Reviewerly/exHarmony huggingface dataset:
A Bibliometric Analysis of Machine Translation Post-editing from 2012 to 2021

Machine translation post-editing (MTPE) has gained a lot of attention lately. This paper conducted a bibliometric analysis of 270 publications on MTPE retrieved from the core database of Web of Science in recent decade from 2012 to 2021 with the aid of literature analysis software VOSviewer. By means of keyword co-occurrence and clustering of literature co-citation, this paper reviews the distribution of annual publications, disciplines, institutions and authors, citation structure of MTPE, etc. Meanwhile, the future trends related to the study of MTPE are predicted so as to provide reference and inspiration for further research. The research findings are as follows: the number of MTPE publications has fluctuated in the past decade, but the overall trend is increasing; MTPE studies are mainly distributed in linguistics and computer science; Dublin City University in Ireland is currently the most productive institution in MTPE research, and Joss Moorkens from the university plays a critical role in promoting the cooperation between scholars in this field; Spain is currently the most productive country in MTPE, but Ireland is the most cited country in the literature. From the analysis of keywords, the paper concludes that most of the current MTPE articles focus on the key concepts of “quality” and “effort while in the future, “NMT”, “translation quality assessment” and “MTPE skills and training” will be the research trends.
Interpretable sentiment analysis based on sentiment words’ syntax information

In recent years, with the vigorous development of deep learning, the pre-trained models such as Bert and GPT have been brilliant, and the sentiment analysis task has made increasingly outstanding achievements. The sentimental accuracy of model recognition is getting higher and higher, and the related application fields are also getting wider and wider. However, because deep learning is a black box model, its internal decision-making mechanism is not transparent to users, and it can’t reasonably explain the output of the model, which brings great limitations to the application of sentiment analysis. In this paper, we integrate the syntax tree based on sentiment words into the embedding module and the attention module of the interpretable sentiment model, and filter the evidence tokens output by the model to achieve the interpretability of sentiment analysis. The model is validated on the DuTrust dataset, and the experiment proves the validity of sentiment words' syntax in interpretable sentiment analysis.
A High-accuracy Camera Calibration Method Based on Special Circular Target

Targets with circular markers are commonly used in camera calibration and vision measurement. This paper proposes a special circular target with complete and incomplete concentric circles and accordingly introduces a high-accuracy camera calibration method. This method first calculates the initial camera parameters by utilizing the improved ellipse fitting method to determine the centers of the complete concentric circles. But the fitted centers do not coincide with the real projected centers due to the perspective transformation and lens distortion. The coordinates of the real projected centers can be obtained by detecting the corners of the incomplete concentric circles and using the properties of projective geometry. Then, based on the determination of the real projected centers, the iterative compensation algorithm is proposed to eliminate the influence of the eccentricity errors and refine the camera parameters. Corresponding experiments were conducted to show the high accuracy and robustness of the proposed camera calibration method.
A Hybrid scheme for Parsing Cantonese Text Based on PyCantonese Plus and PyLTP

A novel scheme for the Cantonese text parsing based on the method of dynamically expanding corpus is presented. Scheme need prepare an additional Chat format file, i.e., export a Cantonese phase file from Cantonese Dictionary firstly, which is widely used in Chinese Mainland, then annotate Jyutping romanization online and annotate them with part-of-speech (commonly known as POS) tags, finally generate the additional Chat format file based on CHILDES database and the above-mentioned Cantonese phase file Jyutping file, POS file, etc. When parsing Cantonese text, we execute iterative operations such as word segmentation with PyCantonese, expanding corpus until the segment result is ideal. Then we do other tasks including POS tagging with our POS dictionary and PyCantonese‘s pos-tagging module, dependency parsing with PyLTP, and visual display all the above parsing results, etc. Test results show the superior performance of the scheme and potential for the parsing Cantonese text.
Similarity Measures Based on Multi-knowledge Integration

Similarity calculation is an important part of natural language processing. The higher the accuracy of the similarity, the better the results for downstream tasks. In previous studies, similarity was calculated using a single knowledge source and the similarity calculation was not satisfactory. Later, computing similarity combining multiple knowledge sources can lead to better similarity computation results. In this paper, we calculate similarity based on multiple knowledge sources and use the simulated annealing algorithm to calculate the damping factor between different knowledge sources to obtain the composite similarity. The error between the composite similarity values of the BioBERT model and the Word2Vec model and the standard similarity values is only 0.03. In the data of the official similarity calculation, the composite similarity can improve the accuracy of the similarity very well.
Research on target tracking algorithm based on multi-layer convolution feature fusion

Due to the poor tracking performance brought on by external factors like the target objects moving quickly, changing light, and scale fluctuation, the Convolutional Neural Network (CNN) trained on the target identification dataset was utilized for feature extraction. On the foundation of this, a multi-layer convolution feature fusion-based target tracking method was developed. First, the VGG19 was used to execute the multi-layer convolutional feature extraction. Second, to finish the target position estimate based on the multilayer convolutional features, the correlation filter tracker was built individually for each layer of convolutional features, and the features were fused by assigning weights according to the feature responses to complete the target position estimation based on the multilayer convolutional features. Once multi-scale sampling had been performed in the target center region in accordance with the correlation filtering model, the scale variation of adjacent frames was ultimately employed to complete the scale prediction of the target. The tracking performance was empirically examined in the dataset OTB50 and contrasted in numerous ways using various tracking methods. The approach presented in this work significantly improved tracking rate and accuracy while exhibited good robustness to scale variation.
Angle and Heuristic Guided Probabilistic Foam for Local Path Replanning

Online planning and replanning methods are commonly used in unknown or dynamic environments to ensure path safety. In this paper, we proposed a novel local path replanning algorithm. We adapted Probabilistic Foam (PF) idea for reactive planning tasks. Probabilistic Foam infrastructure is selected for several reasons. Firstly, these types of planners are lightweight, and secondly, they ensure collision-free path generation. But they don’t guarantee path optimality. In this work, the latest achievement on the PF idea was further improved and adapted to local planning tasks. We proved that our algorithm produces shorter paths and more lightweight than alternatives by conducting qualitative and quantitative tests. We also test our algorithm as a local planner on coverage path tracking and 2d obstacle avoidance tasks designed for a quadrotor in a realistic simulation environment.
A comparative study of Filipino and Chinese prosodies

In second language acquisition, prosody is an important factor that affects learners‘ language intelligibility, and research results on the comparison of prosody between native and target languages can contribute to the effectiveness of second language prosody teaching and acquisition. Few studies have compared Filipino and Chinese ontologies in the field of teaching Chinese to Filipinos, and the similarities and differences between Filipino and Chinese prosodic patterns remain unclear. This paper compares Filipino-Chinese prosodies in terms of type, hierarchical structure, and stress accentuation using read-aloud discourse materials and finds that (1) Filipino syllabic prosodic language is less typical than Chinese; (2) except for intonation phrases, the differences in the size of Filipino-Chinese prosodic units at all levels are not obvious; (3) the duration of silent pauses at prosodic boundaries is shorter in Filipino than in Chinese, and the lengthening effect of syllables before the boundaries is greater than that of Chinese; (4) the duration of prosodic stress at all levels is more prominent in Filipino, and the means of highlighting pitch are more flexible. CCS Concepts: •Mathematics of computing~Mathematical analysis~Numerical analysis •Applied computing~Arts and humanities~Language translation.
Multilingual Document Concept Topic Modeling

Probabilistic topic models have demonstrated a high level of success in mining and analyzing topics from data generated. According to the previous study every topic is denoted as a probability distribution over words. This theory remains unsatisfactory; existing topic models do not implement well when it comes to extracting cross-lingual topics from simple words in different languages. Here, we present a novel framework for extracting topics from document collections. Knowledge is transferred to a cross-lingual model by incorporating a concept layer between the topic and word layers, with the ultimate goal of simplifying the process of extracting shared topics in text data across languages. In particular, we propose a novel Multilingual Document Concept Topic Modeling Method (MDCTM). Training algorithm models based on Gibbs sampling was used to develop MDCTM. Two datasets were evaluated. Using jieba for word segmentation, we demonstrate that the MDCTM model can effectively extract concept topic models from multilingual text data. Furthermore, our proposed model is presented to achieve state-of-the-art performance.
Explainability of Image Classifiers for Targeted Adversarial Attack

Deep neural networks outperform their contemporary peer models for computer vision tasks. These models are complex and challenging to interpret for practitioners. As more and more learning algorithms are deployed in real-world applications, model interpretability has become quite essential. Incidentally, Model interpretability is quite relevant to the case of deep neural networks as they can fall prey to adversarial attacks crafted by adversaries. In this paper, we launch an iterative targeted attack using a set of image classes on base architectures and interpret the results by applying an explanation algorithm before and after the attack. This process leads us to some valuable conclusions regarding the effects of the attack on the explanation methods and how explanation methods can be made to have more adversarial robustness.
Comparison of Different Keyphrase Extraction Algorithms for Supporting Problem Selection in Online Judge System

Online Judge System support the self-study of programming learners. However, it is difficult for learners to select appropriate problems owing to the number of problems posted. Therefore, we support problem selection by automatically extracting keyphrases from the problems using keyphrase extraction. Using keyphrase, we can summarize and adequately tag the problems. This paper compares unsupervised keyphrase extraction algorithms that do not use a corpus to determine the best algorithm for Online Judge system: TextRank, PositionRank, TopicRank, MultipartieRank, and YAKE. We also discuss features for keyphrase extraction.
PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification

Multi-label classification has recently attracted research interest as a data mining task. Many current applications in data mining address problems that have instances belonging to more than one class. This requires the development of new efficient methods. Instance selection has been used in multi-label learning to improve the execution time and classification performance of many learning methods. Following the single-label approach, instance selection has been applied by selecting or unselecting the same instances for all labels. In this paper, we present a different and novel approach. An instance might be useful for some labels and harmful for others; therefore, our algorithm allows each instance to be discarded, selected, or only partially selected for use in the classification of certain labels. An extensive comparison using 45 datasets shows the usefulness of our approach in improving the current instance selection methods for multi-label problems, as well as the ability of our algorithm to compete with other more complex multi-label classification methods.
On diamond products ensuring irreducibility of the associated composed product

Let f and g be two irreducible polynomials of coprime degrees m and n whose zeroes lie in a set G⊆F¯q. Let ⋄ be a diamond product on G. We define the weaker cancelation property of ⋄ and show that it is sufficient to conclude that the composed product of f and g derived from ⋄ is an irreducible polynomial of degree mn. We also prove that a wide class of diamond products on finite fields satisfy the weaker cancelation property. These results extend the corresponding results of Brawley and Carlitz (1987), and Munemasa and Nakamura (2016).
Adopting Continuous Integration Practices to Achieve Quality in DevOps

DevOps phenomenon is gaining popularity through its ability to support continuous value delivery. Every software process transition, technical practices has its own challenges so as DevOps. The aim of this study is to systematically review and analyse challenges confronted and practices adopted in continuous integration practice of software development to improve quality of software in DevOps. We have done systematic literature review of 44 papers. We conclude 26 challenges and 28 practices which are majorly in the continuous practices, automation, tools, monitoring and pipeline. These practices are having evidences of improving quality through faster release, monitoring performance, reduced risk, reduced testing time and efforts, improved security, fast feedback loop. In further research we have to measure the quality factors by using case study methods on selected software applications to quantify impact of presented practices.
Automated Selection of Web Form Text Field Values Based on Bayesian Inferences

The deep web is comprised of a large corpus of information hidden behind the searchable web interfaces. Accessing content through searchable interfaces is somehow a challenging task. One of the challenges in accessing the deep web is automatically filling the searchable web forms for retrieving the maximum number of records by a minimum number of submissions. The paper proposes a methodology to improve the existing method of getting informative data behind searchable forms by automatically submitting web forms. The form text field values are obtained through Bayesian inferences. Using Bayesian networks, the authors aim to infer the values of text fields using the existing values in the label value set (LVS) table. Various experiments have been conducted to measure the accuracy and computation time taken by the proposed value selection method. It proves to be highly accurate and takes less computation time than the existing term frequency-inverse document frequency (TF-IDF) method, hence increasing the performance of the crawler.
Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.
An M-Learning Model in the Context of the Blended Synchronous Learning Environment

Mobile learning provides more flexibility and holds considerable promise for improving the learning process and promoting lifelong learning. In order to reduce the sense of isolation felt by the learners, this research integrates mobile learning in the blended synchronous learning environment (BSLE). This study proposed a mobile learning model in BSLE at Shanghai Open University, and 51 students' satisfaction and engagement surveys were examined. The results showed that student satisfaction with instruction and with interaction can significantly predict behavioral engagement, while satisfaction with instruction and with technology can significantly predict psychological engagement. The findings prove that the mobile learning mode in the blended synchronous learning environments is effective and contributes to the predictors of student engagement. Thus, it can provide some insights to construct a more flexible and effective learning space.
Microblog Emotion Analysis Using Improved DBN Under Spark Platform

In order to solve the problems that traditional single-machine methods find it difficult to complete the task of emotion classification quickly, and the time efficiency and scalability are not high; a microblog emotion analysis method using improved deep belief network (DBN) under Spark platform is proposed. First, the Hadoop distributed file system is used to realize the distributed storage of text data, and the preprocessed data and emotion dictionary are converted into word vector representation based on the continuous bag-of-words model. Then, an improved DBN model is constructed by combining the adaptive learning method of DBN with the active learning method, and it is applied to the learning analysis of text word vectors. Finally, the data parallel optimization of the improved DBN model is realized, based on Spark platform to accurately and quickly obtain the emotion types of microblog texts. The experimental analysis of the proposed method based on the microblog text data set shows that the classification accuracy is more than 94%.
Lossless Decoding Method of Compressed Coded Video Based on Inter-Frame Differential Background Model

For the problems of low decoding accuracy, long decoding time, and low quality of decoded video image in the traditional lossless decoding method of compressed coded video, a lossless decoding method of compressed coded video based on inter frame difference background model is proposed. At the coding end, the inter frame difference background model is used to extract the single frame video image, and the mixed coding method is used to compress the video losslessly. At the decoding side, the CS-SOMP (compressive sensing-synchronous orthogonal matching pursuit algorithm) joint reconstruction algorithm is composed of synchronous orthogonal matching pursuit algorithm (SOMP) and K-SVD (kernel singular value decomposition) algorithm to losslessly decode the compressed encoded video. The simulation results show that the lossless decoding method based on the inter frame difference background model has higher accuracy, shorter decoding time, and ensures the quality of the decoded video image.
An epistemic logic of preferences

Abstract The article studies preferences of agents in a setting with imperfect information. For such a setting, the authors propose a new class of preferences. It is said that an agent prefers one statement over another if, among all indistinguishable worlds, the agent prefers the worlds where the first statement is true to those where the second one is true. The main technical result is a sound and complete logical system describing the interplay between a binary modality capturing preferences and the knowledge modality. The proof of completeness is using a newly proposed “tumbled pairs” technique.
A Light Weight Temper Resistance Client File in an External Memory for Remote User Authentication and Access Control

This research proposes a lightweight tamper resistant client file in an external memory as an alternative to smart card for remote user authentication and access control. The benefit of using this special client file is portability and ease of acquirement, especially in school online portals, online resources portals, and e-commerce portals. The characteristics and design considerations that make smart card tamper resistant are reviewed. Techniques and characteristics to make a client file in an external memory to exhibit a lightweight tamper resistant property has been formulated. The Kumari et al.'s scheme, which is the latest research that uses external memory for remote user authentication, has been reviewed. The basic system design and software design of the proposed client file is presented and modeled. This will enable implementation of the proposed system using any prepared programming or scripting language of one's choice. The proposed scheme and reviewed scheme are also evaluated for efficiency, tamper resistance, and impersonation attack.
Global path guided vehicle obstacle avoidance path planning with artificial potential field method

Abstract An artificial potential field method based on global path guidance (G‐APF) is proposed for target unreachability and local minima problems of the conventional artificial potential field (APF) method in complex environments with dynamic obstacles. First, for the target unreachability problem, the global path attraction is added to the APF; second, an obstacle detection optimisation method is proposed and the optimal virtual target point is selected by setting the evaluation function to improve the local minima problem; finally, based on the obstacle detection optimisation method, the gravitational and repulsive processes are improved so that the path can pass through the narrow channel smoothly and remain collision‐free. Experiments show that the method optimises 40.8% of the total path corners, reduces 81.8% of the number of path oscillations, and shortens 4.3% of the path length in Map 1. It can be applied to the vehicle obstacle avoidance path planning problem in complex environments with dynamic obstacles.