Reviewerly/exHarmony huggingface dataset:
The Effect of L2 Proficiency on Post-editing Machine Translated Texts

The present study examines how proficiency in the second language (L2) affects language learners' ability to post-edit texts that are machine-translated from the learners' native language (L1) to L2.More specifically, the study investigates how L2 proficiency level affects the degree to which Korean learners of English can discern the accuracy of L1-to-L2 machine-translated (MT) text and whether it affects the level of errors (word, phrase, clause, sentence) that are corrected in the post-editing process.Fifty-nine Korean university students' proficiency was measured using a cloze test and a writing test.They were then given a source text (Korean) and a machine-translated text (English) and were asked to detect and correct as many errors as they can.The overall results of the study showed that L2 proficiency does have a significant effect on how language learners post-edit machine-translated output.With increasing proficiency, the number of corrections increased especially above the wordlevel, and significant group differences could be found in post-editing patterns of the MT text.The findings have important pedagogical implications for integrating MT activities in L2 classrooms with learners of different proficiency levels.
Bitcoin and Blockchain Technology

A ledgerLedger can be defined as a “book of permanent record.” With modern information technology, data have become economic resources if they are associated with exclusive owners and put into a ledgerLedger. It is shown in Chaps. 3 and 4 that IoTInternet of Things (IoT) data can be transformed into productive resources while Chaps. 5 and 6 show that transaction data can be turned into money-likeMoney bank deposit currenciesDeposit currency.
Shape-based outlier detection in multivariate functional data

Multivariate functional data refer to a population of multivariate functions generated by a system involving dynamic parameters depending on continuous variables (e.g., multivariate time series). Outlier detection in such a context is a challenging problem because both the individual behavior of the parameters and the dynamic correlation between them are important. To address this problem, recent work has focused on multivariate functional depth to identify the outliers in a given dataset. However, most previous approaches fail when the outlyingness manifests itself in curve shape rather than curve magnitude. In this paper, we propose identifying outliers in multivariate functional data by a method whereby different outlying features are captured based on mapping functions from differential geometry. In this regard, we extract shape features reflecting the outlyingness of a curve with a high degree of interpretability. We conduct an experimental study on real and synthetic datasets and compare the proposed method with functional-depth-based methods. The results demonstrate that the proposed method, combined with state-of-the-art outlier detection algorithms, can outperform the functional-depth-based methods. Moreover, in contrast with the baseline methods, it is efficient regardless of the proportion of outliers.
Implementation of E-Module Stoiciometry Based on Kvisoft Flipbook Maker for Increasing Understanding Study Learning Concepts of Class X Senior High School

Referring to the current era of globalization, which refers the era of revolution 4.0, the use of today's technology in the learning process can be utilized. One of them is in applying ordinary modules into e-modules. The purpose of this study is the use of e-module stoichiometry based on Kvisoft flipbook maker to see the magnitude of the increase in students' understanding of the concept of stoichiometry. The research design used was a pre-experiment with a pretest-posttest design using one class as an experimental class. The sample in this study were students of the Madrasah Aliyah Negeri (MAN) 2 Pekanbaru class X KI 2. The results obtained were an increase in students' understanding of the concept from before using the e-module based on the koftoft flipbook maker with an average value of 40.2. After using Kvisoft flipbook maker based e-module the average value increased to 92.48. Large increase obtained by 0.83 with the category of moderate increase.
Research progress of large-scale knowledge graph completion technology

With the continued growth of various knowledge graphs, such as Google Knowledge Map, DBpedia, Microsoft Concept Graph, and YAGO, the knowledge representation system, constructed based on RDF, has become more well-known. The RDF triple format has become the basic description of knowledge in the real world. Due to its simple structure and clear logic, it is easy to understand and implement. Nevertheless, when faced with extremely complicated knowledge and common sense, complete knowledge can become difficult to describe. The construction process of knowledge graphs is bound to lead to incomplete knowledge contained in the graphs. At this point, the knowledge-based completion technology is particularly important for managing such situations. Any existing knowledge graph must be improved continuously through completion technology and newly inferred knowledge. Beginning with the construction of a knowledge graph, this paper divides the problem of knowledge graph completion into two levels: concept completion and instance completion. (1) The concept completion level primarily focuses on the completion of entity types. It is described in terms of three development stages: a logical reasoning mechanism, based on description logic, a type inference mechanism, based on traditional machine learning, and a type inference mechanism, based on representation learning. (2) The instance completion level can be further divided into an RDF triple completion and new instance discovery. This paper focuses on RDF triples completion learning, which includes entity completion or relationship completion and is described in three development stages, such as statistical relational learning, probability learning based on random walks, and knowledge representation learning. Through the review and discussion of the research process, the development status, and the latest progress in the above-mentioned large-scale knowledge graph completion, we present the challenges that the technology will face and the development prospects of future work.
Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human-robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system.
From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

Are nearby places (e.g., cities) described by related words? In this article, we transfer this research question in the field of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. This is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar, regardless of their distances and the underlying communities of authors. To investigate this, we introduce Multiplex Topic Network s (MTN), which we automatically derive from Linguistic Multilayer Network s (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zipfian organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this finding in the context of cognitive maps , a notion which we extend by so-called thematic maps . According to our interpretation of this finding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way, we come to the conclusion that geographical places, whether close to each other or not, are located in neighboring semantic places that span similar subnetworks in the topic universe.
A Generalized Formal Semantic Framework for Smart Contracts

Smart contracts can be regarded as one of the most popular blockchain-based applications. The decentralized nature of the blockchain introduces vulnerabilities absent in other programs. Furthermore, it is very difficult, if not impossible, to patch a smart contract after it has been deployed. Therefore, smart contracts must be formally verified before they are deployed on the blockchain to avoid attacks exploiting these vulnerabilities. There is a recent surge of interest in analyzing and verifying smart contracts. While most of the existing works either focus on EVM bytecode or translate Solidity contracts into programs in intermediate languages for analysis and verification, we believe that a direct executable formal semantics of the high-level programming language of smart contracts is necessary to guarantee the validity of the verification. In this work, we propose a generalized formal semantic framework based on a general semantic model of smart contracts. Furthermore, this framework can directly handle smart contracts written in different high-level programming languages through semantic extensions and facilitates the formal verification of security properties with the generated semantics.
Logistic Model Tree and Decision Tree J48 Algorithms for Predicting the Length of Study Period

One point to be assessed in the accreditation process in an institution is the length of the student's study period. The Informatics department in XYZ college has been accredited by the national accreditation bureau for higher education (BAN-PT), but the accreditation has the potential to be improved. One thing that affects the accreditation value is many students did not graduate on time. Therefore, the current study used available student data, both academic and non-academic, using data mining. Two model classifications were used, i.e. Logistic Model Tree (LMT) and Decision Tree J48. The study was aimed to compare LMT and Decision Tree J48 algorithm in predicting the length of student’s study and to find out the influence factors. The data were Informatics Engineering students who have graduated in February 2018 to February 2019 (135 records). Results showed that the LMT algorithm produced an accuracy rate of 71% better than Decision Tree J48 (62.8% accuracy) in predicting the length of the student’s study. The factors influencing the length of study of students are temporary grade point average (GPA) of the first semester, temporary GPA of the second semester, organizational status, and employment status.
Multi-Scale Global Contrast CNN for Salient Object Detection

Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that visual contrast is the most important factor in bottom-up visual attention process. Many of the proposed models predict saliency maps based on the computation of visual contrast between salient regions and backgrounds. In this paper, we design an end-to-end multi-scale global contrast convolutional neural network (CNN) that explicitly learns hierarchical contrast information among global and local features of an image to infer its salient object regions. In contrast to many previous CNN based saliency methods that apply super-pixel segmentation to obtain homogeneous regions and then extract their CNN features before producing saliency maps region-wise, our network is pre-processing free without any additional stages, yet it predicts accurate pixel-wise saliency maps. Extensive experiments demonstrate that the proposed network generates high quality saliency maps that are comparable or even superior to those of state-of-the-art salient object detection architectures.
Notice of Violation of IEEE Publication Principles: Study on Sports Volleyball Tracking Technology Based on Image Processing and 3D Space Matching

Notice of Violation of IEEE Publication Principles<br><br> “Study on Sports Volleyball Tracking Technology Based on Image Processing and 3D Space Matching”<br> by Dianchen He, Li Li, Lina An<br> in IEEE Access, April 2020 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br> This paper is duplicated content from the paper cited below. The original content copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br> “Ball Feature Based Likelihood Estimation in Particle Filter Algorithm with Automatic Recovery for 3D ball Tracking in Volleyball”<br> by Xina Cheng<br> in the Master’s Thesis, Waseda University, Japan, 2015<br><br> <br/> The tracking technology of the 3D space position of the target is of great use to the analysis and understanding of professional volleyball match, but there are many factors that affect the volleyball tracking rate in the competition. In this paper, the image processing technology is combined with 3D spatial matching technology, and a sports volleyball tracking system framework is established with particle filter, which improves the accuracy of volleyball tracking more effectively. This article begins with a 3D tracking volleyball workflow diagram and selects using particle filters to improve tracking success rates. Secondly, the paper puts forward the similarity estimation method according to the characteristics of volleyball itself, mainly the similarity estimation of the base and HSV color space, to solve the problem of the super-computing of real-time tracking. Finally, the results of the simulation experiment can be seen that the method can greatly improve the tracking success rate, high efficiency and high accuracy of the characteristics of the dynamic video sports volleyball tracking, has an unpredictable application development value.
Network Embedding Using Deep Robust Nonnegative Matrix Factorization

As an effective technique to learn low-dimensional node features in complicated network environment, network embedding has become a promising research direction in the field of network analysis.Due to the virtues of better interpretability and flexibility, matrix factorization based methods for network embedding have received increasing attentions.However, most of them are inadequate to learn more complicated hierarchical features hidden in complex networks because of their mechanisms of single-layer factorization structure.Besides, their original feature matrices used for factorization and their robustness against noises also need to be further improved.To solve these problems, we propose a novel network embedding method named DRNMF (deep robust nonnegative matrix factorization), which is formed by multi-layer NMF learning structure.Meanwhile, DRNMF employs the combination of high-order proximity matrices of the network as the original feature matrix for the factorization.To improve the robustness against noises, we use 2,1 norm to devise the objective function for the DRNMF network embedding model.Effective iterative update rules are derived to resolve the model, and the convergence of these rules are strictly proved.Moreover, we introduce a pre-training strategy to improve the efficiency of convergence.Extensive experiments on several benchmarks of complex networks demonstrate that our proposed method DRNMF is effective and has better performance than the state-of-the-art matrix factorization based methods for network embedding.
Single-shot structured-light-field three-dimensional imaging

This Letter reports an approach to single-shot three-dimensional (3D) imaging that is combining structured illumination and light-field imaging. The sinusoidal distribution of the radiance in the structured-light field can be processed and transformed to compute the angular variance of the local radiance difference. The angular variance across the depth range exhibits a single-peak distribution trend that can be used to obtain the unambiguous depth. The phase computation that generally requires the acquisition of multi-frame phase-shifting images is no longer mandatory, thus enabling single-shot structured-light-field 3D imaging. The proposed approach was experimentally demonstrated through a dynamic scene.
The Implementation Knowledge Graph of Air Crash Data based on Neo4j

The characteristics of neo4j graph database has been introduced, several methods of constructing graph database based on neo4j importing data have been compared and analyzed, and the “neo4j-admin” method has been finally selected to import the air crash data, to realize the visual display and query. Specific implementation steps as follows:(1) Data sets of air crash worldwide since 1908 [1] have been cleaned and preprocessed to remove incomplete lines and unimportant fields;(2) Crashed aircraft information table, accident details table and the above two table association relationship table have been designed and stored in the form of CSV files;(3)The CSV files have been imported into neo4j using the tool “neo4j-admin”, air crash knowledge graph has been generated; (4) This knowledge graph can be used to query the number of accidents since 1908, the place where the accidents occurred, the number of casualties and the time when the accidents occurred.
Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.
Kahoot in Maritime English Teaching: Its Impact on Nautical Science Cadet’s Oral Reproduction and Vocabulary

This type of research was Participatory Action Research using the Denzin and Lincoln (2005) model which consists of the Kahoot pre-action cycle and post-action cycle. This study aimed to examine the use of Kahoot Platform in Maritime English learning. It improved the speaking skill of nautical science cadets by the increasing number of maritime vocabularies as an indication. The results of this study described that the use of Kahoot quiz made learning experienced in the classroom very interactive, interesting, and effective, because of the two cycles carried out increased from the beginning of the cycle to the next cycle marked by the increasing number of correct answers done by the cadets. Thus, their vocabulary mastery increased as well. Kahoot not only increased vocabularies that supported cadet’s speaking skill initiated by the correct oral reproduction, but also increased cadet’s enthusiasm in learning Maritime English. In conclusion, the use of online gamification-based Kahoot quiz toward nautical science cadets at Sekolah Tinggi Maritim Yogyakarta had a significant impact on vocabulary mastery for effective communication skills.
Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.
Physicians’ Perceptions of the Use of a Chatbot for Information Seeking: Qualitative Study

Background Seeking medical information can be an issue for physicians. In the specific context of medical practice, chatbots are hypothesized to present additional value for providing information quickly, particularly as far as drug risk minimization measures are concerned. Objective This qualitative study aimed to elicit physicians’ perceptions of a pilot version of a chatbot used in the context of drug information and risk minimization measures. Methods General practitioners and specialists were recruited across France to participate in individual semistructured interviews. Interviews were recorded, transcribed, and analyzed using a horizontal thematic analysis approach. Results Eight general practitioners and 2 specialists participated. The tone and ergonomics of the pilot version were appreciated by physicians. However, all participants emphasized the importance of getting exhaustive, trustworthy answers when interacting with a chatbot. Conclusions The chatbot was perceived as a useful and innovative tool that could easily be integrated into routine medical practice and could help health professionals when seeking information on drug and risk minimization measures.