Reviewerly/exHarmony huggingface dataset:
Zebra crossing segmentation based on depthwise separable convolutions

The research of zebra crossing recognition plays an extremely important role in vehicle detection and blind guidance system. From the experiment, we find a method of zebra crossing detection and recognition based on depthwise separable convolutions. In the coding part, we use depthwise separable convolutions to extract the layer from the trunk model, which has been convolved many times and has certain characteristics. In the decoding part, the PSPNet model is improved, the coding layers with different downsampling generations are used for comparison, and all the extraction layers are stacked to get the prediction results, thus solving the problem of small targets disappearing. Through experimental analysis and data training, the mean intersection over union of zebra crossing segmentation reaches 90.7%, and the average recognition speed is less than 0.1s.
High-Frequency Preserving Image Downscaler

The goal of downscaling an image is to reduce its resolution to a lower resolution, while maintaining the visual characteristics of the original image. Recent learning-based algorithms have shown great improvement over conventional methods in preserving high-frequency information. However, they continue to suffer from various artifacts due to the lack of true high-low resolution image pairs in their training datasets. In this paper, an unsupervised image downscaler that preserves the high frequency content of the original image based on an autoencoder is presented. Specifically, the image downscaler is obtained by extracting the decoder of the developed autoencoder. Furthermore, we propose a preprocessing step that enables image downscaler to any arbitrary scales. Experimental results on five benchmark datasets reveal the qualitative and quantitative superiority of the proposed method at various scales compared to other methods.
The Teacher-Student Conflict and Its Educational Implications

The teacher-student conflict is an important issue that educators pay attention to.The study of the teacher-student conflict has important social and practical significance.The article explains the meaning of the teacher-student conflict of an acquired good fortune, the existence by cooperation and the test of the relationship.It points out the life impact of the teacher-student conflict on the teacher-student relationship and teacher-student interaction.And it reveals the educational implications of the judgment of source of the teacherstudent conflict, cooperation enhancement and harmonious construction.
Multi-layer Network Visualization Based on Force-Directed Algorithm

In order to show the community structure and hierarchical structure of multi-layer networks, a multi-layer network visualization method based on force-directed algorithm is proposed. Firstly, the traditional force-directed algorithm is improved to generate a multi-force guidance network layout result with obvious community characteristics. On this basis, a hierarchical clustering algorithm is used to establish a network hierarchy from bottom to top, allowing users to observe hierarchical network topology through multiple interactive operations hierarchical network topology. In order to verify the effectiveness of the method, two multi-layer network instance data are selected for experiments. The visualization results show that the method can effectively display the community structure and hierarchical structure of the multi-layer network, and help users understand and analyze its structural characteristics.
Reform and Implementation of FIR Filter Experiment Teaching in the Course of Online Digital Signal Processing

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 Effect of Grammatical Understanding to the Sentence Effectiveness on the Students’ Thesis of Semarang University

This type of research is qualitative research that aims to describe and analyze the effect of grammatical understanding on the effectiveness of sentences in student thesis writers at Semarang University. The sample of this research was 70 students from four socio-economic group study programs, which is the undergraduate program of Law, Management, Accounting, and Communication Science. The data source for text analysis was obtained from four theses representing those four study programs. Based on the analysis of the completed questionnaires (70 pieces), overall, 58.22% understood and even 21.82% strongly understood, did not understand as much as 19.68%, and only 0.35% did not very understood of 4 grammatical criteria. The effect of this understanding supports the analysis of the thesis document with the findings of 730 sentences consisting of 636 sentences from the thesis writer and quotes from other people's opinions (94 sentences). It is found that 459 over 636 sentences are grammatical sentences and 177 ungrammatical sentences. The ungrammaticality of the sentence includes sentences without subject, without a predicate, subject and predicate, double subject, double predicate, object preceded by a preposition, subject, and double predicate, repeating the subject, without the object, and inappropriate use of the conjunction maka in front of the subject. The result showed 72,170% of students of Semarang University had the ability to write a grammatical sentence, meanwhile, 27, 830% of students needed to improve their ability to produce the grammatical sentence so that they resulted in a qualified thesis, both its content and its language were sophisticated.
Furniture Sales Application with Android-Based Qr Code Technology (Case Study "99" Furniture Store)

The growth of the furniture industry relies heavily on good marketing activities. In this digital and online era can be utilized in terms of marketing in the furniture industry. Toko 99 Mebel is one of the furniture stores that still use conventional transaction and order recording. So product marketing is less innovative and efficient. The purpose of this research was to build an android-based furniture sales application that added a QR Code scan feature that can be a new innovation in 99 furniture stores in marketing products. QR Code serves as a feature to display a description of the product to be ordered quickly. The method used is waterfall, useful to reduce errors in building applications. Testing in this application using the Black Box method, this test method is very important to test the fungisonality of the application as opposed to internal structure or work. With waterfall method and Black Box testing method in application development can be arranged well and appropriately. As well as being able to participate in the sale of 99 Furniture Stores, because it will further facilitate the process of selling furniture online via android smartphone so that it is expected to increase customer interest to buy furniture in 99 Furniture Stores.
Quality Improvement of Dot Diffused Block Truncation Coding using Convolutional Neural Networks

This paper presents a simple technique for improving the quality of Dot Diffused Block Truncation (DDBTC) decoded image. The proposed method is designed based on the Convolutional Neural Network (CNN) framework. It consists of several layers to suppress the impulsive noise, the blocking artifact, and false contours occurred in the DDBTC decoded image. The proposed framework works in the end-to-end learning for the quality enhancement task. As supported with the experimental results, the proposed method improves the quality of DDBTC decoded image.
Dual-Consistency Self-Training For Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is a challenging task characterized by unlabeled target data with domain discrepancy to labeled source data. Many methods have been proposed to learn domain invariant features by marginal distribution alignment, but they ignore the intrinsic structure within target domain, which may lead to insufficient or false alignment. Class-level alignment has been demonstrated to align the features of the same class between source and target domains. These methods rely extensively on the accuracy of predicted pseudo-labels for target data. Here, we develop a novel self-training method that focuses more on accurate pseudo-labels via a dual-consistency strategy involving modelling the intrinsic structure of the target domain. The proposed dual-consistency strategy first improves the accuracy of pseudo-labels through voting consistency, and then reduces the negative effects of incorrect predictions through structure consistency with the relationship of intrinsic structures across domains. Our method has achieved comparable performance to the state-of-the-arts on three standard UDA benchmarks.
A Design Thinking Approach to the Development of Agile Skills of Digital Economy Professionals

The digital economy requires a person to have interdisciplinary knowledge and broad competencies in different sectors of the economy, rather than a narrow profile. Innovative competencies and creative abilities, the ability to think outside the box and find alternatives under uncertain conditions, are in demand. This article raises the importance of complementary ICT skills for organizations that are actively adopting digital technologies. Key features of design thinking in solving problems of IT specialists' teamwork skills, creative thinking, and adaptation to change are considered. Design Thinking methodology-based project activities of students are highlighted.
The Optimization Strategies on Clarification of the Misconceptions of Big Data Processing in Dynamic and Opportunistic Environments

This paper identifies four common misconceptions about the scalability of volunteer computing on big data problems. The misconceptions are then clarified by analyzing the relationship between scalability and the impact factors including the problem size of big data, the heterogeneity and dynamics of volunteers, and the overlay structure. This paper proposes optimization strategies to find the optimal overlay for the given big data problem. This paper forms multiple overlays to optimize the performance of individual steps in terms of MapReduce paradigm. The optimization is to achieve the maximum overall performance by using a minimum number of volunteers, not overusing resources. This paper has demonstrated that the simulations on the concerned factors can fast find the optimization points. This paper concludes that always welcoming more volunteers is an overuse of available resources because they do not always bring benefit to the overall performance. Finding optimal use of volunteers are possible for the given big data problems even on the dynamics and opportunism of volunteers.
Research on Blended Teaching of Advanced Mathematics Based on Personalized Learning

This paper, under the background of the epidemic prevention and control, based on constructivism under the perspective of personalized learning, in order to the advanced mathematics course as the breakthrough point, using the "learning through + QQ group" online teaching platform, starting from personalized learning, this paper studies online and offline teaching of advanced mathematics, organically integrating "online + offline" teaching and adopting "offline dominant" blended teaching mode, which arouses students' learning enthusiasm and cultivates students' independent learning ability and innovation ability.
Development and Verification of Smart-Contracts for the ScientificCoin Platform

We study the process of creating and testing models of programs in the Solidity language (smart-contracts) for the ScientificCoin crowdfunding platform. This platform is an Internet portal for investing in high-tech projects using blockchain technology. We examine the security of the blockchain-based method of conducting money transactions implemented on this platform and the approach to test and to verify used program code. We analyze the tools and algorithms which allow us to formalize the life cycle of the code in the blockchain system. An example of creating a smart-contract model and the way of checking the feasibility of its functional properties and the truth of invariants using the SMT solver are considered.
Generic Sparse Graph Based Convolutional Networks for Face Recognition

Several graph-based methods have been proposed to perform face recognition, such as elastic graph matching, etc. These methods take advantage of the fact that the face has a graph structure. However, these methods are weaker than the CNNs. With the development of graph convolutional neural networks (GCNNs), we can reconsider the benefits of identifying the graph structure. In this paper, a face image is modeled as a sparse graph. The major challenge is how to estimate the sparse graph. Usually, the sparse graph is based on some prior clustering methods, such as k-nn, etc., that will cause the learned graph to be closer to the prior graph. Another problem is that the regularization parameters are difficult to accurately estimate. This paper presents a generic sparse graph based convolutional networks (GSgCNs). We have three advantages: 1) the regularization parameters are not estimated in the generic sparse graph modeling, 2) non-prior and 3) each sparse subgraph is represented as a connected graph of the most adjacent-relevant vertices. Because the generic sparse graph representation is non-convex, we implement the projected gradient descent algorithm with structured sparse representation. Experimental results demonstrate that the GSgCNs have good performance compared with some state-of-the-art methods.
The effect of consumer portfolio on the risk profile of cloud provider

The economies-of-scale model of Cloud services has brought many financial and technical benefits for organizations. However, recent events like the attack on Dyn and GitHub have shown that successful attacks on big Cloud providers can cause a massive impact on a major portion of the Internet ecosystem. In this project, we will study how the consumer portfolio of a Cloud provider may affect its risk profile. We will use the result to develop a recommender system for choosing a Cloud provider based on consumer security and business requirements. Insights from our research can be used to simulate an alternative more secure market structure for the Cloud ecosystem. We invite fellow researchers to further discuss this idea and possible collaboration.
MPASNET: Motion Prior-Aware Siamese Network For Unsupervised Deep Crowd Segmentation In Video Scenes

Crowd segmentation is a fundamental task serving as the basis of crowded scene analysis, and it is highly desirable to obtain refined pixel-level segmentation maps. However, it remains a challenging problem, as existing approaches either require dense pixel-level annotations to train deep learning models or merely produce rough segmentation maps from optical or particle flows with physical models. In this paper, we propose the Motion Prior-Aware Siamese Network (MPASNET) for unsupervised crowd semantic segmentation. This model not only eliminates the need for annotation but also yields high-quality segmentation maps. Specially, we first analyze the coherent motion patterns across the frames and then apply a circular region merging strategy on the collective particles to generate pseudo-labels. Moreover, we equip MPASNET with siamese branches for augmentation-invariant regularization and siamese feature aggregation. Experiments over benchmark datasets indicate that our model outperforms the state-of-the-arts by more than 12% in terms of mIoU.
People Number Scanner Made on iPhone for Avoiding Big Crowds

The aim of this work is to create a mobile app on iPhone which can scan the number of people in a certain place. The app can help avoiding big crowds in shops, supermarkets and even avoiding traffic jams. Initially it is shows a map which clusters the number of people in a certain place showing the number of people in a circle. The application can identify specific people upon their unique device ID, to reduce false positives. The system shares people location, but everything is anonymous, so in other to see other people the user has to share his location, but totally in a private and safe manner.
The creative component of fostering personal culture among prospective teachers by means of foreign-language education

In the paper special attention is paid to creativity and creative thinking development in the research of fostering personal culture among undergraduate pedagogical students by means of foreign-language education. Creativity is considered one of the essential principles of the civilization progress; creativization of education is one of the main aspects of the intellectualization of education that is associated with revealing personal creative traits and developing the capability to implement creative acts in the educational environment, social and professional spheres. The purpose of the paper is to analyze pedagogical conditions for developing emotional, creative and innovative potential of prospective teachers in various productive intellectual and creative activities using foreign language educational material. The methodological basis for identifying conditions for the development of creative thinking, cognitive, speech-thinking, communicative, regulatory, reflexive skills and the subject position of students for the development of personal intellectual culture of prospective teachers are the ontological, personal, environmental, activity approaches. The efficiency of creative tasks, creative technologies and methods of studying a foreign language is analyzed; these activities are used to train students to get knowledge and skills themselves in order to solve certain problems, to enrich the experience of practical application, to expose independence in creative activity and critical thinking, to foster the traits of a creative personality, to make a personally significant educational product of interaction and collective creativity, self-expression and self-realization of prospective teachers in the process of studying a foreign language at university.
An Analysis of Use of Image Processing and Neural Networks for Window Crossing in an Autonomous Drone

The application with autonomous robots is becoming more popular (Kyrkou et al. 2019), and neural networks and image processing are increasingly linked to control and decision (Jarrell et al. 2012; Prescott et al. 2013). This study seeks a technique that makes drones or robots more autonomous indoors fly. The work investigates the implementation of an autonomous control system for drones, capable of crossing windows on flights through closed places, through image processing (de Brito et al. 2019; de Jesus et al. 2019; Martins et al. 2018; Pinto et al. 2019) using convolutional neural network. Object’s detection strategy was used; through its location in the captured image, it is possible to carry out a programmable route for the drone. In this study, this location of the object was established by bounding boxes, which define the quadrilateral around the found object. The system is based on the use of an open-source autopilot, Pixhawk, which has a control and simulation environment capable of doing the job. Two detection techniques were studied. The first one is based on image processing filters, which captured polygons that represent a passage inside a window. The other approach was studied for a more real environment and implemented with the use of convolutional neural networks for object detection; with this type of network, it is possible to detect a large number of windows.
A System for Visualizing the Three-Dimensional Spherical and Elliptic Spaces

Non-Euclidean geometries and spaces find application in various fields of knowledge. The possibilities of computer graphics and visualization make it possible to present such spaces in a way that makes it easy to explore their properties even for people who are not acquainted with the mathematics of non-Euclidean geometry. Not only that, it is also possible to add some interactivity and allow users to explore such spaces freely. In this paper, we present a system for visualizing three-dimensional spherical and elliptic spaces, which are two examples of non-Euclidean spaces. First, we describe the theoretical basis and some algorithms the system is built upon: the group-theoretic way of exploring non-Euclidean spaces and defining transformations of objects lying in them, an algorithm for implementing camera controls similar to those seen in first-person video games, and an algorithm for implementing the distance fog effect in such spaces. Then, we describe the system created based on these approaches and its capabilities.
Research on the Management Mode of College English Listening Teaching under Computer Multimedia Technology

Abstract In recent years, with the prevalence of educational reform, multimedia system, as a more popular new thing, has been introduced into the classroom by most universities. The rich and efficient teaching characteristics of multimedia teaching system make it popular with many teachers and students. Especially in college English listening class, the application of students’ hearing becomes more and more important. Multimedia education system can also play its role properly. This study summarizes this field from three aspects: the application of computer multimedia technology in teaching, the preparation of multimedia courseware in listening class and the management skills of college English listening teaching in multimedia environment.
Asymptotically good towers of function fields with small p-rank

Over any quadratic finite field we construct function fields of large genus that have simultaneously many rational places, small p-rank, and many automorphisms.
An Improved U-Net Method with Deep Feature for Liver Segmentation in CT Images

Surgery is the best treatment for liver canner, segmentation of livers is one of the most crucial steps in surgery. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. Automatic segmentation of livers remains intractable owing to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The paper proposes a new liver segmentation architecture named DU-Net, which is composed of cascade U-Net based on deep separable convolution and deconvolution. We make use of the Dice loss function for training. Finally, we evaluate the proposed DU-Net on liver segmentation task under the dataset provided by the public 3D-IRCADb (3D Image Reconstruction for Comparison of Algorithm Database).The experimental result demonstrates that the proposed network can outperform the U-Net, FCN and RV-FCN methods in term of the performance measures, such as Dice score, VOE, and RVD.
Optical Fragile Watermarking Based on Visual Cryptography and QR Code

An optical fragile watermarking method is proposed based on visual cryptography and QR code for image authentication and tamper detection. On the one hand, the original image is transformed into a QR watermark image to improve the watermark hiding capacity. On the other hand, the visual cryptography and optical phase coding are fused to encrypt the watermark image to enhance system security. The feasibility, vulnerability and imperceptibility of the proposed scheme are tested through a series of attacks and tampering. The simulation results show that the proposed method can not only have good imperceptibility, but also achieve high detection performance under different attacks and tampering.