Reviewerly/exHarmony huggingface dataset:
Preparation Phase (Preparations in SAP ECC System Before SUM) – Part 1

When converting to SAP S/4HANA, the preparation activities are the key to the success of the project. If the preparation activities are incomplete, SUM verifies this during its run and throws error messages. If the error is severe (such as return code 12), SUM stops running in the first phase. If the severity is low (such as return code 8 or 7), SUM stops or sends a message during its third or fourth phase.
Efficient Subgroup Discovery Through Auto-Encoding

Current subgroup discovery methods struggle to produce good results for large real-life datasets with high dimensionality. Run times can become high and dependencies between attributes are hard to capture. We propose a method in which auto-encoding is applied for dimensionality reduction before subgroup discovery is performed. In an experimental study, we find that auto-encoding increases both the quality and coverage for our dataset with over 500 attributes. On the dataset with over 250 attributes and the one with the most instances, the coverage improves, while the quality remains similar. For smaller datasets, quality and coverage remain similar or see a minor decrease. Additionally, we greatly improve the run time for each dataset-algorithm combination; for the datasets with over 250 and 500 attributes run times decrease by a factor of on average 150 and 200, respectively. We conclude that dimensionality reduction is a promising method for subgroup discovery in datasets with many attributes and/or a high number of instances.
All-Russian Institute of Scientific and Technical Information of the Russian Academy of Sciences: Goals, resources and prospects

The goals of All-Russian Institute of Scientific and Technical Information as the Center-Generator of databases and head organization of the State System of Sci-tech Information are defined. VINITI current status and potentialities for processing world flow of sci-tech information and user services are analyzed in brief. Factual data on its resources and user services are provided. The challenges and prospective vectors of VINITI information and technological modernization are examined.
A comparison of the difficulty level of reading passages between Korean College Scholastic Aptitude Test (KCSAT) and International Baccalaureate (IB)

This study compared reading passages of the two tests used for college admissions decisions: the Korean College Scholastic Aptitude Test (KCSAT) and the International Baccalaureate (IB) test. The reading passages of the two tests were compared in terms of length, syntactic complexity, lexical diversity, readability, and topics. The analysis showed that the passages in the KCSAT were shorter in length but were syntactically more complicated and lexically more diverse than those in the IB test. Consequently, the reading passages in the KCSAT were found to require a higher reading ability than the IB test. These findings that the KCSAT required a higher reading ability than the IB challenged the legitimacy of the difficulty level of the KCSAT, given the fact that English is used as the medium of instruction in IB. Some suggestions for the future directions of the KCSAT are provided based on the research findings.
Digital fingerprint extraction method of IOT devices based on Cryptography

In order to improve the identification efficiency of devices in the perception layer of the Internet of Things, reduce the cost of calculation and protect data privacy, a device fingerprint extraction method based on lightweight national secret algorithm is proposed. Firstly, the feature information of embedded module is introduced to construct a comprehensive feature set, and the reasonable feature subset is determined based on the feature selection strategy of expert information weighting mechanism; Secondly, the lightweight cipher algorithm with high security is selected to transform the feature subset data into device fingerprint; Finally, based on the electric energy acquisition equipment in the power Internet of things, the fingerprint value extracted meets the requirements of uniqueness and unforgeability, and has the characteristics of fast response, which verifies the feasibility of the method.
Vulnerable Underground Entrance Understanding for Visual Surveillance Systems

Visual surveillance is of critical importance in public places and especially in underground entrance environments where large chunks of sloping glass ceilings are particularly susceptible to various types of terrorist activity. However, owing to the diversity of underground entrance environments, understanding them remains a challenge. Traditional 3D layout and object pose estimation that are evaluated on 3D point clouds or RGB-D data are energy-consuming and difficult to account for semantic information in environments. In this study, we present a methodology to understand underground entrance environments, and to recover their 3D reconstruction from a monocular camera. Clusters of sloping angle projections are extracted. Through their corresponding vanishing points (VPs), surfaces of sloping structures are estimated. Relative geometric constraints of different planes are built to bridge the gap between 2D sloping surfaces and 3D reconstruction without precise depth or point clouds. An underground entrance scene is approximated by Manhattan and sloping non-Manhattan structures in 3D reconstruction. The approach requires no prior training, and it requires neither the camera being calibrated nor the camera internal parameters being constant. Compared to the ground truth, the percentage of incorrectly understood pixels were measured and the results demonstrated that the method can successfully understand underground entrance scenes, meeting the requirements in safety monitoring for critical infrastructures from a resource-constrained surveillance camera.
Preface of the “The First Symposium on Information, Educational Technology, Applied Mathematics and Engineering (IETAME 2020)”

First Page
The Making of Multimedia Courseware of Communication Countermeasure Reconnaissance Principle

Based on the principle of the communication countermeasure reconnaissance “course as an example, this paper introduces the design of multimedia CAI courseware in detail strategies, overall design scheme and application skills, puts forward the general strategy of multimedia CAI courseware, the structure of the multimedia CAI courseware is analyzed, finally gives the multimedia CAI courseware production skills, make full use of existing technology, It is feasible to make CAI courseware with advanced multimedia technology including video, audio and image, which has certain application and practical value.
Trajectory-Based Anomaly Classification of 6-DOF Guided Missile Using Neural Networks

This paper proposes a learning-based anomaly classification of the guided-missile for performance evaluation. Identifying the anomaly of a missile from the performance metrics is difficult while analyzing the detailed parameters from the Monte Carlo simulation requires expertise in the field. Inspired by recent studies, machine learning is applied to extract features from time-series trajectory data to evaluate the performance of the guided missile. 6-DOF simulation data are used to train the model, and four network structures are tested for the anomaly classification problem. The results show that utilizing the time-series feature from the data enhances the classification accuracy and relational learning reduces the network size significantly.
IncreGNN: Incremental Graph Neural Network Learning by Considering Node and Parameter Importance

Graph Neural Network (GNN) has shown powerful learning and reasoning ability. However, graphs in the real world generally exist dynamically, i.e., the topological structure of graphs is constantly evolving over time. On the one hand, the learning ability of the networks declines since the existing GNNs cannot process the graph streaming data. On the other hand, the cost of retraining GNNs from scratch becomes prohibitively high with the increasing scale of graph streaming data. Therefore, we propose an online incremental learning framework IncreGNN based on GNN in this paper, which solves the problem of high computational cost of retraining GNNs from scratch, and prevents catastrophic forgetting during incremental training. Specifically, we propose a sampling strategy based on node importance to reduce the amount of training data while preserving the historical knowledge. Then, we present a regularization strategy to avoid over-fitting caused by insufficient sampling. The experimental evaluations show the superiority of IncreGNN compared to existing GNNs in link prediction task.
Digital Interactive-Documentary Model in the Framework of Subject Ontology System Analysis

The system analysis during the modern documents developing at the level of regional and municipal structures had been. The structure of a temporal interactive document in the framework of the document life cycle model and interactive-documentaries models had been considered. The model of a temporal interactive document (DITD) as five objects set in an electronic environment had been proposed, that let to interact with DITD in the framework of space and time, as well as in the framework of the logical or associative sequence of speech or non-speech signs. The composition of program modules, knowledge bases and databases that is related to the temporal interactive document had been briefly presented. The purpose of our publication is to propose the detailed description of DITD model that had been designed for conflict situations resolving during the digital communications’ implementation by the narrative in the virtual DITD space representation, where are all stakeholders in the decision-making process has been participated. The processing tools of the document fragments structures synaptic framework in accordance with the document providing temporary chain of additional content sources support dynamics had been described in the “initiator of the N-link of the interactive”\“N-link trigger interactive” subtext resume format. This is the result of the preprocessor using for the text corpus of primary sources processing by the following content n-units distribution that are pertinent to the context of ontological inventories over the following slots with the defining of properties of the reality discourse implicature execution control of the problem area events sequence.
Digital Transformation and Innovation (DTI) through the Learn-Practice-Implement (LPI) Pedagogical Approach

Digitalization has been one of the hottest industry trends over the last decade, but staff training in the manufacturing, services, and construction sectors has not been able to catch up with technological advancements. This is because many Small and Medium Enterprises (SMEs) and even some Multi-National Corporations (MNCs) are still unsure on how to transform their business models, how to implement digital solutions and how to apply the relevant technologies to address the pain points related to their digital needs. Today Heads of Business Unit, Heads of Department, key executives, and non-IT staff generally in many organizations are still finding it a challenge to embrace the rapid changes, fuelled by digitization, mobilization, augmentation, disintermediation, and automation.Digital Transformation and Innovation (DTI) program is henceforth developed by SIMTech with the intent of facilitating non-IT employees’ journey towards supporting their companies achieve digital transformation. DTI rides on the principles of “Learn-Practice-Implement” (LPI) to equip participants with the knowledge and skills to serve the industry needs. Broadly defined, “Learn” is about acquiring fundamental knowledge to understand key concepts related to digital transformation; ‘Practice” is about identifying pain points impeding the path of organizations towards digitalization; “Implement” is about generating initiatives, creating the roadmap, and clearing the way forward for organizations to achieve their business objectives. Applying the LPI principles specifically to DTI, firstly employees are put through the “LEARN” phase on how to build the business model canvas, appreciate challenges from external environment, map value stream flow, generate initiatives and develop a roadmap of actions. Secondly, employees go through the “PRACTICE-IMPLEMENT” phase to affirm initiatives, develop individual initiative system design, enhance vendor proposal, consolidate initiatives, validate Proofs Of Concept (POC), and establish an integrated project. This paper presents the training methodology, and a case study example on how companies digitally transform themselves using the DTI methodology.
An Investigation of Parameter Optimization in Fingerling Counting Problems

The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.
Hate Detection for Social Media Text with User Alert System

Social media is an important factor to build social networks and discover various issues going around the world. However, the increase in the use of social media has led people to engage in spreading and sharing hate speech as well. On social media, people may act more aggressively as they can be anonymous. Hate speech not only causes psychological harm to an individual but may also lead to any physical harm when it incites violence. With the help of advanced technology like Machine learning, many researchers are working on this factor specially to solve the emerging issue of hate speech being used on social media. Hate speech is considered offensive in many countries and there are times when people are arrested for the same. Many users may not be in the right state of mind while interacting on social media so they might make the wrong choice of words. A good way to stay out of such situations is to be cautious and review the content of the comment before posting. The main objective of our model is to build a system using a LTSM-based classification system which is a machine learning approach for identifying and classifying hateful comments and alerting the user from posting any abusive comments on social media. We are also using Bi-LSTM in our model to classify hate which gives about 0.9084 accuracy. Here the user can make his own choice of words depending upon the alert message he gets for his comment.
Endlich schrauben! Der mechanische Aufbau der Fräse

No AccessCNC-Fräsen für MakerApr 2022Endlich schrauben! Der mechanische Aufbau der FräseRalf SteckRalf SteckSearch for more papers by this authorhttps://doi.org/10.3139/9783446474079.005SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 93-126Print ISBN: 978-3-446-47160-3eISBN: 978-3-446-47407-9 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
The Three Tokenomics Problems and a Productivity-Linked Tokenomics Design

This paper defines the Three Tokenomics Problems: token allocation, emission, and value. Does the Productivity-Linked Tokenomics Design fix these?
Adaptive Partition Total Variation Algorithm for Medical Ultrasound Image Denoising

Medical ultrasound images are affected by multiplicative speckle noise in the process of imaging. It causes the quality and readability of the image to decrease. An adaptive partition total variation algorithm to denoise images is proposed by the noise characteristic of ultrasound images. Firstly, the image is divided into texture region and flat region by the Wa-Harris algorithm. Then the multiplicative noise is transformed into additive noise by logarithmic transformation. For the texture region, the anisotropic L1 norm total variation (AL1-TV) denoising model is used. For the flat region, the isotropic L2 norm total variation (IL2-TV) noise denoising model is used. Additionally, the split Bregman iterative algorithm is used to solve the total variation (TV) model quickly. Finally, the image is reconstructed by exponential transformation to achieve the ideal denoising effect. The experimental results show that it’s a significant model based on the Wa-Harris algorithm to remove the speckle noise of medical ultrasound images.
Project to Improve Offensive Phase Finalization of Futsal Teams by Using Linguistic Data Summarization Techniques

In this paper authors collect and analyze data from 2018 futsal season of the University of Informatics Sciences team. In order to obtain linguistic summaries, authors apply linguistic data summarization techniques. The linguistic summaries are storage in database and later use for coaches for decision-making on 2019 season. To validate the research, authors analyze the variables involved in the offensive phase finalization in the “Universidad de las Ciencias Informáticas (UCI)” futsal team with respect to the 2018/2019 seasons, from observation and SPSS statistical analysis, significant differences are evident in the variable goal and positive passes, managing to score 33 goals in 2019 and 13 goals in 2018; for this, 85 positive shots were produced averaging 2.57 shots to reach goal. In the plays of static positional strategies there are significant differences in the variables goal and positive shots. Ten more goals are scored in 2019 as every 3.69 positive shot opportunities the team scores a goal. While in 2018 three goals are scored and an average of 7.66 positive shots are used to score a goal. Positional transitions on the move improve in 2019 where eight goals are scored and on average 2.12 shots are produced to score a goal. Counterattacking transitions present a stable behavior; this variable depends on the way of defending to close and counterattack. In free-kicks two goals are scored in 2019, improving in this regard. The free-kick is improved in 2019 since no goals were scored in 2018 and two goals were scored this year.
Automated Code Review Comment Classification to Improve Modern Code Reviews

Modern Code Reviews (MCRs) are a widely-used quality assurance mechanism in continuous integration and deployment. Unfortunately, in medium and large projects, the number of changes that need to be integrated, and consequently the number of comments triggered during MCRs could be overwhelming. Therefore, there is a need for quickly recognizing which comments are concerning issues that need prompt attention to guide the focus of the code authors, reviewers, and quality managers. The goal of this study is to design a method for automated classification of review comments to identify the needed change faster and with higher accuracy. We conduct a Design Science Research study on three open-source systems. We designed a method (CommentBERT) for automated classification of the code-review comments based on the BERT (Bidirectional Encoder Representations from Transformers) language model and a new taxonomy of comments. When applied to 2,672 comments from Wireshark, The Mono Framework, and Open Network Automation Platform (ONAP) projects, the method achieved accuracy, measured using Matthews Correlation Coefficient, of 0.46–0.82 (Wireshark), 0.12–0.8 (ONAP), and 0.48–0.85 (Mono). Based on the results, we conclude that the proposed method seems promising and could be potentially used to build machine-learning-based tools to support MCRs as long as there is a sufficient number of historical code-review comments to train the model.
Problems of education digitalization when conversion to on-line technologies

The introduction of digital technologies in the system of higher professional education has both positive and negative aspects. The requirements for digital resources and their implementation technologies have been developed and discussed for more than 20 years. The impact of digital technologies on learning outcomes, attracted great attention during the almost mass transition to on-line training in Russia in Spring of 2020. The paper considers two aspect of this problem: the current structure and use of electronic resources and the organizational issues of the educational process.
Sentiment Analysis on the Performance of Engineering Students in Continuous Assessment Evaluation System: A Non-parametric Approach Using Kruskal-Wallis Method

Sentiment Analysis is implicitly related to Continuous Assessment system in educational evaluation. Continuous Assessment system is a systematic and objective process of determining the extent to which changes have taken place in the student’s performance in the various areas of educational objectives viz., Cognitive, Affective and Psychomotor. In this paper, an attempt is made to study in detail the different components of Continuous Assessment system in relation to the objectives noted above in an empirical manner. In the empirical analysis, the internal marks of a sample of students in a higher education institution is used. A nonparametric approach namely Kruskal–Wallis one way ANOVA is used for the analysis of the sampled data. The inferences drawn from the study clearly indicates that the planning mechanism adapted by educational authorities in evolving Continuous Assessment system for engineering education is fulfilled in a successful manner.
International journal of web information systems: An evaluation

This paper presents the findings of a bibliometric study of the International Journal of Web Information Systems from 2005 to 2018 covering a total of 289 articles published during the stated period. This study describes volume-wise distribution of articles, authorship pattern of articles in each volume, article distribution pattern of authors per issue, degree of collaboration, major contributors, pattern of article distribution, geographical distribution of contributors. It also examines the reference distribution pattern of the articles and keywords. The study reveals that the highest publications with a record contribution of 144 articles are contributed by Japan. Majority of articles are found to be three-authored papers (87 articles) followed by two-authored papers (82 articles) and four-authored articles. Hiroyuki Kitagawa and Toshiyuki Amagasa from Japan contributed the highest number of articles (8 articles each). The degree of collaboration was found to be 0.89.
Preface of “The Tenth Symposium on Advanced Computation and Information in Natural and Applied Sciences”

First Page