Reviewerly/exHarmony huggingface dataset:
Investigating post-editing effort

Abstract The working environment of translators has changed significantly in recent decades, with post-editing (PE) emerging as a new trend in the human translation workflow, particularly following the advent of neural machine translation (NMT) and the improvement of the quality of the machine translation (MT) raw output especially at the level of fluency. In addition, the directionality axiom is increasingly being questioned with translators working from and into their first language both in the context of translation ( Buchweitz and Alves 2006 ; Pavlović and Jensen 2009 ; Fonseca and Barbosa 2015 ; Hunziker Heeb 2015 ; Ferreira 2013 , 2014 ; Ferreira et al. 2016 ; Feng 2017 ) and in the context of PE ( Garcia 2011 ; Sánchez-Gijón and Torres-Hostench 2014 ; da Silva et al. 2017 ; Toledo Báez 2018 ). In this study we employ product- and process-oriented approaches to investigate directionality in PE in the English-Greek language pair. In particular, we compare the cognitive, temporal, and technical effort expended by translators for the full PE of NMT output in L1 (Greek) with the effort required for the full PE of NMT output in L2 (English), while we also analyze the quality of the final translation product. Our findings reveal that PE in L2, i.e., inverse PE, is less demanding than PE in L1, i.e., direct PE, in terms of the time and keystrokes required, and the cognitive load exerted on translators. Finally, our research shows that directionality does not imply differences in quality.
Deep forest regression prediction model for dioxin emission concentration by using new representation strategy

The dioxin (DXN) is a key environmental indicator for municipal solid waste incineration (MSWI) process. However, the concentration of DXN's emission prediction model always relies on long-term and expensive offline experimental analysis. To address this issue, some researchers employed deep neural networks (DNN) to construct a soft measurement model, but it has poor training efficiency, inflexible model size, and weak interpretability. Recently, the deep forest regression (DFR) algorithm has achieved success in the field of modeling domain. Therefore, it is used for DXN emission prediction in this paper. To encourage the diversity of features between layers, this paper endeavors to implement DFR for DXN emission concentration soft measurement by a new representation strategy. Firstly, the dioxin emissions and data characteristics of the MSWI process are described. Then, the structure of DFR is analyzed and the problems of representation strategy are stated briefly. Finally, we explore the representation learning inside the DFR structure based on stacked generalization. The effectiveness of the proposed method is verified by DXN data of the MSWI process.
Method with Data to Text Generation Based on Selecting Encoding and Fusing Semantic Loss

A method with data to text generation based on selecting encoding and fusing semantic loss is proposed in this paper. By highlighting key content and reducing the redundancy of text description information, the quality of the generated text is significantly improved. First, a new selection network is designed, which uses the amount of information related to data records as the coding basis for content importance, and multiple rounds of dynamic iterations of the results to achieve accurate and comprehensive selection of important information. Secondly, in the decoding process using Long Short-Term Memory (LSTM), a hierarchical attention mechanism is designed to assign dynamic selection weights to different entities and their attributes in the hidden layer output to obtain the best generated text Recall rate. Finally, a method of calculating the semantic similarity loss between the generated text and the reference text is introduced. By calculating the cosine distance of the semantic vectors of the two and iteratively feedback to the training process to obtain the optimization of key features, while reducing the redundancy of description information and improving the model BLEU performance. The experimental results show that the test Precision rate, Recall rate and BLEU is up to 94.58%, 53.72% and 17.24, which are better than existing models.
Acoustic Signal Target Recognition Using Improved Clustering Autoencoder

According to the small-sample data discretization problem, this paper proposes an acoustic signal target recognition model using improved clustering autoencoder(ICAE) to complete acoustic signal target recognition. Specifically, the clustering loss function of the proposed ICAE is developed to encode and cluster the identity authentication(I-vector), which can solve the large gap between a small amount of target-related data and the poor recognition effect. Moreover, the linear discrimination analysis(LDA) is adopted to project the dataset on the feature subspace that can differentiate the different targets with dimensionality reduction. The experimental results show that the recognition model using the proposed ICAE can achieve better recognition performance and strong adaptability. Compared with other methods, the proposed ICAE in this paper has an obvious clustering effect on a small amount of data.
Development and Application of English Translation Software Based on Computer Corpus

On the practical level, bilingual dictionary writing, translation teaching, interpreter training and translation assistance can be carried out through parallel corpora. In English teaching, we should make effective use of corpora, take students as the teaching subject, and teachers should be problem-oriented, and spread students' thinking, so that students can discuss corpora by observing corpora, forming a cooperative relationship with others, knowing the background of problems, and then solving corpus problems. With the continuous development of modern computer software technology, the development and use of English translation software has attracted people's attention. In order to effectively improve the accuracy of modern electronic dictionary translation, this paper analyzes the modern English teaching strategies and the design ideas of English translation software based on computer corpus.
An E-health System for Encrypting Biosignals Using Triple-DES and Hash Function

This Electronic Health (E-Health) is a broad expression that enables the communication between healthcare professionals in handling patient information through the cloud. Exchanging medical data over the public cloud requires securing transferring for the data that&#x0027;s direct many researchers in proposing different secure schemes to enable users to handle data safely without hacking or alternating. In this study, one of the most common encryption algorithms called Triple Data Encryption Standard (Triple-DES) has been implemented with the aid of the hash function and DNA cryptography base to encrypt different biosignals into DNA format to add a security layer for the proposed system.
Efficient Decomposition Selection for Multi-class Classification

Choosing a decomposition method for multi-class classification is an important trade-off between efficiency and predictive accuracy. Trying all the decomposition methods to find the best one is too time-consuming for many applications, while choosing the wrong one may result in large loss on predictive accuracy. In this paper, we propose an automatic decomposition method selection approach called “D-Chooser”, which is lightweight and can choose the best decomposition method accurately. D-Chooser is equipped with our proposed difficulty index which consists of sub-metrics including distribution divergence, overlapping regions, unevenness degree and relative size of the solution space. The difficulty index has two intriguing properties: 1) fast to compute and 2) measuring multi-class problems comprehensively. Extensive experiments on real-world multi-class problems show that D-Chooser achieves an accuracy of 80.56% in choosing the best decomposition method. It can choose the best method in just a few seconds, while existing approaches verify the effectiveness of a decomposition method often takes a few hours. We also provide case studies on Kaggle competitions and the results confirm that D-Chooser is able to choose a better decomposition method than the winning solutions.
An Ensemble Learning Method with Feature Fusion for Industrial Control System Anomaly Detection

With the deepening of the integration of industrialization and informatization, information technology brings great risks to the safe operation of industrial control system. How to effectively detect the network anomaly behavior in the industrial control system is the key problem of industrial control security research. This paper proposes an ensemble learning method with feature fusion to solve the problem of anomaly classification of network data. Computational results illustrate that the method proposed in this paper has excellent detection effect on different kinds of network attacks in the experiment and improves the detection accuracy.
Speckle-based Telecommunication and Computing in Multimode Fibers

We demonstrate the exploitation of dynamically changing speckle patterns for the implementation of a space division multiplexing scheme and for performing photonic computation within the framework of reservoir computing.
Image Feature Eradication Using Hadoop With Gpu Accelerators

In this paper, analysis of distributed image processing framework is presented, so that it becomes very useful to beginners and to those who are new to distributed image processing field. The outcome of the study of distributed image processing with emphasis on benefits and application domains is described. Various research issues are examined and some future research directions are identified for distributed image processing. This study provides concise understanding of the distributed image processingarea.
Experience Space Design Strategy and Case Realization Based on VR Technology

research-article Share on Experience Space Design Strategy and Case Realization Based on VR Technology Author: Bo Zhang Chongqing Technology and Business University, China Chongqing Technology and Business University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2237–2242https://doi.org/10.1145/3482632.3484136Online:22 November 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks3 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
TSD: A new dataset for shadow detection of transparent objects

In recent years, with the rapid development of deep learning and its wide application in the field of computer vision, a series of shadow detection algorithms based on deep learning have been proposed on public datasets such as SBU and ISTD. These shadow detection algorithms verify better performance than traditional shadow detection algorithm based on the physical model. However, the existing shadow detection dataset only performs image acquisition for non-transparent objects, and ignores the requirement for shadow removal of transparent objects in practical applications. Therefore, in this article, we will focus on the production of the transparent object shadow detection dataset, and propose a method of making synthetic dataset pictures through Blender software. We propose a new dataset for shadow detection of transparent objects and the new dataset contains 800 images including 100 images with complex backgrounds, 233 images with bright backgrounds and 467 images without backgrounds. We also verify it on the existing shadow detection algorithm. The experimental results show that the datasets we made has good characteristics and can be better used for the training of shadow detection networks and the detection of shadow positions of transparent objects.
Decoding Topological Subsystem Color Codes Over the Erasure Channel Using Gauge Fixing

Topological subsystem color codes (TSCCs) are an important class of topological subsystem codes that allow for syndrome measurement with only 2-body measurements. It is expected that such low complexity measurements can help in fault tolerance. While TSCCs have been studied over depolarizing noise model, their performance over the erasure channel has not been studied as much. Recently, we proposed erasure decoders for TSCCs and reported a threshold of 9.7%. In this paper, we continue our study of TSCCS over the erasure channel. We propose two erasure decoders for topological subsystem color codes. These decoders employ a mapping of the TSCCs to topological color codes (TCCs). In addition, these decoders use the technique of gauge fixing, where some of the gauge operators of the subsystem code are promoted to stabilizers. We perform gauge fixing using 4-body and 8-body gauge operators. With partial gauge fixing, we obtained a threshold of 17.7% on a TSCC derived from the square octagon lattice. Using an order maximal gauge fixing decoder we were able to improve the threshold to 44%. The performance of the order maximal gauge fixing decoder can be further improved to close to 50% in conjunction with an optimal erasure decoder for topolological color codes. We also study the correctability of erasures on the subsystem codes.
Recognition of High Difference Features in Urban Planning Images Based on Morphological Filtering

As an effective information carrier, image is the main source for human beings to obtain and exchange information. Therefore, the application field of image processing involves all aspects of human life and work. Image enhancement is an important part of image processing and plays an important role in the whole process of image processing. This paper mainly studies the image enhancement method based on partial differential equation. By analysing the combination of partial differential equation theory and enhancement, aiming at the shortcomings of low recognition accuracy, high error rate, and long time consuming in the current method of urban planning image feature recognition, a feature enhancement and simulation of urban planning image based on partial differential equation method is proposed; the preprocessing of urban planning image is realized by collecting the urban planning image. On the basis of preprocessing the urban planning image, the urban planning image is divided into several equal area subareas; the pixel gray value of each subarea and the average value of pixel distribution density of node landscape image are calculated; and whether the pixel points are at the edge of urban planning image is judged by setting the comprehensive mean threshold. According to the judgment results, the high difference features of urban planning images are intelligently recognized. Simulation results show that the proposed method can realize efficient and accurate recognition of high difference features in urban planning images.
Maximum likelihood speed estimation of moving objects in video signals

Video processing solutions for motion analysis are key tasks in many computer vision applications, ranging from human activity recognition to object detection. In particular, speed estimation algorithms may be relevant in contexts such as street monitoring and environment surveillance. In most realistic scenarios, the projection of a framed object of interest onto the image plane is likely to be affected by dynamic changes mainly related to perspectival transformations or periodic behaviours. Therefore, advanced speed estimation techniques need to rely on robust algorithms for object detection that are able to deal with potential geometrical modifications. The proposed method is composed of a sequence of pre-processing operations, that aim to reduce or neglect perspetival effects affecting the objects of interest, followed by the estimation phase based on the Maximum Likelihood (ML) principle, where the speed of the foreground objects is estimated. The ML estimation method represents, indeed, a consolidated statistical tool that may be exploited to obtain reliable results. The performance of the proposed algorithm is evaluated on a set of real video recordings and compared with a block-matching motion estimation algorithm. The obtained results indicate that the proposed method shows good and robust performance.
Reset- and Reconnection-based Covert Channels in CoAP

The Internet of Things (IoT) and the Industrial Internet of Things (IIoT) are fast growing areas. Therefore, several protocols are specifically designed for these domains. CoAP (Constrained Application Protocol) is one of the more common ones. This lightweight protocol typically communicates over UDP, but can also use TCP and other carrier protocols, and is particularly suitable for embedded systems. In this work, we investigate CoAP with respect to covert communication and explore a new network hiding technique that is based on Artificial Resets. In addition, we investigate another covert channel by using the pattern PT15 Artificial Reconnections. We also propose and discuss detection methods for both covert channels.
A new fast atmospheric correction method for Landsat 8 images

Atmospheric correction is the basis for quantitative analysis of satellite remote sensing images, such as monitoring land surface changes. However, precise atmospheric correction is still challenging. Landsat 8 is a satellite used for surface monitoring launched by the United States National Aeronautics and Space Administration (NASA) in 2013, with good spatial resolution. Rich spectral information. In this paper, an improved dense dark vegetation(DDV)aerosol retrieval algorithm is developed, and the retrieved AOD map will be used to process the aerosol impact factor of remote sensing images. Atmospheric correction is performed based on a lookup table generated by the 6SV model. Validation with the Land Surface Reflectance Code (LaSRC) algorithm produced atmospheric correction images, correction images of the paper algorithm showed a good agreement with high correlation(Correlation R exceeds 95%). Meanwhile, a reliable software prototype system for processing atmospheric correction on Landsat 8 OLI images was developed. This system is based on C++ language and can perform atmospheric correction automatically, low-latency, and accuracy. The data products corrected by the software prototype are helpful for the widespread application of remote sensing data in emergency response, environmental monitoring, and national defense.
A Design of Haptic Hand Exoskeleton for Virtual Reality Applications

The paper presents a mechanical design for a haptic hand exoskeleton that can be employed in delivering sensation to a fingertip of an operator while he interacts with virtual objects in the computer simulation. The linkage structure of the device is designed according to the skeletal characteristics of a human finger, which allows it to create the normal force on the fingertip while natural movements of fingers are maintained. Moreover, motion range of the designed hand exoskeleton is carefully calculated to guarantee full capacity of human fingers. Evaluation of the design, where its kinematic structure and force transmission were analysed, shows effectiveness of the proposed device.
Design and Implementation of Campus Online Music Platform Based on Music Feature Matching Algorithm

research-article Design and Implementation of Campus Online Music Platform Based on Music Feature Matching Algorithm Share on Author: Lina Zhang Pingdingshan Vocational and Technical College, China Pingdingshan Vocational and Technical College, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2752–2756https://doi.org/10.1145/3482632.3487508Online:22 November 2021Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks0 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Development of Compound Non-Standard Word Dataset Using Crowdsourcing Method

During the COVID-19 Pandemic, many activities, such as communication and socialization are forced to be carried out through digital devices. Many communication applications have been developed to improve the users’ communication experience, namely chatbots, auto-translation, sentiment analysis and many other applications. However, most Indonesian use non-standard words when communicating with each other. These non-standard words used caused the text processing software work to be nonoptimal. This research aims to create a corpus of standard and non-standard words. This corpus can then be used to normalize the non-standard word to the standard version. The crowdsourcing method was chosen to create the dataset. This research has successfully collected 371 records as final corpus data. The most common problem is the difference in respondents’ perceptions in determining single or compound non-standard words. There were 5 problems found in the forming of corpus data, namely character repetition in non-standard words, various forms of loan words, typing errors, differences between respondent’s answers, and expression word without standard.
Algoritmos y problemas de aprendizaje en la materia de cálculo, un enfoque sociológico de su aplicación a la educación superior

This essay aims to provide a sociological approach to the application of artificial intelligence algorithms as an educational intervention that accurately identifies the learning problems of engineering students in the subject of calculus. The first section, by way of introduction, makes a general conceptual approach to evidence the general objective of the proposal. The second part, through a brief description and analysis of the scope of the CEIBAL Plan, builds a bridge of analysis between the potential of this proposal and sociology in order to show its implications in the algorithmic conception and its application as an intervention. The third and last part draws attention to ethical and sociological concerns regarding algorithms and their application in university classrooms, with special emphasis on the teacher's attitude in the midst of this paradigmatic change both in the definition of didactic sequences and in the teacher's role in defining actions in the face of learning problems and strategies to identify the needs of his or her students.