Reviewerly/exHarmony huggingface dataset:
Word sense disambiguation using semantic kernels with class-based term values

In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval). With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better than others?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?, 4.) Is their combination superior to either type?, 5.) Is ensemble of these kernels perform better than the baseline?, 6.) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms can outperform baseline in terms of F-score.
INVESTIGATING THE EFL TEACHERS’ ATTITUDES TOWARD THE INTEGRATION OF WEB 2.0 TOOLS IN TEACHING ENGLISH AT ISLAMIC BOARDING SCHOOLS

The purposes of this study are to investigate EFL teachers’ attitudes toward the integration of Web 2.0 tools in teaching English at Islamic Boarding Schools and to examine the extent of using Web 2.0 tools in teaching English at Islamic Boarding Schools. There were 18 EFL teachers from Islamic Boarding Schools in South Kalimantan province, Indonesia participated in this study. The data were taken from the EFL teachers were gathered by using the questionnaire, analyzed using descriptive statistics and followed up in the interview. The results of the study indicated that most EFL teachers have positive attitudes toward the integration of Web 2.0 tools in teaching English and percentages of their attitudes were high, with 75%. The findings also showed that almost all of the EFL teachers strongly agreed that Web 2.0 tools made them more efficient and interesting in the teaching and learning process at the class. The majority of EFL teachers that integrated towards Web 2.0 tools in teaching English used tools such as YouTube, Google Sites, blogs, and wikis. However, there were the barriers that were faced by the EFL namely lack of technological equipment in school, limited time at the class to explore the technology and the students were not allowed to bring communication tools at the class, so Web 2.0 tools have not been fully explored at the English class.&#x0D; Keywords: EFL teachers attitudes, The integration of Web 2.0 tools, Teaching English
Lagrangian Decomposition for Optimal Cost Partitioning

Optimal cost partitioning of classical planning heuristics has been shown to lead to excellent heuristic values but is often prohibitively expensive to compute. Lagrangian decomposition and Lagrangian relaxation are classical tools in mathematical programming that apply to optimization problems with a special block structure. We analyze the application of Lagrangian decomposition to cost partitioning in the context of operator-counting heuristics and interpret Lagrangian multipliers as cost functions for the combined heuristics. This allows us to view the computation of an optimal cost partitioning as an iterative process that can be seeded with any cost partitioning and improves over time. We derive an anytime algorithm to compute an optimal non-negative cost partitioning of abstraction heuristics without involving an LP solver. In each iteration, the computation reduces to independent shortest path problems in all abstractions. Finally, we discuss the extension to general cost functions.
Pseudo Supervised Matrix Factorization in Discriminative Subspace

Non-negative Matrix Factorization (NMF) and spectral clustering have been proved to be efficient and effective for data clustering tasks and have been applied to various real-world scenes. However, there are still some drawbacks in traditional methods: (1) most existing algorithms only consider high-dimensional data directly while neglect the intrinsic data structure in the low-dimensional subspace; (2) the pseudo-information got in the optimization process is not relevant to most spectral clustering and manifold regularization methods. In this paper, a novel unsupervised matrix factorization method, Pseudo Supervised Matrix Factorization (PSMF), is proposed for data clustering. The main contributions are threefold: (1) to cluster in the discriminant subspace, Linear Discriminant Analysis (LDA) combines with NMF to become a unified framework; (2) we propose a pseudo supervised manifold regularization term which utilizes the pseudo-information to instruct the regularization term in order to find subspace that discriminates different classes; (3) an efficient optimization algorithm is designed to solve the proposed problem with proved convergence. Extensive experiments on multiple benchmark datasets illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.
Chinese Text Relation Extraction with Multi-instance Multi-label BLSTM Neural Networks

Recently, deep learning models have emerged as powerful tools for relation extraction.However, little work has been done on relation extraction for the Chinese language.One major challenge for relation extraction in Chinese texts is that Chinese sentences have no obvious word segmentation.This ambiguity increases the possibility of word segmentation errors.Another challenge is the lack of broad-scale Chinese text datasets.In this paper, we propose an attention-based multi-instance multi-label bidirectional long short-term memory network for distantly supervised Chinese relation extraction.Our model takes Chinese character embeddings and position embeddings as input without Chinese word segmentation errors.Then, the attention mechanism is used to extract richer Chinese character and sentence features.Finally, we handle the multi-label nature of relation extraction by using multi-label loss functions in the neural network classifier.Based on the idea of distant supervision, we constructed a new dataset for relation extraction in Chinese texts.Experiments on this dataset show that our method has achieved relatively high performance, and that the proposed network architecture is suitable for Chinese relation extraction.Furthermore, we also ran experiments on a popular English benchmark dataset, and the results show that our method is superior to some existing methods.
Infrastructure Authentication, Authorization and Accounting Solutions for an OpenStack Platform

The security of open-source clouds is important, and there are many challenges in the area of user access which can be resolved using various implementations. OpenStack-based clouds must implement some identity solution based on a built-in Keystone project. In addition, it is important to secure the lower layer of this cloud and its infrastructure, where there is some potential vulnerability. This area can be mistakenly underestimated. The approach based on an authentication, authorization and accounting (AAA) solution offers some possibilities for extending Keystone by expanding it to other services, which will help to integrate it into the existing environment. Furthermore, it will enhance security features and thus secure the entire environment. This article discusses the authentication, authorization and accounting principles for an OpenStack platform and offers a simplified description of their implementation and testing.
Research on Cross-Cluster Migration Technologies

To solve the problem that OpenStack cloud platforms can not well support the migration of virtual machines across clusters, several cross-cluster migration technologies are proposed. Based on a novel multi-cluster collabrative architecture, a cross-cluster static migration approach and a cross-cluster live migration approach are provided. The experiments of comparing our approachs with the original intra-cluster migration are carried out. The results show that the proposed approaches have nearly the same migration time cost but gain better performance in disaster tolerance.
Artificial intelligence meets software engineering in the classroom

We aimed to assess the reliability of teaching Artificial Intelligencefor Software Engineering master students. We propose a semi-interactive course where the students have to develop applications for solving real world problems by using various intelligent tools. We try to integrate these two disciplines, since both deal with modeling of the real case studies, sharing some common elements.We report on a study that we conducted on observing student teams as they develop AI-based applications. We validate the proposed semi-interactive course by using various criteria. In addition, we checked if some best practices from industrial teams are followed by our students.
Compaction of Church Numerals

In this study, we address the problem of compaction of Church numerals. Church numerals are unary representations of natural numbers on the scheme of lambda terms. We propose a novel decomposition scheme from a given natural number into an arithmetic expression using tetration, which enables us to obtain a compact representation of lambda terms that leads to the Church numeral of the natural number. For natural number n, we prove that the size of the lambda term obtained by the proposed method is O ( ( slog 2 n ) ( log n / log log n ) ) . Moreover, we experimentally confirmed that the proposed method outperforms binary representation of Church numerals on average, when n is less than approximately 10,000 .
Evaluating Parameterization Methods for Convolutional Neural Network (CNN)-Based Image Operators

Recently, deep neural networks have been widely used to approximate or improve image operators. In general, an image operator has some hyper-parameters that change its operating configurations, e.g., the strength of smoothing, up-scale factors in super-resolution, or a type of image operator. To address varying parameter settings, an image operator taking such parameters as its input, namely a parameterized image operator, is an essential cue in image processing. Since many types of parameterization techniques exist, a comparative analysis is required in the context of image processing. In this paper, we therefore analytically explore the operation principles of these parameterization techniques and study their differences. In addition, performance comparisons between image operators parameterized by using these methods are assessed experimentally on common image processing tasks including image smoothing, denoising, deblocking, and super-resolution.
Slice Localization for Three-Dimensional Breast Ultrasound Volume Using Deep Learning

In routine ultrasonography, slice images of the internal organs of the human body are usually generated through an 1D array probe. The position and orientation of the probe is adjusted manually to obtain slice planes with pathological features. This is quite dependent on the experience and technique of a sonographer. This paper aims to locate 2D slice planes in a 3D breast ultrasound volume, which has significant application value in clinical ultrasound examinations. We propose a deep learning approach mapping all possible 2D image slices to their 3D coordinates parameters using a fully connected neural network implemented on MATLAB. We emphasize that this training must be done separately for each patient since the mammary tissue structure varies greatly from one person to another. The trained network can be interpreted as an image-slice location database for each patient. Our study is validated on GE ABUS (Automated Breast Ultrasound System) volume data. Each 2D image slice has four spatial parameters. The method achieves a prediction error of 0.14mm/0.25mm and 0.5 degree/0.3 degree for translation (x/y) and rotation (yaw/roll) parameters respectively, averaged over all practically scannable slices. It takes less than 0.1ms to predict the location of one 64Ã—64 slice image. Thus, slice locations may be displayed with high accuracy in real-time when scanning with a conventional 1D probe, potentially allowing physicians to manipulate the probe to any scan planes of interest.
Method for Quantum Key Establishment through a Multimode Fiber

Secure communication became extremely important in the Information Age. Quantum communication protocols have been developed to provide absolutely secure transmission of information. Historically, quantum key distribution (QKD) uses the two-dimensional polarization basis to encode information and the key element of long-distance communication is single-mode optical fiber. However, it intrinsically limits the information content to 1 bit per photon. Nowadays, significant effort is devoted to increase the data carrying capacity of a single quantum secure optical line. Large-core multimode fibers support a multitude of transverse optical modes and can potentially transfer information at much higher density.
Innovative Development of Tools and Technologies to Ensure the Russian Information Security and Core Protective Guidelines

In this work, we produced the analysis results of information technology development and information security threats in various areas.Effective use information technologies is a core factor for accelerating economic development and organizing new information society.However, information technology evolution is limited by capabilities of information security tools, their extensive development and ever-growing threats.The existing autocratic control of the information security system leads to the inevitable introduction of bans on technologies and infrastructures not controlled by it, that in turn reduces the effectiveness of innovation activities in the field of information technologies.Contradictions between the growth of information technology applications by business entities and the achievement of an acceptable level of their information security are shown.It is shown that intelligence tools and an effective information security system are in constant antagonistic conflict that can be described by a zero-sum pair game, and there is a tendency for advanced technical development of intelligence systems and tools in relation to the information security system.The possible ways for innovative development of information security tools and technologies are produced.This tools and technologies implementing principle of non-conflict protection.
Multi-project Regression based Approach for Software Defect Number Prediction

Software defect prediction can make software quality assurance (SQA) process more efficient, economic and targeted.Previous studies mainly focused on classifying software modules as defect-prone or not.However, prediction the number of defects for a new software module is rarely investigated.Moreover, these studies built models independently for each project, which may ignore the relatedness among multiple projects.To effectively utilize the relatedness, we propose a novel approach MPR (multiproject regression) for SDNP (software defect number prediction).To verify the effectiveness of MPR, we perform experimental studies on 30 real-world projects and compare our approach with 6 state-of-the-art baselines (i.e., LR, NNR, SVR, DTR, BRR and DBR).AAE (Average absolute error) and ARE (average relative error) performance measures are used to evaluate the performance of MPR.The results show MPR can achieve better performance in most cases, which indicates the competitiveness of MPR in the context of SDNP.
Digital nets in dimension two with the optimal order of L_p discrepancy

We study the L p discrepancy of two-dimensional digital nets for finite p. In the year 2001 Larcher and Pillichshammer identified a class of digital nets for which the symmetrized version in the sense of Davenport has L 2 discrepancy of the order logN/N, which is best possible due to the celebrated result of Roth. However, it remained open whether this discrepancy bound also holds for the original digital nets without any modification.
KawalPilpres2019: a highly secured real count voting escort architecture

This paper reports on the highly secured information security architecture used by the KawalPilpres 2019 to escort Voting Commission (KPU) data entry. For the first time, a voting escort implements ISO 27001 compliance information security. As of 15 May 2019, 9550 volunteers reported 482,602 voting data of 336,445 voting booths, both in the country and overseas, through the micro-apps KawalPilpres2019. PeSanKita is used as a secure communication channel and to run micro-apps KawalPilpres2019. Double Ratchet Algorithm secures the channel. Different from other voting escort initiatives, KawalPilpres2019 uses (1) primarily C1 Plano photo, (2) no upload limit per voting booth, (3) no web upload, rather via PeSanKita Secured Platform. Behind the scenes, the verification process is done twice before displaying data to the publicly accessible monitoring web. The result is a robust voting escort system, difficult to hack and guarantee data integrity. Guaranteed security, availability, and data integrity are the main requirements for future eVoting systems.
A Dynamic Programming Track-Before-Detect Algorithm Based on EKF for Acceleration Targets

Aiming at nonlinear issue of real system model to some extent in projects and poor performance as a result of constant transfer step size in conventional Dynamic Programming Track-Before-Detect (DP-TBD) algorithm when tracking acceleration targets, a DP-TBD algorithm based on extended Kalman filter (EKF-DPTBD) is proposed in this paper. The new algorithm makes use of EKF to estimate different transfer step sizes between consecutive states, avoiding mismatch between the transfer step size and target velocity when detecting acceleration maneuvering targets and thus the state searching efficiency for acceleration targets is improved. The proposed method can track targets with both strong and weak maneuverability accurately. Simulation results verify the effectiveness of the proposed algorithm and performance of this algorithm is analyzed and compared with conventional DP-TBD algorithm.
Using Games to Improve Students’ Active Involvement in the Learning of English Syntax at IAIN Madura: An Autonomous Learning

Learning English syntax is taken as a boring subject for the majority of students since the activities frequently monotonous and the classroom setting becomes quiet and too formal.The study is intended to discover the use of games in the learning of English syntax.It is conducted to find out whether the games provided by students themselves (autonomously) are able to make a joyful learning activities and able to increase their involvement in the learning process.The study makes use descriptive research which is classified into a case study.The data are collected through classroom observation, questionnaire to gain students' response toward the classroom learning activities.The research subjects are the students of 5th semester at English department IAIN Madura who join in English syntax class.The researcher focuses on D class, with 33 students, where the time schedule for the learning is at 13.00 in the afternoon.From the data result, it is found that the use of games, which are prepared by the students autonomously, is able to make the class more joyful and able to increase their active involvement in the learning activities.Making this study distinctive from other studies is the games are provided by the students themselves instead of being provided by the lecturer.So, learning autonomy is really there.The lecturer only gives them a clear rule that is the learning activities should be made joyful by using any games related to the topic being discussed.
Concepts and trends of information communication networks

This paper discusses the history, fundamental theory and current concepts, and latest trends of information communication networks.The important challenges faced by such networks analyzed and the performance gains of collaborative multi-dimensional resource management and control are explored.The coupling relationships among data, network, and intelligence, as well as the key technologies that pave the way to future information communication networks with full spectrum, space, and intelligence are also discussed.
Real-time face tracking in video content using Viola-Jones algorithm

In this paper an implementation of an application for detecting and tracking faces in real-time using the Viola-Jones algorithm and OpenCV library has been presented.Also, using the developed application it is possible to recover from failure during face tracking.Video content is obtained from a file or webcam.
Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

A new image feature extraction method for face recognition called Tri-direction 2D-Fisher Discriminant Analysis (T2D-FDA) is proposed to deal with the Small Sample Size (SSS) problem in conventional 1D-Fisher Discriminant Analysis (1D-FDA). Moreover, the essence of T2D-FDA is investigated, and the equivalence of the left-multiplying 2D-FDA of the original image matrices and the left-multiplying D2D-FDA of diagonal image matrices is verified if each column is viewed as a computational unit. Different from the 1D-LDA based approaches, the T2D-FDA is based on 2D image matrices rather than column vectors, so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist anymore because within-class scatter matrices constructed is full-rank in T2D-FDA. The proposed method is applied to face recognition where only few training images exist for each subject. Experiment results show T2D-FDA outperforms the current linear subspace methods in SSS problem on four public databases: ORL, Yale, AR and FERET face databases.
Influence of Color and Size of Particles on Their Perceived Speed in Node-Link Diagrams

Edges in networks often represent transfer relationships between vertices. When visualizing such networks as node-link diagrams, animated particles flowing along the links can effectively convey this notion of transfer. Variables that govern the motion of particles, their speed in particular, may be used to visually represent edge data attributes. Few guidelines exist to inform the design of these particle-based network visualizations, however. Empirical studies so far have only looked at the different motion variables in isolation, independently from other visual variables controlling the appearance of particles, such as their color or size. In this paper, we report on a study of the influence of several visual variables on users' perception of the speed of particles. Our results show that particles' luminance, chromaticity and width do not interfere with their perceived speed. But variations in their length make it more difficult for users to compare the relative speed of particles across edges.