Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

Associative Classification (AC) is envisioned as one of the most attractive classification approaches to facilitate managers for prediction and decision making in a highly accurate and easily interpretable way. The most of existing AC algorithms mainly focus on the two static metrics of association rules: support and confidence. However, in this paper, we identify a potential limitation of the confidence and further point out these AC algorithms merely consider the explicit expression of classification information and knowledge of Class Association Rules (CARs) and neglect the implicit expression of classification information and knowledge of CARs. And even the explicit expression of classification information and knowledge of CARs is equivalent, the implicit expression of classification information and knowledge of them may be different. Thus, these CARs will cause different influence on prediction or decision-making in terms of the interpretability and rationality of classification, and will result in predictive bias or decision-making bias in practice. In response to this drawback, we introduce the notion of information entropy and propose an innovative approach for associative classification based on information entropy of frequent attribute set, named EAC. Different from the existing schemes, the proposed EAC algorithm enjoys the following promising merits: (1) lower predictive bias or decision-making bias by levering the information entropy of frequent attribute set; (2) better interpretability and rationality by setting higher support threshold; (3) setting global optimum parameter dynamically through repeated trials with reducing the dimension of data sets. Experiments on 20 well-known benchmark data sets demonstrate that our EAC approach is highly competitive to other state-of-the-art AC algorithms in terms of predictive bias or decision-making bias, interpretability, and efficiency, which can be used to construct a new classifier efficiently and effectively in many realistic scenarios.
Neural network method for base extension in residue number system

Confidential data security is associated with the cryptographic primitives, asymmetric encryption, elliptic curve cryptography, homomorphic encryption, cryptographic pseudorandom sequence generators based on an elliptic curve, etc. For their efficient implementation is often used Residue Number System that allows executing additions and multiplications on parallel computing channels without bit carrying between channels. A critical operation in Residue Number System implementations of asymmetric cryptosystems is base extension. It refers to the computing a residue in the extended moduli without the application of the traditional Chinese Remainder Theorem algorithm. In this work, we propose a new way to perform base extensions using a Neural Network of a final ring. We show that it reduces 11.7% of the computational cost, compared with state-of-the-art approaches.
Design and Application of a Prediction Model for User Purchase Intention Based on Big Data Analysis

With the proliferation of information technology, precision marketing has emerged as an important strategy to increase the return rate.To improve the effect of precision marketing, the traditional manual survey should be replaced with big data analysis to disclose user demand and understand user purchase behaviors.For accurate forecast of user purchase behaviors, this paper firstly analyzes the quality and features of the historical data on user purchases, and preprocesses the data by a self-designed procedure.Then, the various features of user purchase behaviors were summarized and optimized, and the features of user purchase intentions for products were extracted.On this basis, a DenseNet purchase intention prediction model was established on Bagging strategy.The effectiveness of the proposed model was proved through experiments.Our model enables enterprises to quickly identify potential sales targets.
Kinerja Mengajar dengan Sistem Work From Home (WFH) pada Guru di SMK Purnawarman Purwakarta

This article tries to identify the teaching performance of teachers at SMK Purnawarman Purwakarta using the Work From Home (WFH) system during the Covid-19 pandemic. The population of this study is teachers at SMK Purnawarman Purwakarta which is 22 teachers. The method used mixture of Explanation Sequential, which is qualitative data can explain further more or strengthen the initial quantitative data. The results ot this study used descriptive statistical analysis with quantitative descriptive forms that are collaborated with descriptive qualitative data analysis. Data collection techniques using questionnaires, interviews and documentation in the form of school data. The results of this study showed the average performance of teachers in teaching using work from home system is only 50%. Overall, the performance results do not exceed the 70% limit. It happened because several factors are the half of the teachers do not understand some applications that used in teaching, also the distances caused the teachers face obstacles in interaction with students optimally.
Visual attention method based on vertex ranking of graphs by heterogeneous image attributes

The paper discusses a method of visual attention based on vertex ranking of graphs on the basis of image features. The aim of the research is to develop a method that allows high-precision detection of objects in images with low color contrast between the selected and background areas. The image is pre-segmented into regions to calculate the saliency map. The graph is based on regions. Each region is associated with related regions, as well as with areas adjacent to adjacent regions. The regions are vertices of the graph. The vertices of the graph are ranked according to the characteristics of the corresponding image areas. The scope is highlighted based on requests from background areas. The saliency map is determined based on background area queries. Regions adjacent to the edges of the image belong to the background areas. Color features of the image were used in the existing approach of visual attention based on the manifold ranking. Texture features and shape features are additionally used in the proposed method to improve accuracy. Gabor's energy function is used to calculate texture features. The distance between centers of the regions is calculated by analyzing the form. The proposed method has shown good results for detecting objects in images in which the background color and object color are in similar ranges. The experimental results are presented on test images. Precision-recall curves showing the advantage of the developed method are constructed.
COMBINED PATCH-WISE MINIMAL-MAXIMAL PIXELS REGULARIZATION FOR DEBLURRING

Abstract. Deblurring is a vital image pre-processing procedure to improve the quality of images. It is a classical ill-posed problem. A new blind deblurring method based on image sparsity prior is proposed here. The proposed image sparsity prior combines patch-wise minimal and maximal pixels of latent image, and improves gradually the image sparsity during deblurring. An algorithm that is different with half quadratics splitting algorithm is applied under the maximum a posterior (MAP) framework. Experiment results demonstrate that the proposed method can keep more subtle texture and sharpened edges, reduce the artefacts in visual, and the corresponding evaluated indexes perform favourably against it of the state-of-the-art methods on synthesized, natural and remote sensing images (RSI) quantitatively.
A Study on Multisecret-Sharing Schemes Based on Linear Codes

Secret sharing has been a subject of study since 1979. In the secret sharing schemes there are some participants and a dealer. The dealer chooses a secret. The main principle is to distribute a secret amongst a group of participants. Each of whom is called a share of the secret. The secret can be retrieved by participants. Clearly the participants combine their shares to reach the secret. One of the secret sharing schemes is threshold secret sharing scheme. A threshold secret sharing scheme is a method of distribution of information among participants such that can recover the secret but cannot. The coding theory has been an important role in the constructing of the secret sharing schemes. Since the code of a symmetric design is a linear code, this study is about the multisecret-sharing schemes based on the dual code of code of a symmetric design. We construct a multisecret-sharing scheme Blakley’s construction of secret sharing schemes using the binary codes of the symmetric design. Our scheme is a threshold secret sharing scheme. The access structure of the scheme has been described and shows its connection to the dual code. Furthermore, the number of minimal access elements has been formulated under certain conditions. We explain the security of this scheme.
Searching a database of source codes using contextualized code search

Consider the case where a programmer has written some part of a program, but has left part of the program (such as a method or a function body) incomplete. The goal is to use the context surrounding the missing code to automatically "figure out" which of the codes in the database would be useful to the programmer in order to help complete the missing code. The search is "contextualized" in the sense that the search engine should use clues in the partially-completed code to figure out which database code is most useful. The user should not be required to formulate an explicit query. We cast contextualized code search as a learning problem, where the goal is to learn a distribution function computing the likelihood that each database code completes the program, and propose a neural model for predicting which database code is likely to be most useful. Because it will be prohibitively expensive to apply a neural model to each code in a database of millions or billions of codes at search time, one of our key technical concerns is ensuring a speedy search. We address this by learning a "reverse encoder" that can be used to reduce the problem of evaluating each database code to computing a convolution of two normal distributions.
Research and Verification of Convolutional Neural Network Lightweight in BCI

With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.
Implementation of an Adaptive Learning System based on Agents and Web Services

Today, the integration of web services and agent technology into Internet applications has attracted the attention of many researchers, so that these applications allow a web service to call an agent service and vice versa. Web services are emerging and promising technologies for the development, deploy-ment and integration of the Internet applications and the use of agents makes them dynamic and automatic, they can provide updates when there is new infor-mation available and improve the qualities of web services by exploiting the ca-pacities and the characteristics of agents. In this context, we propose a prototype of a multi-agent adaptive learning system based on Incremental Hybrid Case Based Reasoning in order to support the learner in his learning process by offer-ing him a learning path adapted to his profile and predict his future learning. This support will be achieved through the execution of a hybrid cycle of Case Based Reasoning which brings together a set of agents collaborating and interacting with each other to provide specific services.
Design of web systems for inventory control in the E-commerce sector under the Agile methodologies approach

Management control projects in a company have, in some cases, become a major conflict of order and decision making.In view of this, the present work seeks to achieve the design of an effective web system that provides the necessary tools to the company, through the Scrum methodology, which allows us to carry out projects of great complexity in a short time.In the case study, five Sprint were determined, which were based on stages of development of the application.The results of this study showed that the inventory control would help in the decision making of the sales made.The method applied to this study to carry out the design was very interesting to be able to propose to other companies.
Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification

Given a video containing a person, the video-based person re-identification (Re-ID) task aims to identify the same person from videos captured under different cameras. How to embed spatial-temporal information of a video into its feature representation is a crucial challenge. Most existing methods have failed to make full use of the relationship between frames during feature extraction. In this work, we propose a plug-and-play non-local attention module (NLAM) for frame-level feature extraction. NLAM, based on global spatial attention and channel attention, helps the network to determine the location of the person in each frame. Besides, we propose a non-local temporal pooling (NLTP) method used for temporal features’ aggregation, which can effectively capture long-range and global dependencies among the frames of the video. Our model obtained impressive results on different datasets compared to the state-of-the-art methods. In particular, it achieved the rank-1 accuracy of 86.3% on the MARS (Motion Analysis and Re-identification Set) dataset without re-ranking, which is 1.4% higher than the state-of-the-art way. On the DukeMTMC-VideoReID (Duke Multi-Target Multi-Camera Video Reidentification) dataset, our method also had an excellent performance of 95% rank-1 accuracy and 94.5% mAP (mean Average Precision).
Distributed and Optimal RDMA Resource Scheduling in Shared Data Center Networks

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability and convergence issues in solving a large-scale RDMA resource scheduling problem.
The Impact of English Learning Motivation and Attitude on Well-Being: Cram School Students in Taiwan

As English is a global language, it is important for students to learn it effectively and efficiently. Learning English from English cram schools is very popular in Taiwan. Most students have studied in English cram schools for some period of time of their English learning experience. The present study concerns about how English cram school learners’ English learning attitudes related to their learning motivation and learning well-being in Taiwan. By using the quantitative research methodology, an empirical research model has been proposed and 277 valid questionnaires were collected. The research results show that learning motivation has a significant impact on learning attitude and learning well-being. Then, the English learning attitude provides mediated effects between learning motivation and well-being. Learning attitude is the key to English learning well-being. Furthermore, a participant’s gender has a significant moderating effect between learning intrinsic motivation and attitude. According to research findings, some suggestions such as using e-learning tools were provided for teachers and educators of the cram schools in Taiwan.
Junior high school students’ difficulties of English language learning in the speaking and listening section

This study aims to determine students' problems in learning English, especially in terms of speaking and listening. Basically, the teaching and learning process will run smoothly if students feel comfortable with the learning activities; therefore, this research provides some appropriate suggestions for teachers in overcoming difficulties faced by students. The respondents of this study were junior high school students. This study used a questionnaire as an instrument to determine the extent of students' skills in English. This study also conducted data analysis by analyzing the importance of English in students' views, the difficulties encountered in learning English, and also the teacher's teaching strategies desired by students. The results of this study indicate that most students have some difficulties in speaking due to lack of confidence and skills such as pronunciation, vocabulary, and grammar. Likewise, the difficulty in listening is caused by students’ lack of vocabulary that leads to the difficulty in understanding conversations in English. To overcome this problem, teachers need to have strategies such as identification, diagnosis, prognosis, and assistance or therapy. This research can facilitate the teachers in knowing complaints about English language difficulties experienced by the students.
Pelatihan melalui Web Seminar terkait Publikasi Artikel untuk Menembus Jurnal Sinta 2 dan Scopus

This community service activity aims to provide training for lecturers and students in producing journal articles that can be submitted for publication in the Sinta 2 journal and Scopus journal. This activity was carried out through a web seminar due to the condition of the Covid-19 pandemic which made it impossible to conduct conventional training. Organizers of this activity are Pamulang University and Bina Insani University. This activity was attended by lecturers and students from DKI Jakarta, West Java, Central Java, East Java, Bali and East Kalimantan. The method of carrying out this activity consists of several stages. The first stage is the planning stage through discussion with the organizers to meet the expectations of participants regarding the benefits of this activity. The second stage is the delivery of material and discussion through web seminar via Zoom application and Google Meet. The third stage is further online discussion with participants who need additional explanations. Based on feedback obtained from the participants, this activity is very useful for participants and motivates participants to improve the quality of research articles so that they can be submitted for publication in the Sinta 2 journal and Scopus journal. DOI : https://doi.org/10.26905/abdimas.v5i2.4244
Need-analysis on Importance of Health Education Assessment Module for Lower Primary Teachers in Classroom-based Assessment

This study aims to develop a Health Education assessment module in the classroom for lower primary teachers.The objective of this study is to identify the importance of the assessment module of Health Education in classroom assessment for lower primary teachers.The design used in this study is the design and development research method, which has three phases.The first phase is to analyze the needs, the second phase is design and development, and the third phase is the implementation and assessment of the module.The instrument used in this phase is the questionnaire to identify the importance of assessment material, which is the assessment module for teachers who teach Health Education for lower primary class students in classroom-based assessment.Analysis towards the data of this study was made by using 'SPSS' version 22. Research findings show the importance and the need to develop the assessment module of Health Education for lower primary teachers in conducting the classroom-based assessment.In conclusion, results prove that the Health Education assessment module development is very imperative and essential for teachers in teaching Health Education subject for lower primary students.Not that only, data obtained from the questionnaire show that Health Education teachers felt more comfortable to teach Health Education using the module comparatively as the workload has decreased.The analysis also evidenced that 93.4 percent of teachers think that they can retain the Health Education assessment module as an assessment record.On the whole, this research will be an asset to Malaysia curriculum development.
The Use of Surface Topography for the Identification of Discontinuous Displacements Due to Cracks

The determination of three components of displacements at material surfaces is possible using surface topography information of undeformed (reference) and deformed states. The height digital image correlation (hDIC) technique was developed and demonstrated to achieve micro-level in-plane resolution and nanoscale out-of-plane precision. However, in the original formulation hDIC and other topography-based correlation techniques perform well in the determination of continuous displacements. In the present study of material deformation up to cracking and filan failure, the ability to identify discontinuous triaxial displacements at emerging discontinuities is important. For this purpose, a new method reported herein was developed based on the hDIC technique. The hDIC solution procedure comprises two stages, namely, integer-pixel level correlation and sub-pixel level correlation. In order to predict the displacement and height changes in discontinuous regions, a smoothing stage was inserted between the two main stages. The proposed method determines accurately the discontinuous edges, and the out-of-plane displacements become sharply resolved without any further intervention in the algorithm function. High computational demand required to determine discontinuous displacements using high density topography data was tackled by employing the graphics processing unit (GPU) parallel computing capability with the paging approach. The hDIC technique with GPU parallel computing implementation was applied for the identification of discontinuous edges in an aluminium alloy dog bone test specimen subjected to tensile testing up to failure.