N-gram models, especially for the words that are out-of-vocabulary such as transliteration either in precision rate and recall rate.
Educational possibilities of social networks and group work in Engineering Programs

The change in classroom methodologies due to Covid-19 Pandemia come with the application of the Internet and 2.0 tools (mainly social networks) in a country that does not have an equal distribution of internet connection. Furthermore, Universidad San Ignacio de Loyola's competencies model approach based on the constructivism theory led us to research if students' training can be improved by this type of resources as they foster important aspects such as socialization, information searching and the achievement of a common goal, etc. This work aims to analyze the information and communication technology (ICT) university learning processes and student preferences for working either inside or outside the classroom of engineering students. Our objectives are focused on knowing: the students' feelings on social software and its influence on collaborative and group work; the social network tools they use and, if there are any differences in terms of collaborative work perceptions based on a specific students' location. The instrument for data gathering was a three-dimensional questionnaire. The main results are: students are interested in group work as a type of classroom methodology; students have little knowledge of technology tools (except for social networks). These results provide a reliable diagnostic instrument for the variables that comprise this tool.
Efficient and Robust Keyed Hash Function Based on Artificial Neural Networks

In this paper, we propose a new dynamic key and message-dependent hash function based on Artificial Neural Networks (ANN) that satisfies the necessary security requirements with low computational complexity. It requires only two rounds of confusion and diffusion operations. Moreover, a dynamic non-invertible construction technique of a synaptic weight matrix is defined in order to ensure the one-way property. The proposed message authentication algorithm is evaluated in terms of desirable cryptographic properties. The results show that the proposed solution is robust due to the dynamic cryptographic primitives approach, which also results in reduced latency and required resources. The initial weight matrix is changed at regular time intervals depending on the application requirements. Recent enhancement in ANN hardware implementations enables the practical application of the proposed solution within wireless and mobile networks.
An application based on deep learning for cancer diagnosis

Over the years, cancer has been attracting great attention worldwide due to its widespread and lethality. People have been trying to predict cancer using the machine in the hope of early diagnosis and treatment. Experts and scientists have been examining the performance of both basic machine learning techniques and some advanced neural networks on cancer prediction. In this work, we used convolutional neural networks (CNN) to make predictions based on images from histopathologic scans, known for their specialty in image recognition. We chose Visual Geometry Group 16 (VGG16) as our model, and we ran two experiments, one using pretrained parameters and one without. For both models, we ran a total of 10 epochs and recorded the accuracy, precision, and recall on both the training data and the testing data. According to the experiment results, the model trained from scratch had a slightly higher accuracy, with the highest accuracy being 89.01%. In addition, we designed a user interface so that people can access it more conveniently. Users can upload their scanned images, and the model will make a prediction and inform the users. In addition, we also designed a Natural Language Processing (NLP) chatbot to guide the users
A Survey on Information Retrieval System for Authenticating a Tweet

With the growing needs and population, there is tremendous increase in volume of data produced each and every hour. Social sites like Twitter, Facebook etc., produce incredible amount of data and information which are blogs i.e. tweets which are written and updated every minute and second. With this growing data, a need for an Information retrieval system is seen, wherein the essential information or data can be identified and retrieved easily from large databases. Th e collection of information from relevant information resources is Information retrieval system. It is a science of searching data or information in documents also finding documents and also metadata from databases images, text or sounds. As very less search has been done in this field, a need for automated information retrieval system is seen, wherein searching of these documents and information can been done automatically or say using machine learning. In this paper, we proposed a model that will gather data from twitter, in the form of tweets and find whether the given tweet is fake or un fake i.e. spam or not. We will use machine learning algorithms and develop an IR model for it. Our proposed system would be more efficient than the traditions IR models