Reviewerly/exHarmony huggingface dataset:
Adversarial Robustness in Deep Learning: From Practices to Theories

Deep neural networks (DNNs) have achieved unprecedented accomplishments in various machine learning tasks. However, recent studies demonstrate that DNNs are extremely vulnerable to adversarial examples. They are manually synthesized input samples which look benign but can severely fool the prediction of DNN models. For machine learning practitioners who are applying DNNs, understanding the behavior of adversarial examples will not only help them improve the safety of their models, but also can help them have deeper insights into the working mechanism of the DNNs. In this tutorial, we provide a comprehensive overview on the recent advances of adversarial examples and their countermeasures, from both practical and theoretical perspectives. From the practical aspect, we give a detailed introduction of the popular algorithms to generate adversarial examples under different adversary's goals. We also discuss how the defending strategies are developed to resist these attacks, and how new attacks come out to break these defenses. From the theoretical aspect, we discuss a series of intrinsic behaviors of robust DNNs which are different from traditional DNNs, especially about their optimization and generalization properties. Finally, we introduce DeepRobust, a Pytorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. Via our tutorial, the audience can grip the main ideas of adversarial attacks and defenses and gain a deep insight of DNN's robustness. The tutorial official website is at https://sites.google.com/view/kdd21-tutorial-adv-robust.
Epipolar-Constrained Optical Flow Triangulation for the Interior Problem in CBCT

When looking at the sequence of cone-beam x-ray projection images of an object, humans can intuitively make some assumptions about the shape and size of the scanned object judging by its motion field during the acquisition. This works even in highly truncated scenarios by intuitively extrapolating the observed part of the motion field to the occluded outer regions. Based on this observation, we present a method that allows to infer knowledge about the shape and size of the object under investigation even far outside of the scan field of view of a truncated cone-beam x-ray acquisition. By making use of the optical flow vectors computed from a sequence of projection images, the correspondences between points of interest in consecutive projection frames are established and triangulated back to locate their projective origins in 3D space. In a previous work we have shown as a proof of concept that this can be used as an estimation of the patient's maximum extent. Here, we add further epipolar constraints to the general method which allows for obtaining more precise structural and shape information about the patient outside of the commonly reconstructable scan field of view. Results show that from severely truncated clinical projection data of the human skull, this method enables to locate parts of the anterior skull far outside of the scan field of view and therefore can be used to e.g. optimize the fit of an extrapolation, to initialize iterative reconstruction methods or even for deep learning-based methods.
A GitHub Project Recommendation Model Based on Self-Attention Sequence

In this paper, we propose a new GitHub recommendation model based on self-attention mechanism that considers user's historical operation sequence. It includes a project embedding layer, multiple encoder layers and a prediction layer. The main idea of our method is to add a position vector to the original project embedding vector to indicate the sequence information of the current project in the user's operation sequence. And considering that the next possible operation project of the user is largely determined by the previous project, model includes a residual connection to the encoder layer. Evaluated our method on a variety of large, real-world datasets, and it shows quantitatively that our outperforms alternative algorithms, especially on sparse datasets. The model can capture personalized dynamics and is able to make meaningful recommendations.
A Radar Image Compression Algorithm Based on Machine Vision

Modern radars have high resolution and large data volume, and the echo contains a large amount of radar image data, this has caused great difficulties for the high-speed transmission and real-time storage of radar data. The existing radar compression algorithm has high complexity and cannot meet the real-time requirements. In response to this problem, through the analysis of the actual radar echo data, it is found that the data has the characteristics of strong correlation and short correlation length. On this basis, this article is based on machine vision and morphological image processing, by setting the neighborhood search range and priority, to improve the search method of the radar image target contour. Experimental tests show that this radar lossy compression algorithm has low complexity and real-time characteristics.
Self-Supervision By Prediction For Object Discovery In Videos

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data, and unsupervised settings pose many challenges, such as finding the right inductive bias in diverse scenarios. In this paper, we propose an object-centric model for image sequence representation that uses the prediction task for self-supervision. By disentangling object representation and motion dynamics, our novel compositional structure explicitly handles occlusion and inpaints inferred objects and background for the composition of the predicted frame. Using auxiliary losses to promote spatially and temporally consistent object representations, we train our self-supervised framework without the help of any annotation or pretrained network. Initial experiments confirm that our new pipeline is a promising step towards object-centric video prediction.
What Machines Can Learn About Our Complex World - and What Can We Learn From Them?

We live in a complex world where we are overwhelmed by data, but information is hard to extract and the consequences of our actions are difficult to predict. Humans have learned to navigate this complexity quite efficiently. Mathematics, statistics, and machines are still far less capable. In this short note, I provide a few general considerations, and my perspective, about modeling real, complex systems and about the automation of this process with learning machines.
Multi-channel Feature Extraction and Super-Resolution Reconstruction of Remote Sensing Images

Abstract Super-resolution reconstruction is an imaging method to improve image resolution. It refers to reconstruct a clear high-resolution image from a low-resolution image. High-resolution remote sensing images can provide more detailed information and higher density, but in the field of remote sensing, because of the limitation of the hardware and vast distances, the remote sensing images are fuzzy sometimes. To facilitate subsequent tasks, this paper proposes a multi-channel feature extraction generative adversarial remote sensing image reconstruction method. According to the characteristics of remote sensing image, a generator is designed, which adds Laplace operator to enhance the edge information of the image, and uses multi-channel feature extraction, which not only enhances the ability of feature extraction but also reduces the number of parameters. In this paper, the super-resolution reconstruction task is carried out based on the 2X magnification factor, and the experimental results are evaluated on SET5/14 and NWPU-RESISC45 dataset. The experimental results show that the images generated by this method have a higher detailed texture and better super-resolution reconstruction effect of remote sensing images.
Multi-criteria choice of the software package architecture for automating the analysis of the forest vegetation state

Abstract The article describes a method for solving the problem of choosing the architecture of a software package (SP) of an information system for analyzing the state of forest vegetation, carried out using distributed information resources and calculation modules which usually belong to various organizations. The SP quality indicators can be assessed using both quantitative and qualitative scales. The proposed method is based on the joint application of the mathematical apparatus of the fuzzy sets and experimental design theories. It is shown that the predominant type of architecture for this class of tasks is the service-oriented architecture, which provides the best value of the integral indicator of the SP quality.
Investigation of Grammatical Ambiguity of Most Frequent words of the Russian Language

During our previous research, we found that the grammatical ambiguity of most frequent words of European languages has a different distribution in comparison with less frequent ones. In the current research, we investigate in more details the reasons of such a phenomenon; we pay a special attention to the first thousand of most frequent tokens. Our investigation of modern disambiguation systems demonstrated that the increase of language diversity, we had found for most frequent words, leads to increase of number of mistakes made by those systems..
Research on the Model of Academic Status Based on Deep Learning

For universities, the quality of talent training is particularly important. This article attempts to use machine learning methods to build a predictive model, using undergraduates’ entrance scores and the scores obtained in the first two years of university as the data source, With the increase of training times, the prediction accuracy of the model proposed in this paper is better than other algorithms, which can explain the method proposed in this paper. Has a certain universality and promotion.
Classification of RIGID and Non-Rigid Transformations with Autoencoder Representations

Feature matching in transformed images is critical to many fields of computer science, from autonomous robots to video analysis. However, most widely used feature matching algorithms vary in their ability to track features depending on whether rigid or non-rigid image transformations occur. This makes it critical, especially in real-time calculations, to be able to identify what kind of transformation is taking place quickly in order to deploy the best feature matching algorithm for that type of transformation. The proposed research uses a combined autoencoder and neural network classification model to classify rigid or non-rigid transformations in order to improve feature matching on the image pairs. This system is the first to perform this kind of analysis with representation learning and opens new ways to improving feature matching performance. We show that using this method improves the amount of feature matches found between correctly identified image pairs.
Multi-sensor Detection Alliance Based on Improved Plant Cell Colony Algorithm

In order to acquire better multi-sensor detection alliance schemes, an improved plant cell swarm algorithm is proposed. The algorithm is proposed based on the natural phenomenon that the whole plant has the maximum contact area with the sunlight due to the sunward nature of the plant in the growth process, which includes five steps: initialization of plant cell population, determination of the location of the strongest light, distribution of auxin, calculation of growth rate and update of plant cell population. The simulation part compares the improved plant cell swarm algorithm (IPCCA) with the basic plant cell swarm algorithm (BPCCA), particle swarm algorithm (PSOA), bee colony algorithm (BCOA) and wolf colony algorithm (WCOA) to verify the effectiveness of the improved plant cell swarm algorithm. The simulation results show that the improved plant cell swarm algorithm has the strongest optimization ability, can effectively avoid premature and jump out of the local optimal solution, and has good global search ability in dealing with multi-sensor detection alliance problem.
Validation of Object-Oriented Static and Dynamic Metrics

Dynamic metrics play a vital role to uncover features like dynamic binding, polymorphism, runtime cohesion, etc., whereas static metrics failed to capture these features of object-oriented (OO) programming languages. Many techniques exist in literature to perform dynamic software analysis, whereas aspect-oriented programming (AOP) has a transparent edge on other approaches. Therefore, in this paper, a new set of dynamic metrics for cohesion, complexity, and polymorphism measures of OO software is proposed. AspectJ tool evaluates these metrics by designing new aspects using aspect-oriented programming (AOP) on the Eclipse platform. Further, theoretical validation of proposed metrics is done based on Briand’s framework, and it is concluded that the proposed set of dynamic metrics satisfies all the properties of Briand’s framework and helps the software industry improve software quality.
Advantages of Green Products and Services: Successful Cases among Listed Companies in China

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Decision Support System for Selection of Quality Doves with Web-Based Analytical Hierarchy Process (AHP) Methods

The purpose of making this system is to make it easier for breeders and pigeon lovers to determine high quality pigeons. The method used is the analytical hierarchy process (AHP), this method is used to support completing the program, using the analytical hierarchy process method, using the analytical hierarchy process method. this is because it can make it easier to make decisions. The result of this system is a web-based system that supports windows and linux operating systems, the purpose of making this system is to facilitate the decision making of high pigeons in determining quality pigeons that are integrated with database storage. The benefit of using this system is to facilitate the decision-making process for quality pigeons.
Technology and crime prevention: A systematic review of literature

In order to identify the application of technology in crime prevention, a systematic analysis of literature on technological tools applied to crime prevention was carried out. 41 articles published in journals indexed in the Scopus, Proquest, Dialnet, Redalyc and Alicia databases from the last ten years were reviewed. Eight technological tools were identified. During the last 10 years they have been applied in the attention of criminal processes. The most frequently applied technological tools are: Artificial Intelligence and Information Technology. It is concluded that the use of technology in crime prevention is more and more frequent and that its greater application could contribute to the reduction of crime.
Application of big data technology in blockchain computing

Under the information age, a large number of structurally complicated and unmanageable data generated by all walks of life is increasingly multiplied. As a newly emerging technology, compared with the traditional data management methods, blockchain is characterized in advantages such as decentralization, de-trustness, and data encryption, which enables it to solve data management problems existing in big data applications in a more efficient way. Since government big data involves huge economic and social values, it is of great significance these curity sharing of government big data for the transformation of the government and social demand patterns. Taking government big data as an example, this paper has analyzed the feasibility of big data security sharing that is based on blockchain, and proposed the demand model and support plan of big data security sharing. Last but not least, it expounded the characteristics of big data security sharing based on blockchain technology, so as to provide beneficial references for the e-government big data security sharing of the government.
Irony recognition combined with LDA and improved one-dimensional intra-attention model

For short texts with ironic emotions, because the data is sparse and irony features are difficult to predict and extract, which causes the problem of substitution of irony text recognition accuracy, a more accurate irony recognition model is proposed. On the basis of the internal attention model, focus on the unique contradictory emotional vocabulary of ironic sentences; then, simultaneously input the sentences into the LDA model to obtain the maximum probability topic of the short text and use the Bi-LSTM model to obtain the two-way semantic dependence of the text; Finally, before the prediction layer, the above three are spliced for softmax classification. Compared with traditional irony recognition models such as LSTM, it has achieved better results on the Weibo comment data set and Ptacek data set.
Cohabitation Discovery via Spatial and Temporal Clustering

Recent years have witnessed the rapid development of deep learning in various aspects, such as image classification, face recognition, and object detection. Yet, these approaches often focus on a single entity. The relationship between different entities is remained to be explored. Cohabitation is a kind of important relationship. In a scenario of residential entries, knowing the relationship of cohabitation could a) prevent tailgaters; b) identify unregistered strangers, and especially c) prevent the disease from spreading during the Cov-19 period. In this paper, we propose a method combining computer vision with graph algorithms to discover the cohabitation relationships as long-term and regular co-occurrence. We demonstrate the method beneficial to both industrial and technical aspects.
Prediction of Stock Prices Based on Markov Chain

Markov Chain is a random process with Markov property in probability theory and mathematical statistics, which exists in discrete exponential set and state space. The essence of the Markov chain prediction model is “no after effect”. No after effect generally refers to the state of things in the future is only related to the state of this stage and has nothing to do with the state in any previous stage [3]. The Markov chain suitable for continuous exponential set is called Markov process. But it is sometimes regarded as a subset of Markov chain, namely Continuous-Time MC, CTMC, and Discrete-Time MC, DTMC correspondingly. So, Markov chain is a relatively broad concept. Based on the Markov chain, this paper makes a prediction on the closing price of Shanghai Stock Exchange Index. The stock market is risky. There are many ways to predict the stock market, which can be summarized into two categories: stock price fluctuation prediction models based on statistical theory [1] and artificial intelligence prediction models [2]. The paper introduces a brief introduction of Markov chain and uses the case of the closing price of the above stock index to measure the accuracy of Markov chain price prediction.
On the Influence of Agile in the Usage of Software Development Practices

Background: Agile Software Development has been around for almost 20 years and it is now mainstream. Many companies claim to be agile but what that really means is not clear. There is a wide scope of software engineering practices in use nowadays, some are technical and some organizational, and some are agile and some are not agile at all. Objective: In this study we aim to understand the influence o f A gile in the set of practices that are currently in use in the software industry. Method: The data from HELENA Survey, a large-scale international initiative, was analyzed. Results: The practices with higher usage rate are strongly related to Agile, some of them were introduced by agile methods while others are pre-agile. Those that were introduced by agile methods are mainly organizational practices, while those that are pre-agile are mainly technical practices. Conclusion: The main usage of agile nowadays in software development is related to people and work organization; and the most popular technical practices are those pre-agile practices that were included in the agile methods.
Information system development method for domain ontology reuse

research-article Information system development method for domain ontology reuse Share on Authors: Bin Liu Shijiazhuang Campus of Army Engineering University, China Shijiazhuang Campus of Army Engineering University, ChinaView Profile , Baojun Wu Shijiazhuang Campus of Army Engineering University, China Shijiazhuang Campus of Army Engineering University, ChinaView Profile , Xinxin Huang Shijiazhuang Campus of Army Engineering University, China Shijiazhuang Campus of Army Engineering University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 113Pages 1–5https://doi.org/10.1145/3469213.3470316Online:18 August 2021Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Drawventure: Teaching Design Sketching Through Gameplay

How can we make design sketching a more engaging, bite-sized practice in our daily schedules? Learning design sketching typically involves repeated practice of fundamentals. This approach can be discouraging and demotivating. We want to enable more self-directed learning and thereby democratize the design sketching education. In this project, we paper-prototyped and user-tested an application called Drawventure that reframes sketching lessons as micro-challenges, where player sketches become objects that populate an increasingly engaging toy world.
Application of Computer Multimedia Technology in English Teaching in Higher Vocational Colleges

Abstract The application of computer multimedia in English Language Teaching (ELT) in higher vocational colleges has greatly promoted its teaching reform and development. However, there are still many deficiencies and problems in the process of using multimedia technology in ELT in most vocational colleges, such as the lack of attention to multimedia tech, low cognitive level, and poor information literacy of teachers and so on. Based on this, this paper first analyzes the ELT model of computer multimedia technology, then studies the construction of computer multimedia ELT system, and finally gives the application strategy of computer multimedia technology in Higher Vocational ELT.
Computer vision application to determine crowdedness in public transport stations

Computer vision has advanced greatly in the last years, providing a useful tool to extract information from images and video streams, enhancing the capabilities of classical video detection algorithms. In the transport field, it may be used in many applications, like information systems (for people and vehicle count, identification of lost objects, etc.), but is mature enough to be trusted also safety systems (monitoring rails for foreign objects, illegal crossings, and many more). In this article, the authors present a solution to detect crowdedness in public transport stations, that can be helpful for local authorities/public transport operators to make proper decisions regarding anti-Covid measures, but also enhance the response of the public transport system.