Reviewerly/exHarmony huggingface dataset:
Negative Rhetoric Based on the Error Sentences of Foreign Chinese Learners from the Perspective of Computer Aided Syntax Recognition Algorithms

This article first classifies the negative sentences in Chinese and English, summarizes the respective characteristics of Chinese and English negative sentences, and then compares the various negative sentences of the two from the perspective of syntactic form, and finally the main negative sentences of the two are compared from the perspective of semantics. Semantic characteristics of words. The expressions of English negative sentences are flexible, the negative words have multiple parts of speech, the opposite complementarity of the meaning of the negative words is not obvious, and the negative expression logic is fuzzy. However, the expression form of Chinese negative sentences is relatively constant, the part of speech of the negative words is binary, the semantic features of the negative words are mutually complementary, and the negative expression logic is strict. The semantic features of Chinese main negative words and English negative words are not one-to-one correspondence. Experiments show that the phrase correct rate and recall rate reached 75.17%, 73.69%, 65.06%, 56.55% in the closed test and the open test, respectively.
PCG/PCGML evaluations: Introducing panda evaluation using the soft launch

This study takes a new perspective on the procedural content generation (PCG) evaluation problem, extracts current PCG evaluation methods from previous works, and presents a novel classification of these methods while showing each method’s capabilities. Also, the present study introduces a novel concept called Panda Evaluation. Additionally, the soft and hard launches were presented as two evaluation methods and possible building blocks of PE. A group of papers was analyzed to understand previous works and find new opportunities. In doing so, some missing PCG evaluation areas were found, and some new methods were proposed for future PCG evaluations. To the best of our knowledge, this is the first time these concepts have been presented in PCG evaluation.
Design of Multichannel Resource Adaptive Scheduling Algorithm for English Online Translation Teaching

In order to optimize the multichannel resource scheduling of English online translation teaching and realize the goal of adaptive scheduling algorithm, a multichannel resource adaptive scheduling algorithm for English online translation teaching is designed. The clustering algorithm is used to sort out various English online translation teaching multichannel resources and remove redundancy. Build a unified standard specification; establish an English online translation teaching resource scheduling model with three modules of foundation layer, environment layer, and application layer; form an easy-to-find data set; build an adaptive scheduling algorithm function module of system management, standard management, media materials, online courses, courseware library, and test question library; and optimize the integration speed under multiobjective scheduling algorithm; this paper analyzes the multichannel resource scheduling process of English online translation teaching and realizes the research on the designed adaptive scheduling algorithm. The experimental results show that the test results of each functional module of the scheduling platform under the designed algorithm meet the functional requirements, and the update amount of resources is close to the update upper limit, which can reasonably manage teaching resources and improve students’ interest and learning ability.
Algorithm Development Analysis: searching and sorting

The Development and analysis of algorithms are fundamental to all aspects of problem-solving: artificial intelligence, neural networks, fuzzy approaches, networking, operating systems, etc. It requires an understanding of the alternative solutions available for solving a computational problem, including the performance constraints accompanying a particular solution. It also involves an understanding of what it means for an algorithm to be correct as it thoroughly and efficiently solves the problem. Algorithm Development is necessary for traditional and complex problems, which are sometimes challenging to implement. This article gives an exact idea of algorithms and a straightforward approach to understand. There is a need to know the time complexity of an algorithm, sorting data, and searching a particular element in many cases. These operations are explained well and can be used in daily computer applications. A List of sorting and searching Algorithms and their efficiency are presented so that the best algorithm can be chosen.
Piano

Abstract Based on research on the acoustics and perception of piano timbre, the physics and physiology of the keystroke, and the psychology of piano fingering, the chapter explains how that timbre of an isolated tone cannot be varied independently of its loudness but depends on finger-key, key-keybed, hammer-key sounds, and the use of both pedals. The timbre of a chord further depends on the balance and onset timing of its tones, whereby louder tones tend to sound earlier (melody lead, velocity artifact). Both the sustaining pedal and una corda can enhance sostenuto. Leap trajectories are curved and asymmetrical. Optimal fingering is determined by physical, anatomic, motor, and cognitive constraints interacting with interpretive considerations, and it depends on expertise. The spectrum of a piano tone is stretched relative to a harmonic series, which also stretches intervals relative to equal temperament—more so for outer registers and smaller pianos. Keyboard feel and timbre depend on mechanism regulation and hammer voicing.
Trafficformer: A Transformer-based Traffic Predictor

We propose a deep learning model that can pre-dict future traffic. The proposed Transformer-based model is evaluated using the visit data of popular Wikipedia pages for more than 2 years through multiple accessing devices such as mobile and desktop. The experiment results demonstrate that the proposed model can predict the future traffic with high accuracy.
Enhancement of the super pixel-CNN based road segmentation using cycle consistent adversarial network

Understanding road scenes is a fundamental problem in advanced driver assistance systems (ADAS) for having a safe and comfortable driving. In our previous work, we proposed a novel approach to utilise the advantages of enhancement-based segmentation method to improve the road segmentation performance at reasonable computational effort. However, unpaved roads, cast shadows, grass and the side walk near the road boundary are still fuzzy and could not precisely to be identified. They suffer from either insufficient training data or the limitation of higher-order potentials in pairwise Conditional Random Field (CRF) models. To overcome these drawbacks, we propose a semi-supervised refinement strategy based on a modified cycle generative adversarial network (CycleGAN), which is more generalisable by enforcing higher-order consistency without being limited to a very specific class of high-order potentials. Proposed method uses only a fraction of annotated images, which may significantly reduce human annotation efforts. Our contribution is that unlike the existing adversarial learning methods, we proposed a modified generative model with fewer parameters than the original CycleGAN, which improves the performance while decreasing the computational cost. Moreover, we enforce cycle consistency to learn the mapping between 4-D channel unpaired images and label domain. To guarantee that the generated image from our modified network corresponds to the original image, we added the distance between a sub-set of images and their paired targeted label. The adversarial learning procedure is limited to the already predicted road boundary obtained from our recent work, which together with the limited number of annotated images boost the segmentation performance. Experiments on KITTI public road segmentation benchmark shows the effectiveness of the 4-7% of improvement with respect to our previous work based on the super pixel-CNN approach and achieves comparable performance among the top-performing algorithms of recent un/semi-supervised semantic segmentation tasks.
Correlation Filter-Based Visual Tracking with Multi-featured Adaptive Online Learning

While discriminative correlation filter (DCF) has attracted much attention due to its excellent computational efficiency and robustness, factors like occlusion, motion blur, deformation and background interference cause tracking failure. To address these issues, this work proposes improved background-aware correlation filter (BACF) that utilize colour features as a complement of Histogram of Oriented Gradient (HOG) to improve the representation of the target, and implementing adaptive feature fusion in the response layer using Peak to Sidelobe Ratio (PSR) as a reference metric. Moreover, to further reduce the risk of model drift, dynamically adjusting the learning rate to adapt to complex background. Extensive experiments on the OTB-50, OTB-100 benchmarks have shown that the proposed performs well compared against many state-of-the-art trackers, achieving an AUC score of 79.6% on the OTB-100.
Research on visual analysis method of software function testing process

The existing research on software testing visualization is mostly at the code and developer level, and the visualization content is mostly at the code line and document level, and the target user group is very limited. In addition, the thirdparty software evaluation agency involves a large number of paper documents in the testing and review, which is inconvenient to manage, difficult to consult and analyze, and work efficiency is low. In order to solve these problems, we propose a visual analysis framework for software functional testing process. Starting from the functional testing process and the key information of each process, a set of visual analysis process is constructed, the data extraction method of the test document and the visual analysis method of the test process are studied, and the visual analysis prototype system is constructed to support the software testing process. And the key information of each process for quick analysis and further exploration. Finally, we demonstrated the application of the system in actual test projects through case analysis.
A Discrete Matrix-product Operation

Convolution neural networks based on discrete convolution operation play an indelible role in the breakthrough and development of deep learning. However, the convolutional neural network also has shortcomings and drawbacks. It is more inclined to remember the attributes of the learning object, and lacks the learning of the directional information of the attributes of the learning object. In response to these shortcomings and drawbacks, based on the discrete convolution operation, the paper proposes a new operation called discrete matrix-product operation. The paper focuses on the definition and properties of discrete matrix-product operation, as well as the definition and properties and matrix-product theorem of its discrete Fourier transform. It provides a theoretical basis for the proposal of matrix-product neural network and hopes to promote the further development of deep learning
Asymptotic Properties of Minimax Trees and Game-Searching Procedures

chapter Share on Asymptotic Properties of Minimax Trees and Game-Searching Procedures Author: Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 61–90https://doi.org/10.1145/3501714.3501723Online:04 March 2022Publication History 0citation4DownloadsMetricsTotal Citations0Total Downloads4Last 12 Months4Last 6 weeks2 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An open-source natural language processing toolkit to support software development: addressing automatic bug detection, code summarisation and code search

<ns3:p>This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project – acronym for “DEveloper COmpanion for Documented and annotatEd code Reference” – (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering. </ns3:p><ns3:p> The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts. </ns3:p><ns3:p> The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package – “Activities for the developer” – intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection.</ns3:p><ns3:p> Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.</ns3:p>
Solar Irradiation Forecasting for the City of Durban Using Time Series Analysis

As a result of the country&#x0027;s rich solar resources, local and international investors have grown interested in the solar energy industry. The country has sufficient renewable energy resources that can be exploited to generate electricity. Solar power has exposed achievement in the area of electric power generation. The fuel that powers solar energy is Light from the sun and solar radiation. As a substitute for fossil fuel-based energy, renewable energy plays a vital role in developing countries such as those in Africa, Asia, and Latin America. Published publications related to this topic often use mathematical models to model the solar resources instead of measurements. The most preferable data is measured since the effects of weather and pollution are included. The commissioning of regional networks for monitoring solar stations in southern Africa has established a unique source for sun strength measurements in Southern Africa. This study presents sun strength measurements from the solar station in southern Africa Universities Radiometric Network (SAURAN) is compared with NASA data.
A RotBoost Generalized Multiclass SVM Classifier for Automated Face Shape Prediction in Orthognathic Plastic and Reconstructive Surgery During Computer-Assisted Diagnosis and Planning

Artificial intelligence-related researches aids in the planning and simulating orthognathic plastic and reconstructive surgery. It improves patient satisfaction, which is closely related to expectations. Plastic surgeons did not have an accurate solution for predicting the outcomes of post-operative shapes of patients before the surgery using their pre-operative face shapes. Hence, orthognathic plastic and reconstructive surgery requires an efficient framework for Computer-assisted planning to discover the correct patterns from the high volume of numerical and visuals clinical data that is available with respect to face shapes. Hence forth, a novel RotBoost generalized multiclass SVMs classifier involving a large dataset using the 3D morphable model is proposed in this paper. This proposed model assists in recognizing the orthognathic surgery patients&#x0027; face from large data classified with the minimum false rates. Thus, high-quality 3D scans automatically classify orthognathic patients&#x0027; faces and help assess pre-operative and postoperative face shapes. This process also assists the patients to predict the after shapes during medical planning even before commencing the surgery. The proposed approach provides superior performance compared with the existing techniques in accuracy, sensitivity, and specificity.
Fast 3D Object Detection with RGB-D Images Using Graph Convolutional Network

In this paper, a lightweight 3D object detection model using color and depth images is proposed. In recent years, several studies have focused on the application of deep learning to object detection. They use many techniques, including improved feature extraction methods and instance segmentation, to increase the accuracy. However, such 2D object detection has its limitations. Other models and methods are needed to deal with occlusion and to identify 3D positions. In contrast, there have been many studies in this field applying deep learning to 3D object detection. However, many of them are computationally expensive and difficult to run in real time because they deal with dense point clouds. In the proposed model, after feature extraction from the color image, a sparse point cloud is created from the range image to achieve fast object detection. Graph convolution for point clouds and feature extraction with depth information are also used. As a result, the proposed model achieved 56.4 fps when using ResNet34.
Application of data mining in air defense equipment support design

This paper takes typical air defense equipment as an object, analyzes the characteristics of the data in the whole life cycle of equipment, sorts out the data mining requirements in support design of equipment in demonstration, scheme and development stages, including spare parts design analysis, fault analysis, preventive maintenance analysis, state-based maintenance analysis, using data analysis requirements involved in data mining, and puts forward the realizing methods of data analysis and mining. It can greatly improve the comprehensive support design level of air defense equipment.
Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks

The degradation of roads is an expensive problem: in the United Kingdom alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with its own benefits and drawbacks. This paper builds upon the successful processing of magnetic flux leakage (MFL) data by echo state networks (ESNs) for damage diagnostics, by augmenting ESNs with the depth of concrete cover as part of a data fusion approach. This fusion-based ESN outperformed a number of non-fusion ESN comparators and a previously used analytical technique. Additionally, the fusion ESN had an optimal threshold value whose standard deviation was three times smaller than that of the nearest alternative technique, potentially prompting a move towards automated defect detection in ‘real-world’ applications.
Cloud security

A computer cloud is a target-rich environment for malicious individuals and criminal organizations. It is, thus, no surprise that security is a major concern for existing users and for potential new users of cloud computing services. The chapter starts with a discussion of cloud users' concerns related to security in Section 8.1, and then, in Section 8.2, we elaborate on the security threats perceived by cloud users, already mentioned in Section 2.10. Privacy and trust are covered in Sections 8.4 and 8.5. Encryption protects data in cloud storage, but data must be decrypted for processing as discussed in Section 8.6. Threats during processing originating from flaws in the hypervisors, rogue VMs, or a VMBR, discussed in Section 5.15, cannot be ignored. An analysis of database service security in Section 8.7 is followed by a presentation of operating system security, VM security, and security of virtualization in Sections 8.8, 8.9, and 8.10, respectively. Sections 8.11 and 8.12 analyze the security risks posed by shared images and by a management OS. An overview of the Xoar hypervisor, a version of Xen that breaks the monolithic design of the TCB, is discussed in Section 8.13, followed by mobile device security in Section 8.14. Sections 8.15 and 8.16 cover the current cloud security threats and AWS security. Further readings and a set of exercises and problems in Sections 8.17 and 8.18 conclude the chapter.
Decision Graphs

In this chapter, we will introduce the concepts of decision graphs (also referred to as influence diagrams) that are extensions to Bayes networks targeted for sequential decision-making with dedicated focus on evaluating strategies and expected utilities.
Intelligence Tutorial System for Learning Cultures and Local Languages in Indonesia

Indonesia has so many local cultures and languages that spread from Sabang to Merauke, and most Indonesians do not know how to speak two or more regional languages in Indonesia. Most Indonesians only know familiar tribes that are only commonly heard. Because of this problem, governments in the Indonesian provinces have included lessons on Indonesian culture. The lesson will explain Indonesia’s national and local culture for their area. In this way, the government can also help Indonesians learn more about their local language. This paper proposes a solution to create a learning portal website for users, both from Indonesia and other countries, to learn Indonesian culture and regional languages. In this way, the author hopes that users can learn about Indonesian local culture and language and contribute to preserving Indonesian local culture and language. This application was built on a web-based basis by applying the following HTML, CSS, and javascript using Personal Home Pages (PHP) for database connection using MySQL. The built application was designed using a use case diagram and class diagram to describe the database relations.
Training of academic lawyers for implementation of high-tech research projects (“Digital Law”, “Megascience” and “Lex genetica” projects case study)

Abstract The legal profession has both conservative and innovative features. On the one hand, present-day lawyers are the heirs of the centuries-old tradition of global jurisprudence. On the other hand, the technological challenges of the external environment inevitably transform not only the legal map of the world, but also the peculiarities of training for modern legal professionals. The article deals with the main transformational paradigms of legal professionals training, using the example of three high-tech spheres of public life – genetics, digital economy and mega-science installations development. Academic lawyers (legal scholars) are in many ways at the forefront of the present-day jurisprudence, defining both the present and the future of the global legal space. Their training takes longer than the standard education course for lawyers. The data of the Russian legal system and the legal systems of other countries, as well as international legal practice, served as the empirical basis for the research.