Reviewerly/exHarmony huggingface dataset:
Semi-Automatic Construction of a Readability Corpus for the Vietnamese Language

Text readability is a measure of how easy or difficult it is to read a text. This readability factor plays a crucial role in the processes of drafting and comprehending the texts, affecting the choice of proper texts for reading. While text readability has been a focus in research on computational linguistics for English and other resource-rich languages, there is still little work on this subject in understudied languages like Vietnamese. In this study, we propose a semi-automated method to build a corpus for text readability purposes for the Vietnamese language. This method performs the classification of new documents based on comparing the correlation readability with the documents that have been manually pre-assessed by language experts. The evaluation results of experts show that the classification accuracy of documents in the corpus is over 90%. The experimental results on other available corpus also show that our proposed method can not only address the lack of corpora that are graded on various difficulty levels in Vietnamese, but it can also be extended to the analysis of other resource-poor languages without requiring major adjustments.
Investigating country-focused studies in Library and Information Science journals

A country-focused study can be defined as a type of study in which the article’s topic is limited to a particular country. Based on the assumption that a country-focused article contains one or more country names in the title or keywords (CNtk), the objective of this study was to investigate the characteristics of country-focused articles in Library and Information Science (LIS) journals. For this study, we selected 30 journals from the Scimago Journal Rank with the highest h-index scores to empirically investigate LIS articles with CNtk. The respective bibliographic records of these journals from 2010 to 2020 were downloaded from Scopus. In terms of subject categories, journals that addressed scientific and technical topics published fewer country-focused articles. In contrast, those on culture, society and government published a higher number of country-focused articles. One exception to this generalisation is that country-focused studies were found to be highly prevalent in certain technical subjects, such as bibliometrics. Although additional empirical evidence is needed for other fields of study, the proposed method seems valuable in analysing journal characteristics since it provides country-specific information about the published articles.
Scanner++: Enhanced Vulnerability Detection of Web Applications with Attack Intent Synchronization

Scanners are commonly applied for detecting vulnerabilities in web applications. Various scanners with different strategies are widely in use, but their performance is challenged by the increasing diversity of target applications that have more complex attack surfaces (i.e., website paths) and covert vulnerabilities that can only be exploited by more sophisticated attack vectors (i.e., payloads). In this paper, we propose Scanner++, a framework that improves web vulnerability detection of existing scanners through combining their capabilities with attack intent synchronization. We design Scanner++ as a proxy-based architecture while using a package-based intent synchronization approach. Scanner++ first uses a purification mechanism to aggregate and refine attack intents, consisting of attack surfaces and attack vectors extracted from the base scanners’ request packets. Then, Scanner++ uses a runtime intent synchronization mechanism to select relevant attack intents according to the scanners’ detection spots to guide their scanning process. Consequently, base scanners can expand their attack surfaces, generate more diverse attack vectors and achieve better vulnerability detection performance. For evaluation, we implemented and integrated Scanner++ together with four widely used scanners, BurpSuite, AWVS, Arachni, and ZAP, testing it on ten benchmark web applications and three well-tested real-world web applications of a critical financial platform from our industry partner. Working under the Scanner++ framework helps BurpSuite, AWVS, Arachni, and ZAP cover 15.26%, 37.14%, 59.21%, 68.54% more pages, construct 12.95×, 1.13×, 15.03×, 52.66× more attack packets, and discover 77, 55, 77, 176 more bugs, respectively. Furthermore, Scanner++ detected eight serious previously unknown vulnerabilities on real-world applications, while the base scanners only found three of them.
Evaluating the Effect of EFL College Students’ Intention To Utilize Mobile English Vocabulary in The Learning Process: A TAM Framework

In today's world, it is no longer a surprise that (smart) mobile devices have become the most helpful technological devices to be used for various purposes, one of which is in English education. Therefore, studies on improving language learning, especially vocabulary for learners using mobile device technology, have become commonplace in today's digital era. However, to achieve maximum use, there needs to be an intention from students to accept the English vocabulary technology in the learning process. Therefore, the purpose of this study was to evaluate the integration capability of the Technology Acceptance Model (TAM) to predict and explain EFL college students' intention to utilize mobile English vocabulary learning. This research involves TAM because TAM has been widely applied to study information technology because of its effectiveness in assessing the level of user acceptance. Through a quantitative method using a cross-sectional survey approach involving 456 respondents, this study ultimately found that, after being analyzed using the PLS-SEM analysis technique, the constructs in TAM, including perceived ease of use (β = 0.302, T-Value = 6.587, and P-value = 0.000) and usefulness (β = 0.359, T-Value = 7.501, dan P-value = 0.000), had a positive and significant effect on EFL college students' intention to utilize mobile English vocabulary. Perceived usefulness shows the most dominant effect. However, this study has limitations that need to be considered. And, of course, caution in generalizing is necessary.
GO-Finder: A Registration-free Wearable System for Assisting Users in Finding Lost Hand-held Objects

People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, various computational systems that provide information on their locations have been developed. However, prior systems for assisting people in finding objects require users to register the target objects in advance. This requirement imposes a cumbersome burden on the users, and the system cannot help remind them of unexpectedly lost objects. We propose GO-Finder (“Generic Object Finder”), a registration-free wearable camera-based system for assisting people in finding an arbitrary number of objects based on two key features: automatic discovery of hand-held objects and image-based candidate selection. Given a video taken from a wearable camera, GO-Finder automatically detects and groups hand-held objects to form a visual timeline of the objects. Users can retrieve the last appearance of the object by browsing the timeline through a smartphone app. We conducted user studies to investigate how users benefit from using GO-Finder. In the first study, we asked participants to perform an object retrieval task and confirmed improved accuracy and reduced mental load in the object search task by providing clear visual cues on object locations. In the second study, the system’s usability on a longer and more realistic scenario was verified, accompanied by an additional feature of context-based candidate filtering. Participant feedback suggested the usefulness of GO-Finder also in realistic scenarios where more than one hundred objects appear.
A comparative analysis of matrix reordering algorithms regarding canonical data patterns

Matrix reordering is an Information Visualization topic that aims to study how to reveal information hidden within matrix-based visualizations, through appropriate reordering of its rows and columns. Considering the plethora of algorithms currently available to perform this task, and the set of matrix patterns presented in the visualization literature, we noted a lack of a comprehensive comparative analysis related to a subset of these patterns, known as canonical data patterns. Thus, this work aimed to perform a broad comparison of 37 state-of-the-art reordering algorithms by measuring the efficiency (execution time) and effectiveness (which combines objective functions as Moore Neighborhood, Minimal Span Loss Function and Circular Correlation, and a qualitative approach by visually assessing the output matrices) of the algorithms. We used as input a large synthetic matrix data set with different canonical data patterns. As a result, it was possible to indicate the most appropriate reordering algorithms for each pattern considered.
Xamã : Optical character recognition for multi-domain model management

Abstract The development of systems following model-driven engineering can include models from different domains. For example, to develop a mechatronic component one might need to combine expertise about mechanics, electronics, and software. Although these models belong to different domains, the changes in one model can affect other models causing inconsistencies in the entire system. Only few tools, however, support management of models from different domains. Indeed, these models are created using different modeling notations and it is not plausible to use a multitude of parsers geared toward each and every modeling notation. Therefore, to ensure maintenance of multi-domain systems, we need a uniform approach that would be independent from the peculiarities of the notation. Notation independence implies that such a uniform approach can only be based on elements commonly present in models of different domains, i.e., text, boxes, and lines. In this study, we investigate the suitability of optical character recognition (OCR) as a basis for such a uniformed approach. We select graphical models from various domains that typically combine textual and graphical elements. We start by analyzing the performance of Google Cloud Vision and Microsoft Cognitive Services, two off-the-shelf OCR services. Google Cloud Vision performed better than Microsoft Cognitive Services being able to detect text of 70% of model elements. Errors made by Google Cloud Vision are due to absence of support for text common in engineering formulas, e.g., Greek letters, equations, and subscripts. We identified the multi-line text error as one of the main issues of using OCR to recognize textual elements in models from different domains. This error happens when OCR misinterprets one textual element as two separate elements. To address the multi-line text error, we build Xamã on top of Google Cloud Vision. Xamã includes two approaches to identify whether the elements are positioned on a single line or multiple lines, and merge those identified as positioned on multiples lines. With and without shape detection, Xamã correctly identified 956 and 905 elements, respectively, out of 1171. Additionally, we compared the accuracy of Xamã and state-of-the-art tool img2UML, and we observe that Xamã outperformed img2UML in both precision and recall, being able to recognize 433 out of 614 textual elements as opposed to 171 by img2UML.
Manipulator Mechanical System Design and Operation Algorithm Based on Multisensor Fusion

With the advancement of modern technology and the improvement of the level of social productivity, people are paying more and more attention to the reduction of labor intensity and the change of working environment. At the same time, in order to explore the application of multisensor fusion in the design and operation algorithm of manipulator mechanical system, we first introduced the big data fusion, proposed the multisensor data fusion algorithm, and analyzed the gradient descent method, auxiliary filtering method, and Kalman filter. Finally, the rated motion parameters of each axis motor of the manipulator are analyzed, and at the same time, the motion state of each axis motor of the manipulator under nonrated speed is analyzed. The research proposal in this paper is aimed at contributing to improving productivity.
Lower Bounds from Fitness Levels Made Easy

Abstract One of the first and easy to use techniques for proving run time bounds for evolutionary algorithms is the so-called method of fitness levels by Wegener. It uses a partition of the search space into a sequence of levels which are traversed by the algorithm in increasing order, possibly skipping levels. An easy, but often strong upper bound for the run time can then be derived by adding the reciprocals of the probabilities to leave the levels (or upper bounds for these). Unfortunately, a similarly effective method for proving lower bounds has not yet been established. The strongest such method, proposed by Sudholt (2013), requires a careful choice of the viscosity parameters $$\gamma _{i,j}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>γ</mml:mi> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>,</mml:mo> <mml:mi>j</mml:mi> </mml:mrow> </mml:msub> </mml:math> , $$0 \le i &lt; j \le n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>i</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mi>j</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> . In this paper we present two new variants of the method, one for upper and one for lower bounds. Besides the level leaving probabilities, they only rely on the probabilities that levels are visited at all. We show that these can be computed or estimated without greater difficulties and apply our method to reprove the following known results in an easy and natural way. (i) The precise run time of the (1+1) EA on LeadingOnes . (ii) A lower bound for the run time of the (1+1) EA on OneMax , tight apart from an O ( n ) term. (iii) A lower bound for the run time of the (1+1) EA on long k -paths (which differs slightly from the previous result due to a small error in the latter). We also prove a tighter lower bound for the run time of the (1+1) EA on jump functions by showing that, regardless of the jump size, only with probability $$O(2^{-n})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> the algorithm can avoid to jump over the valley of low fitness.
Dynamics of quantum effects in a three-level system interacting with two-mode time-dependent fields including parametric down conversion and damping

In this article, the interaction between a time-dependent damping field and Λ-type of three-level atom is investigated. The effect of the atom and the fields' parameters on the behaviour of the atomic inversion, the generated entanglement between the atom and the cavity modes, the Pancharatnam geometric phase are discussed. It is shown that the phenomena of the collapses and revivals for the three physical quantities depend on the time-dependent coupling and the damping parameter. The long-lived behaviour of the non-classical correlation is observed either on the presence of only time-dependent coupling or at large values of the damping parameter. The damping effect has different features on the three phenomena, where the atomic inversion decays, the upper bounds of entanglement increases and increases periodic time of the Pancharatnam geometric phase.
A Video Classification Method Based on Spatiotemporal Detail Attention and Feature Fusion

With the explosive growth of Internet video data, demands for accurate large-scale video classification and management are increasing. In the real-world deployment, the balance between effectiveness and timeliness should be fully considered. Generally, the video classification algorithm equipped with time segment network is used in industrial deployment, and the frame extraction feature is used to classify video actions However, the issue of semantic deviation will be raised due to coarse feature description. In this paper, we propose a novel method, called image dense feature and internal significant detail description, to enhance the generalization and discrimination of feature description. Specifically, the location information layer of space-time geometric relationship is added to effectively engrave the local features of convolution layer. Moreover, the multimodal feature graph network is introduced to effectively improve the generalization ability of feature fusion. Extensive experiments show that the proposed method can effectively improve the results on two commonly used benchmarks (kinetics 400 and kinetics 600).
Analysis and Evaluation of the Relationship between Teaching Pressure and Self-Efficacy of College Teachers Based on Artificial Neural Network

With the reform of the education system, the society today raises higher requirements for college teachers, which cause immense psychological stress among them. To enhance the quality of teachers, it is important to analyze the relationship between teaching pressure and self-efficacy. Therefore, this paper tries to analyze and evaluate the relationship between teaching pressure and self-efficacy of college teachers based on artificial neural network. Firstly, a grey correlation analysis (GRA) model was established for the teaching pressure and self-efficacy of college teachers, and the analysis procedure was detailed. Then, the possible multicollinearity of the GRA model was tested. In addition, a linear regression model was established based on Lasso variable selection model and ridge regression variable selection model, aiming to eliminate the multicollinearity between various teaching pressure factors in the GRA model. Finally, a multilabel learning algorithm was proposed based on neural network and label correlation. In this way, the correlations between the various teaching pressure factors and teachers’ self-efficacy were mined automatically. The proposed model proved valid through experiments.
Estimation of the Complexity of a Finite Mixture Distribution: From Well- to Less Known Methods

Abstract Mixture models occur in numerous settings including random and fixed effects models, clustering, deconvolution, empirical Bayes problems and many others. They are often used to model data originating from a heterogeneous population, consisting of several homogeneous subpopulations, and the problem of finding a good estimator for the number of components in the mixture arises naturally. Estimation of the order of a finite mixture model is a hard statistical task, and multiple techniques have been suggested for solving it. We will concentrate on several methods that have not gained much popularity yet deserve the attention of practitioners. These can be categorized into three groups: tools built upon the determinant of the Hankel matrix of moments of the mixing distribution, minimum distance estimators, likelihood ratio tests. We will address theoretical pillars underlying each of the methods, provide some useful modifications for enhancing their performance and present the results of the comparative numerical study that has been conducted under various scenarios. According to the results, none of the methods proves to be a “magic pill”. The results uncover limitations of the techniques and provide practical hints for choosing the best-suited tool under specific conditions.
The Technological Transformation Process From Electronic Intelligence to Cyber Intelligence

Throughout the whole of human history, concepts such as defense, security, safety and intelligence have been very important for human beings on a personal level, and for human communities in general.The sociological transformation that came as a result of these processes played a key role in the development of science and technology.Thanks to the developments in electronic science during the 20th century, systems using electromagnetic energy have come to the fore.This development process which started with systems such as telephones, radios and radars has eventually been used in many different areas such as air defense systems, guided missiles, early warning receivers, communication systems, and computers.For this reason, the control and active use of what can be called the electromagnetic spectrum in short, has been an important factor in all kinds of activities.By the 21st century, almost all of the systems used in this process began to operate in a cyberspace environment and became software controlled.The concept of the target intelligence needed in this transformation process has changed dimensions and shifted from electronic intelligence to cyber intelligence.This study will focus on the transformation of the electronic intelligence process, which is an indispensable element of the 20th century, into the concept of cyber intelligence in the 21st century.
Label Propagation Clustering Algorithm Based on Adaptive Angle

The direction-based label propagation clustering (DBC) algorithm needs to set the number of neighbors ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> ) and the angle value (degree), which are highly sensitive. Moreover, DBC algorithm is not suitable for datasets with uneven neighbor density distribution. To overcome above problems, we propose an improved DBC algorithm based on adaptive angle and label redistribution (ALR-DBC). The ALR-DBC algorithm no longer input parameter degree, but dynamically adjusts the deviation angle through the concept of high-low density region to determine the receiving range. This flexible receiving range is no longer affected by the uneven distribution of neighbor density. Finally, those points that do not meet the expectations of the main direction are redistributed. Experiments show that the ALR-DBC algorithm performs better than DBC algorithm in most artificial datasets and real datasets. It is also superior to the classical algorithms listed. It also has good experimental results when applied to wireless sensor data annotation.