Reviewerly/exHarmony huggingface dataset:
Research on Working Target Detection of Bulldozer Based on Binocular Vision

Abstract Aiming at the problem of bad working environment of bulldozer in landfill, this paper presents a target detection method suitable for autonomous working bulldozer. Firstly, the working environment information was obtained by the binocular camera, the maximum inter-class variance method was used to remove the sky background by binarization of the image, and the color component of the ground was extracted from the HSV color space and converted into a binarization image. The absolute value of the difference between the two binarization images was the approximate area of the working target. Then the binocular camera was calibrated and the feature points were detected and matched in the target area. The parallax was obtained by triangulation principle to obtain the depth information of the target. The experimental results show that the method can correctly extract the depth information of the target area and target, and the accuracy is high, which can meet the requirements of the bulldozer in the landfill for target detection.
SUBJECTIVITY AS A PREREQUISITE FOR SELF-DESIGN

The article presents indicators for fixing the characteristics of the subjective development of students, obtained in the course of research on the topic of self-design of educational and professional activities. The data on the distribution of respondents according to the three types of subjectivity are given, the specified characteristics of the subjective types are described.
Controlled Neural Response Generation by Given Dialogue Acts Based on Label-aware Adversarial Learning

Building a controllable neural conversation model (NCM) is an important task. In this paper, we focus on controlling the responses of NCMs using dialogue act labels of responses as conditions. We introduce a reinforcement learning framework involving adversarial learning for conditional response generation. Our proposed method has a new label-aware objective that encourages the generation of discriminative responses by the given dialogue act label while maintaining the naturalness of the generated responses. We compared the proposed method with conventional methods that generate conditional responses. The experimental results showed that our proposed method has higher controllability conditioned by the dialogue acts even though it has higher or comparable naturalness to the conventional models.
Performance Analysis of Image Enhancement Techniques on X-Ray Images

Image enhancement is widely used in the area of image processing to improve the quality of image without content manipulation. In the present year, COVID-19 has spread widely in the world which is affecting the lungs of the human body. The X-ray images of human lungs are considered for detecting the patients affected by COVID-19. Therefore, the initial step before detection process is the pre-processing step which involves image enhancement. In the present paper, various methods of image enhancement like Linear transformation, Logarithmic transformation, Power Law transformation, Contrast-Stretching Transformations, Histogram equalization, Contrast limited adaptive histogram equalizer were analyzed based on the parameters like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).
Liner Time Invariant Systems Frameworks are Better than Basic State Machines

Time invariant frameworks are frameworks where the yield doesn't rely upon when information was applied. These properties make LTI frameworks simple to address and see graphically. LTI frameworks are better than basic state machines for portrayal since they have more memory. Direct frameworks are frameworks of conditions in which the factors are never increased with one another however just with constants and afterward summarized. Straight frameworks are utilized to portray both static and dynamic relations between factors. A straight framework is supposed to be reliable in the event that it has at any rate one arrangement; and is supposed to be conflicting in the event that it has no arrangement. Have no arrangement, an extraordinary arrangement, and endlessly numerous arrangements, individually. A direct condition of two factors addresses a straight line in R2. A genuine illustration of a LTI framework is any electrical circuit comprising of resistors, capacitors, inductors and straight enhancers. Straight time-invariant framework hypothesis is additionally utilized in picture preparing, where the frameworks have spatial measurements rather than, or notwithstanding, a transient measurement. Straight time is an idea where by time is seen consecutively, as a progression of occasions that are driving toward something: starting, and an end.
Trust Computing Model of Online Transaction Based on Social Network and Mobile Agent

In view of the deficiency of the existing trust model based on recommendation, on the basis of referring to the interpersonal trust research of sociology, combined with the specific characteristics of C2C online auction, using the collaborative filtering technology of mobile Agent system, a personalized trust computing model based on recommendation is established. The model is based on the idea of social network and forms the virtual social network of target users according to the historical transaction data. By examining the trust factors such as trust situation and trust tendency, the trust path is constructed and the trust degree of potential trading partners is calculated. The empirical analysis of the model is carried out by using eBay data, and compared with the beth model, the results show that the trust calculation model is simple and effective, and has good engineering feasibility.
Survey Analysis for Medical Image Compression Techniques

This paper presents a survey for medical image compression for both lossy and lossless approaches. This survey discusses twenty-five publications with several applied lossy and lossless compression techniques. All approaches are distributed into seven groups based on the applied technique. Fractals, wavelet, region of interest (ROI) and non-region of interest (Non-ROI), and other approaches represent four lossy compression groups. Adaptive block size, least square, and other approaches represent three lossless medical image compression techniques. Technically, medical image communication requires large space to be represented and sent over the network creating a number of challenges in terms of interaction, processing, storage, and transmission operations. Therefore, significant compression ratio (CR) and peak signal-to-noise ratio (PSNR) are always targeted in image communication. Both CR and PSNR are considered in this survey as the main metrics to investigate and evaluate models performance. As a result of this survey analysis, ROI and Non-ROI has shown the best average CR with 91, and wavelet has shown the best average PSNR with 80.
An Auto-Contouring Method for Kidney Using a Novel Semi-Supervised Leaning Extreme Learning Machine Method

Background : Adaptive radiation therapy planning requires contour segmentation of dangerous organs in medical images. However, manual contour rendering is the most time-consuming and laborious work in radiotherap planning. In order to solve this problem, we propose a novel semi-supervised leaning extreme learning machine (SSL-ELM) method to realize abdominal Magnetic Resonance Imaging (MRI) guided Adaptive Radiation Therapy (MR-ART) automatic contour rendering. Method/Material : Our algorithm is based on the assumption that data within the same class are close to each other. We use this heuristic method to improve the ELM algorithm. The experimental results show that our proposed method outperforms existing classification algorithms. We used a data set of eight patients with unresectable abdominal malignant tumors recruited by professionals approved by the Cleveland Medical Center Institution Review Board. Each group included MRI and airborne T1MRI with randomly selected treatment phases. Each MRI consisted of 16 slices with a resolution of 370×370 pixels. Manual and automatic contours of the kidney were compared using Dice similarity index (DSI). Results : The proposed SSL-ELM algorithm has better performance than most classification algorithms, and the experimental results also show that the DSI values are above 0.87, with some samples reaching 0.99.
UDM: Private user discovery with minimal information disclosure

We present and analyze User Discovery with Minimal information disclosure (UDM), a new protocol for user discovery in anonymous communication systems that minimizes the information disclosed to the system and users. UDM solves the following user-discovery problem. User Alice wishes to communicate with Bob over an anonymous communication system, such as cMix or Tor. Initially, each party knows each other’s public contact identifier (e.g., email address or phone number), but neither knows the other’s private platform identifier in the communication system. If both parties wish to communicate with each other, UDM enables them to establish a shared secret and learn each other’s private platform identifier. Unlike existing systems, including those based on private set intersection, UDM learns nothing about the social contacts of the users, is not vulnerable to off-line dictionary attacks that expose contact lists, does not reveal platform identifiers to users without the owner’s explicit permission, and enjoys low computation and communication complexity. Using the anonymous communication system, each pair of users who wish to communicate with each other uploads to the user-discovery system their private platform identifier, encrypted with a key derived from their shared secret. Indexing their request by a cryptographic tag derived from their shared secret, each user can then download each other’s encrypted private platform identifier. In doing so, UDM uses an untrusted user-discovery system, which processes and stores only public information or values encrypted with keys it does not know. Therefore, from the data values it processes, UDM cannot learn any information about the social contacts of its users.
Application of English Score Management System Based on Spark-Decision Tree Algorithm

Abstract This paper is based on the classification decision tree algorithm, based on the Spark cloud platform, through the construction of the decision tree algorithm, and setting AUC parameters, comprehensive comparison of the support vector machine algorithm, and Logistic regression. The results show that the Spark cloud platform has the best robustness, Better realize data forecasting.
Improving the effectiveness of student learning with the help of electronic textbooks

Changes in education contribute to its modernization and renewal. Along with new forms, methods and means of enriching the educational process, the well-known teaching methods using electronic textbooks also hold a positive trend. In the Tomsk Technical School of Railway Transport — branch of the Siberian Transport University electronic textbooks are widely used. The tendency of their application and implementation in educational activities, both with full-time and distance learning, gives good results. Because they use an electronic textbook created for everyone requirements. A plus is the fact that the electronic textbook is developed on the basis of the copyright material of the subject teacher, taking into account intrasubject and intersubject communications. Such development gives a number of advantages over the traditional print publication and allows to achieve higher levels of learning in comparison with traditional forms of training.For students of all specialties, the electronic textbook is an indispensable assistant in the development of disciplines, and for graduates of the specialty “Information Systems” such a textbook is also a good test of their knowledge, skills and competencies. Students of this specialty develop and create electronic textbooks for a technical school under the guidance of scientific advisers and subject teachers, from whom it was requested to present the training course in a multimedia form. as an example, we consider the features of the development, creation, implementation of an electronic textbook on the subject “Astronomy” in the educational process.
Challenges when Applying Repertory Grid Technique for Software Practices

The Repertory Grid Technique (RGT) has been applied within the software engineering domain to investigate a variety of topics. These include topics relating to architectural knowledge, team level tacit knowledge, and project success mechanisms. The technique is based on Personal Construct Theory (PCT) and is claimed to be suitable for gaining a deep understanding of peoples’ perspectives on a topic. The essence of RGT is a consideration of similarities and differences, for example, between different project instances. In this paper, we describe a case study in which we applied the technique with the aim of eliciting practitioners’ viewpoints on contextual factors for situated software practices. We interviewed twelve practitioners in three organisations. We found that the RGT approach was challenging to implement for several reasons. Participants had difficulty in choosing specific instances of a software practice, identifying similarities and differences tended to be problematic and causal pathways were not always easy to establish. Our contributions are the highlighting of the challenges that may occur when implementing this technique, an analysis of the issues encountered and some possible mitigation approaches. These may serve as support for SE researchers considering an RGT based study.
A Hybrid Prediction Model of Recycled Sodium Aluminate Solution Concentration in Evaporation Process

The sodium aluminate solution of the evaporation process is provided for the digestion process to recycle useful resources, decrease alkali concentration in waste solution, and reduce environmental pollution. However, the concentration of recycled sodium aluminate solution as an indispensable indicator for manipulating the evaporation process of the industrial alumina production is acquired offline leading to delayed feedback information. To ensure stable production of the subsequent process, reduce energy, and resource consumption, this article focuses on developing a hybrid prediction model for recycled sodium aluminate solution concentration in evaporation process. First, data reconciliation is utilized to improve the quality of material flow information, and the process mechanism model is established through mechanism research and equilibrium principle to obtain the concentration prediction result. Moreover, an industrial production condition analysis as well as fuzzy expert rules, are introduced for modifying prediction results from the mechanism model. Furthermore, the errors are predicted by the kernel extreme learning machine (KELM), and a concentration prediction model integrated error compensation results and modified predictions are established. The experimental simulations and industrial applications show that the accuracy of the prediction error obtained by the proposed model reaches 90% within ±2%, and the advantages of the hybrid model are particularly prominent under different conditions, which is beneficial for efficient and clean production.
Distant Supervision for Relation Extraction via Noise Filtering

As a widely used method in relation extraction at the present stage suggests, distant supervision is affected by label noise. The data noise is introduced artificially due to the theory and the performance of distant supervision will be restricted during the modeling process. To solve this problem on the sentence level, the task of relation extraction in our project is modeled with two parts: sentence selector and relation extractor. Sentence selector, based on the theory of reinforcement learning, processes the corpus in units of entity pairs. The training corpus is divided into three parts including selected sentences, discarded sentences, and unlabeled sentences. We try to obtain more semantic information of the training corpus by introducing the intra-class attention and inter-class similarity. To make the operation of filtering noise data more accurate, this model evaluates the predicted value produced by the relation extractor between the selected and discarded sentences in the sentence package. The result shows that the redesigned reinforcement learning algorithm WPR-RL in this study can significantly improve the deficiencies of the existing approach. At the same time, we also carry out a number of composite tests to discuss the impact of each improvement on the performance of the model.
Design and analysis of a robust breast cancer diagnostic system based on multimode MR images

<abstract> In this paper, we propose a Robust Breast Cancer Diagnostic System (RBCDS) based on multimode Magnetic Resonance (MR) images. Firstly, we design a four-mode convolutional neural network (FMS-PCNN) model to detect whether an image contains a tumor. The features of the images generated by different imaging modes are extracted and fused to form the basis of classification. This classification model utilizes both spatial pyramid pooling (SPP) and principal components analysis (PCA). SPP enables the network to process images of different sizes and avoids the loss due to image resizing. PCA can remove redundant information in the fused features of multi-sequence images. The best accuracy of this model achieves 94.6%. After that, we use our optimized U-Net (SU-Net) to segment the tumor from the entire image. The SU-Net achieves a mean dice coefficient (DC) value of 0.867. Finally, the performance of the system is analyzed to prove that this system is superior to the existing schemes. </abstract>
An Image Defog Network Based on Multi-scale Feature Extraction and Weighting

The research on image defogging is a long-running topic. With the wave of artificial intelligence and deep learning sweeping the world, the research on image defogging based on deep learning has gradually reached a new height. However, the following problems are still emerging, such as severely distorted image color after defogging, image jagging, and image blurring and so on. In response to these problems, this paper proposes an improved image defogging network based on the traditional CycleGAN. Different from the single feature extraction of traditional CycleGAN, this paper adopts a multi-scale feature extraction method. Combining small-scale convolution and large-scale convolution with residual network for in-depth feature extraction, and at the same time, the extracted features are weighted and fused through the attention module to make the extracted network information more complete. Finally, a clear and fog-free image is reconstructed through upsampling. Compared with the traditional defogging network, the improved network can better avoid the problems of image color distortion and blurring.
Say No to Case Analysis: Automating the Drudgery of Case-Based Proofs

I present an argument that long, tedious proofs requiring a human to check many cases should be replaced by an algorithm, so a computer can do the work instead.
Intelligent System of Ecological Civilization Construction based on Communication System Optimization Model

Civilization construction of information society needs a solid industrial foundation, which means that the development of ecological civilization, and improve the level of ecological civilization construction, need to be built on the basis of emerging industries. From the content of ecological civilization construction, the collaborative governance system of ecological civilization construction includes collaborative governance of environmental protection, and collaborative governance of social ecological concept construction. Environmental protection refers to taking protective measures to the fragile ecological environment or the ecological environment that may be damaged, so as to maintain the existing state or develop in a better direction. This paper introduces the related technology of communication optimization theory, and constructs the intelligent system of ecological civilization construction.
DEVELOPING SHORT STORY WRITING TECHNIQUE BOOK BASED ON READER RESPONSE TO LITERATURE

The aim of this research is to develop an effective book to write short story for students at Indonesia Language and Literature Education Study Program. The specific aims of this research are (1) to develop short story writing technique book based on the reader’s response to literature, (2) to develop an applicable book for writing short story, and (3) to develop an effective book in writing story. Research and development method is used namely (1) self-evaluation, (2) experts panels, (3) one-to-one evaluation, and (4) rapid prototyping. Beside, needs assessment is conducted. The result is that the writing book technique based on the reader’s response is effective for writing short story.
Task-relevant graph to propagate labels for few-shot classification

Few-shot learning aims to learn a classifier with limited training instances to recognize unseen classes in test. Recently, some effective few-shot learning approaches have achieved promising classification performance. However, these approaches do not pay attention to the importance of task-relevant features in image classification. To address this issue, we propose Task-Relevant Graph Metric Learning method that adopts a novel meta-learning framework for transductive inference. Specifically, we first extract task-relevant features by a Squeeze-and-Excitation module. Then learning a graph construction module in order to obtain the manifold structure in the data. Afterward, self-training is utilized to propagate labels from labeled instances to unlabeled test instances. Experiments on benchmark datasets demonstrate that TRGML improves classification performance (4%-5%) over baseline systems on miniImageNet and tieredImageNet.
Work-In-Progress-Exploring VR Conference Navigation Employing Audio Cues

According to the World Health Organization, there are more than one billion people with moderate or severe distance vision impairment or blindness worldwide in 2020. Due to recent COVID-19 safety measures, online events have moved to online platforms in order to maintain physical distancing. The transition towards online gatherings have shown gaps associated with technology and internet access, along with lacking accessibility, which has resulted in reactive responses to improve usability by adding captioning, compatibility with screen readers, and image captioning to cite some examples. However, traditional video conference lacks immersion and presence otherwise found in virtual reality (VR), for example Mozilla Hubs, Engage VR, AltspaceVR, and Virbela amongst others. Although these services and consumer-level hardware are becoming ubiquitous, there are still accessibility challenges to be met when designing VR experiences. In this work-in-progress paper we explore the design of an inclusive web-based VR application that simulates registering at a virtual conference and navigating to a presentation room. Our preliminary study focused on usability and engagement by inviting participants to experience the demo eyes closed. Our preliminary results show the audio cues were helpful to navigate the environment without visual feedback.