Reviewerly/exHarmony huggingface dataset:
Programming and execution models for next generation code intelligence systems (keynote)

McCarthy defined Artificial Intelligence (AI) as “the science and engineering of making intelligent computer programs”. I define Code Intelligence (CI) by specializing his definition to intelligent computer programs that analyze other computer programs (PAPs for short) and use it to structure the keynote in two parts. The first part argues for more research on engineering foundations of PAPs. The second part outlooks new directions of combining rule-based intelligence of current PAPs with learning capabilities. Together, novel engineering foundations and enhanced intelligence characterize what I think will be the next generation of PAPs.
Data Mining Techniques for the Analysis of Student’s Admission Data

With the rapid increase of data, fast, efficient and correct decisions making is becoming more and more important. Data mining is used for analysing data and extracting information and knowledge to support decision-making. This paper briefly reviews data mining process, analyses different data mining techniques and presents results of analysis of student’s admission data using data mining techniques.
OPS-Net: Over-Parameterized Sharing Networks for Video Frame Interpolation

The video frame interpolation algorithm can improve temporal resolution by inserting non-existent frames in the video sequence. With the help of skip connections, many kernel-based methods train deep neural networks to accurately establish the complicated spatiotemporal relationship among pixels in adjacent frames. Still, these connections are only performed in the feature dimension. To this end, we introduce the Over-Parameterized Sharing Networks (OPS-Net) to implement weight sharing under different layers, capable of integrating deep and shallow features more directly. Specifically, we over-parameterize each convolutional layer to capture movement information efficiently, where the additional trainable weights from distinct ones will be shared. After the training, the additional weights will be fused into the conventional convolutional layer and do not increase the test phase’s computation. Experimental results show that the proposed method can generate favorable frames compared with several state-of-the-art approaches.
Research on the Reform of General Education Learning Model in Private Colleges under the Big Information Environment

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.
Analysis on the Current Situation of Financial Management Accounting Personnel Training Program and Curriculum in Colleges and Universities in the Era of Big Data

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.
The peculiarities of design and development of software in the context of digitalization of natural science education

The goal of this research is to develop the theory and practice of synergetic design and development of software for digital physical laboratories to increase the level of compliance of behavior of the object with reality and reduce the level of student&amp;rsquo;s immersion into the virtual environment.&amp;nbsp;One of the tasks for achieving this goal lies in using a synergetic aspect in design and development of software for implementation in the process of teaching natural science disciplines, which allows consolidating virtuality and reality of the surrounding world.&amp;nbsp;In the course of&amp;nbsp;this research, the authors substantiate the synergetic aspect in the development of software for digital laboratories in the context of taking higher school physics.&amp;nbsp;The authors build the main scenario and functional scheme of the software product, which are realized on the high-level C++ language using OpenGL libraries.&amp;nbsp;Software implementation of the digital physical laboratory required&amp;nbsp;preparation of videos with a series of physical experiments, determination of the course of execution of laboratory task, and examples of &amp;ldquo;returning&amp;rdquo; from virtual reality.&amp;nbsp; The developed use case diagram of the actors allowed determining the behavioral pattern of the parties to the development, as well as using the digital physical laboratory in the educational process.&amp;nbsp;Synergetic aspect and possibility of its implementation were tested on the example of digital laboratory work in the context of studying the pendulum motion and fall of bodies.&amp;nbsp;The promising development trend is the design and implementation of software products for determination of synergistic effects in studying the sections of optics and thermodynamics.
Axiological aspects of foreign language teachers’ preparation for primary foreign language education

The article outlines axiological basis of professional training of English teachers, capable to develop communicative, cognitive and moral development of junior schoolchildren when they master a new language and facts from a different culture. The article refers both to the history of methodological research, in which the issues of the formation of professionally significant skills of a foreign language teacher were studied, and to the modern problems of the formation of the teacher’s personality and his readiness for pedagogical activity. The article examines linguistic values and substantiates a new goal of professional training of a future foreign language teacher for the primary stage of secondary school, taking into account the priority of his spiritual ability and recognition of professionalism as its fundamental characteristic. The authors associate the formation of professionalism with the disclosure of the personal potential of the future teacher, which is an integrative phenomenon, the components of which are the basis for determining the new content of teacher training as a person capable of realizing the educational and developmental potential of primary foreign language education.
Self-Adaptive Swarm System (SASS)

Distributed artificial intelligence (DAI) studies artificial intelligence entities working together to reason, plan, solve problems, organize behaviors and strategies, make collective decisions and learn. This Ph.D. research proposes a principled Multi-Agent Systems (MAS) cooperation framework -- Self-Adaptive Swarm System (SASS) -- to bridge the fourth level automation gap between perception, communication, planning, execution, decision-making, and learning.
Improving The Robustness Of Convolutional Neural Networks Via Sketch Attention

The convolutional neural networks (CNNs) is biased towards texture while human eyes relying heavily on the general structure. The inconformity leads to the vulnerability of CNNs. The convolutional results is determined by the local patterns and delicate adversarial perturbation would be amplified layer-wise. Meanwhile the image context and object structure, which can be represented by sketch, stay almost unchanged. Therefore we propose that the sketch information is weak but more robust. In order to transfer the robustness from sketch to image and improve the capture of global structure, a sketch attention guided CNNs (SAG-CNNs) pipeline is constructed. The experiments on CFAR-10 demonstrate that the defensive capability against black-box attacks of SAG-CNNs outperforms other counterparts evidently and achieve preferable trade-off between generalization and robustness.
Kullback-Leibler Divergence-Based Visual Servoing

This paper proposes a Kullback-Leibler (K-L) divergence-based visual servoing scheme. K-L divergence, also known as relative entropy, is a measure of the difference between two probability distributions. By employing the K-L divergence as a new error metric to evaluate the similarity between the actual and desired images, and then formulating the visual servoing process as an optimization problem with the K-L divergence as the optimization objective, the servoing control law can be designed to achieve the positioning task of the robot. The proposed method does not require any feature detection, tracking and matching. In addition, compared with the histogram-based visual servoing method, the proposed method has a faster convergence rate. Numerical simulations carried out on the ViSP platform validate the effectiveness of proposed method.
A survey of multi-party dialogue research based on deep learning

In recent years, with the extensive application of deep learning technology, breakthroughs have been made in the study of human-computer dialogue. However, most of the current human-machine dialogue systems are designed under the assumption that both parties are involved, and the research and application of more challenging multi-party human-machine dialogues are not yet mature. Based on the field of natural language processing, this paper will review the research progress of multi-party dialogue based on deep learning in recent years. First, from the perspective of human-machine dialogue, we sort out the key problems and existing solutions of the multi-party dialogue system; then, we introduce other natural language processing tasks based on multi-party dialogue; afterwards, we summarize the existing multi-party dialogue research dataset and make a comparative analysis of limitations on the existing dataset; Finally, we look forward to the future development trend of multi-party dialogue research.
Report on the first workshop on bias in automatic knowledge graph construction at AKBC 2020

We report on the First Workshop on Bias in Automatic Knowledge Graph Construction (KG-BIAS), which was co-located with the Automated Knowledge Base Construction (AKBC) 2020 conference. Identifying and possibly remediating any sort of bias in knowledge graphs, or in the methods used to construct or query them, has clear implications for downstream systems accessing and using the information in such graphs. However, this topic remains relatively unstudied, so our main aim for organizing this workshop was to bring together a group of people from a variety of backgrounds with an interest in the topic, in order to arrive at a shared definition and roadmap for the future. Through a program that included two keynotes, an invited paper, three peer-reviewed full papers, and a plenary discussion, we have made initial inroads towards a common understanding and shared research agenda for this timely and important topic.
Research on the Construction of a New System of Computer Based Whole Brain Physical Education Teaching and Training Method

Abstract With the introduction of artificial intelligence technology, the traditional teaching methods are no longer used in physical education. The traditional means are not competitive and can not mobilize the enthusiasm of students. However, the new computer-aided teaching has not been recognized by most physical education teachers. There are still great problems in this aspect. Computer aided instruction (CAI) has developed into intelligent computer aided instruction (ICAI). ICAI not only overcomes the weakness of traditional CAI, but also greatly improves the effect and efficiency of physical education. In this paper, the characteristics and functions of CAI system are analyzed, and a corresponding implementation framework is given.
How decentralized systems can upgrade AI

Decentralized systems enhance future algorithmic applications.Centralized algorithmic automation is a very powerful tool for humanity.It fulfills increasingly important roles in society for the betterment of humanity.It optimizes human interaction, outputs, and performance and can elevate most functions of technology-enhanced designs in society.At the same time, the conveniences and benefits that derive from centralized algorithmic automation bring with them risks to humanity that cannot be fully quantified.Decentralized systems can help counteract the downsides and threats of algorithmic automation and thereby enhance the long-term benefits of technological solutions for humanity.
Full-Field 3D Mode Shape Measurement Using the Multiview Spectral Optical Flow Imaging Method

In some modal testing applications, image-based techniques offer compelling advantages compared to conventional sensors, particularly in cases where mass loading is problematic or a high spatial resolution is required. Still, the limited field of view of the well-established stereo DIC method can be a limiting factor in some measurements. By employing the principles of multi-view imaging, this constraint can be alleviated. For linear, time-invariant structures, multi-view triangulation can be performed in the frequency domain. The measurement field of view can in this way be arbitrarily extended using only a single moving camera imaging system. By using the simplified optical flow method in the displacement spectra identification step, a still-frame camera can be used for image acquisition, considerably lowering the complexity and cost of the imaging system. In this work, the spectral optical flow imaging and frequency-domain multi-view triangulation approaches are combined in an effort to identify mode shapes of a simple three-dimensional structure. The aim is to develop a robust and cost-effective single still-frame camera-based modal testing method.
Comparative Analysis of the Lifting Action of Collective Five Person Event in Hubei College Students Aerobics Championship in 2019

Through the research on the lifting action of group A, group B and group C in Hubei College Students' Aerobics Championship in 2019, it is found that in order to strengthen the lifting action level of the collective five person event in Hubei Province, the following aspects should be gradually strengthened. First of all, on the basis of the existing, we should strengthen the basic quality practice of each group. A solid foundation is an important factor to improve the overall level of the team. Secondly, in the arrangement of the lift, athletes and coaches should be bold in innovation, according to the style of music, the quality of the players and other conditions, so that the lifting action in the combination form constantly change. Finally, in order to increase the diversity of lifting movements, while constantly changing the combination forms, top athletes should properly use the difficult movements of turning, turning and splitting legs in the air to increase the diversity of difficult movements.
Zipf’s law analysis on the leaked Iranian users’ passwords

Textual passwords are one of the most common methods of authentication and an important factor in systems security. Knowing the correct distribution of users’ passwords can play an important role in defining password policies and preventing various attacks. Culture and language can affect the pattern of users’ password selection and consequently, influence the vulnerability of passwords to guessing attacks. Therefore, knowing the distribution of English users’ passwords may not be appropriate for the security analysis of non-English users’ passwords. The main purpose of this paper is to analyze the passwords of Iranian users and investigating their differences from English-speaking users. The paper also examines the existence of Zipf’s law on Iranian passwords as the most well-known distribution for passwords. Password analysis of Iranian users shows that the popular password length between Iranian users and users of other countries is not much different, but in terms of the combination of characters used in the passwords, Iranian users are more inclined to use numeric passwords while English language users are more inclined to use passwords made up of alphabet. In this paper, Zipf’s law is reviewed on five datasets of Iranian users’ passwords using three different approaches including PDF, PDF with removing unpopular passwords and, CDF. Among these methods, in the CDF method, the passwords best matched with a Zipf’s law distribution between 0.02 and 0.07. Finally, the robustness of Iranians’ passwords to statistical guessing attacks has been measured and it is concluded that the passwords of Iranian users are more vulnerable to guessing attacks than English language users.
Algorithm for Path Planning of Mobile Robot

Research in Unmanned Surface Vehicles (USVs) and Autonomous Mobile Robots navigation has demonstrated some success in navigating flat indoor environments while avoiding obstacles. But in common resilience to unsafe conditions, unmanned surface vehicles (USVs) and autonomous mobile robots have wide applications in security reconnaissance, investigation of an obscure domain, and crisis reaction. Various examinations have been directed on the driving mechanism, motion planning, and trajectory tracking strategies for robots, yet restricted investigations have been led with respect to the obstacle detection and avoiding ability of robots. However, for little scale robots that contain sensitive surveillance sensors and can't afford to utilize heavy defensive shells, the nonappearance of obstacle avoidance solutions arrangements would leave the robot helpless before possibly risky hindrances. In this paper, we present an algorithm for obstacle detection and avoidance system has been developed for miniature Unmanned Surface Vehicles (USVs) and autonomous mobile robots. We show an investigation differentiating heading build rules and relative course directions dodging distinctive operators in multi-specialist conditions, estimating separation between the two robots. The integration of distance measurement and avoiding other robots or obstacles is our observation in a multi-robot environment, where obstacles are also available. In this paper, we have discussed two algorithms, one will try to avoid other robot or obstacles and another will try to measure the distance between main agents to another agent. It utilizes an algorithm so that the system is both compact and power efficient. The proposed system can detect not only the presence, but also the approaching direction of a ferromagnetic obstacle. Therefore, an intelligent avoidance behavior can be generated by adapting the trajectory tracking method with the detection information. Design optimization is conducted to enhance the obstacle detection performance and detailed avoidance strategies are devised. Experimental results are also presented for validation purposes.
RETRACTED ARTICLE: Mechanical properties of basin loess based on image recognition and aerobic exercise data monitoring of athletes

Image recognition technology is an important part of machine vision applications. In the process of image recognition, the perception mechanism must exclude the input redundant information and extract important information. Loess is rich in calcium carbonate, and its particles are mainly composed of silt soil with obvious pores between soil particles. Loess in the basin is characterized by high water content, easy collapse, easy collapse, and strong permeability. Loess is very vulnerable to the influence of water, which greatly reduces the support of loess. Most of the quality problems of loess foundation-related projects in the basin are caused by insufficient support. In this paper, the loess in the watershed is selected as the investigation object. Firstly, in order to investigate the change of mechanical properties of loess, freezing thawing cycle, alternation of drying and wetting, and non-direct shear and compaction tests were carried out under these combined effects. Then, combined with the numerical simulation software geo-studio, the effects of temperature field, infiltration field, freezing and melting cycle, and wetting and drying alternation on the slope stability were simulated. Aerobic exercise is helpful to maintain the physiological health of the subjects; the results show that aerobic exercise with small intensity, moderate intensity, and medium intensity has a positive effect on the inhibition and conversion function of athletes’ brain; aerobic exercise of three intensities can promote the development of brain refresh function; the results show that aerobic exercise of small plus medium intensity and small plus intensity has a positive effect on the brain activation of switching and inhibition function when athletes complete cognitive tasks. Based on the image recognition technology, this paper discusses the mechanical properties of loess in the basin and studies the monitoring of aerobic exercise data of athletes.
PARROT: An Adaptive Online Shopping Guidance System

With the development of e-commerce, it is necessary to build an online shopping guidance system to help users to choose the products they desired. Task-oriented dialogue systems can be used as an online shopping guidance system in e-commerce websites. Current dialogue systems can only extract basic attributes which are the inherent attributes of products. These systems can not process users’ requests containing high level attributes which describe products’ functions and user experience. These requests, however, appear frequently in real scenarios. To solve this problem, we build PARROT, an adaptive online shopping guidance system. PARROT can extract both basic and high level attributes from dialogues and recommend suitable products to users. The novel features of PARROT are as follows: (1) We propose a new architecture of task-oriented dialogue systems which can extract both basic and high level products’ attributes (functional attributes and experience attributes). (2) We construct knowledge base to map from high level attributes to basic level attributes or products. (3) We build a task oriented dialogue system which can finish the task of shopping guidance in websites. We test PARROT in three main scenarios and these tests demonstrate that PARROT can successfully recommend suitable products to users by extracting both basic and high level attributes.
Adaptive Signal Variances: CNN Initialization Through Modern Architectures

Deep convolutional neural networks (CNNs), renowned for their consistent performance, are widely understood by practitioners that the stability of learning depends on the initialization of the model parameters in each layer. Kaiming initialization, the de facto standard, is derived from a much simpler CNN model which consists of only the convolution and fully connected layers. Compared to the current CNN models, the basis CNN model for the Kaiming initialization does not include the max pooling or global average pooling layers. In this study, we derive an new initialization scheme formulated from modern CNN architectures, and empirically investigate the performance of the new initialization methods compared to the standard initialization methods widely used today.