Reviewerly/exHarmony huggingface dataset:
Research on Visual Perception Technology of Autonomous Driving Based on Improved Convolutional Neural Network

Abstract With the continuous development of the world’s scientific and technological level and the continuous progress of society and economy, driverless cars have become the key research targets of the existing automotive industry. Based on the convolutional neural network theory, this paper redesigned the Faster R-CNN network framework to improve the target recognition accuracy and apply it to the unmanned visual perception system. At the same time, the improved algorithm is used to test the cars under different simulated travel conditions. The computer vision system toolbox of MATLAB was used to verify the effectiveness of the algorithm, and the driving scene designer was used to construct multi-sensor fusion and simulated road scenes, and the corresponding visual control system was designed to effectively realize the automatic code generation. The experimental results show that the average detection accuracy of the improved algorithm is higher, the algorithm is more feasible and robust, and it is helpful to promote the development of driverless cars.
Learning crude oil based on enviromental literacy

Abstract This research is carried out using design-based research methods to develop chemical teaching materials on crude oil based on environmental literacy. The process of forming and processing crude oil, fossil fuels, the quality of gasoline, the impact of burning fossil fuels and alternative fuels is presented in the form of a chemistry magazine oriented aspects of environmental literacy. The magazine presents text, images, graphics, tables, games, comics, poems and videos that are accessed via QR-code. This research uses Design Based Research that adopts the stages of ADDIE (Analysis, Design, Development, Implement, and Evaluations). The results of the validation test show that the magazine made can be declared valid and is suitable for use. It is hoped that teaching materials can be used to foster student awareness of maintaining and improving the environment.
Keeping a secure hold on data through modern electronic content management

Proper storage of data is a security necessity. This is because data can often contain valuable company information, which is liable to fall into the wrong hands if not properly managed. The problem is growing in tandem with increased content creation – the world is full of data. It is estimated that the aggregate amount of data, which doubles in size every two years, measures 4.4 zettabytes (trillion gigabytes) and is likely to reach a massive 44 zettabytes by 2020. 1 Companies are attempting to deal with a tidal wave of data. And a lack of integration inside a business can promote the dangerous phenomenon of content sprawl. This occurs when different departments do not harmonise their processes and there is no plan to address outdated content or to ensure that data is stored in the right way. Companies need to gain a tight rein on their digital assets and institute a rigid content management system to keep up with the data explosion. But that can be easier said than done, explains Paul Hampton of Alfresco.
Approximation Methods for Monte Carlo Tree Search

Today planning algorithms are among the most sought after. One of the main such algorithms is Monte Carlo Tree Search. However, this architecture is complex in terms of parallelization and development. We presented possible approximations for the MCTS algorithm, which allowed us to significantly increase the learning speed of the agent.
Collaborative Filtering Techniques for Trust Prediction in Online Social Network

Collaborative filtering is one of the rapidly grown techniques in Machine learning for the recommendation. Several algorithms based on Collaborative filtering are developed and analysis by researchers in e-commerce and other areas of the business. As data is becoming the next economy of the world, the online social network is now playing a crucial role in various regions of the humankind and organization. It is uttermost important to have a secure and trustworthy Online Social Network. To achieve the goal, the techniques required to predict trustworthy users. Collaborative filtering can be one of the technique to predict the trusted users in the online social network. The study will provide different Collaborative filtering techniques, classify the collaborative filtering techniques, analysis the issue with the techniques, also discuss the use of collaborative filtering in perspective with OSN and compare these algorithms with the real-life dataset.
Off-line handwritten chinese character recognition by using support vector machines

The research proposes an efficient high-accuracy SVM-based handwritten Chinese character recognition system. The 304-dimensional feature vector of a handwritten Chinese character consists of the contour direction feature, the crossing count feature, the peripheral background area feature, and the contour line length feature. The mean-vector recognition method is first used as the coarse classifier to get a small number of candidate classes for the input vector. After the preliminary multi-class SVM-based recognition method is trained by 200 instances per candidate class, the recognition rate for the test handwritten characters of 5,401 classes can achieve 98.31%, which is much higher than about 93% accuracy for the mean-vector recognition method alone. To speed up the recognition, the forward and the backward greedy two-class SVM-based recognition methods are proposed to greatly reduce the recognition time to a practical level.
EVPS: An Automotive Video Acquisition and Processing Platform

This paper describes a versatile and flexible video acquisition and processing platform for automotive. It is designed to meet aggressive requirements in terms of bandwidth and latency when implementing ADAS functions. Based on a Xilinx Ultrascale+ FPGA device, a vision processing pipeline mixing software and hardware tasks is implemented on this platform. This setup is able to collect four automotive camera streams (MIPI CSI2) and process them in the loop before transmitting a more intelligible pre-processed/enhanced data.
Increasing PE Utilization with a SW/HW Co-Design Technique for Sparse Convolutional Neural Networks

Pruning convolution neural networks (CNN) has proved to be an effective technique to decrease the network size without loss of accuracy. By processing the compressed format of the network, the energy consumption can be considerably reduced. However, the existing SIMD-like sparse CNN accelerator suffers from low processing engine (PE) utilization due to the irregular distribution of effectual pairs. In this paper, we address this issue by proposing a software and hardware codesign technique, including a novel data compression scheme and a dedicated module to handle this compressed format. When compared to a state-of-the-art SIMD-like accelerator, the proposed co-design technique can reduce the computation time of conv3, conv4, conv5 of AlexNet by 15%, 33%, 31%.
Video Summarization Based on ListNet Scoring Mechanism

With the increase of video data, we have a new challenge in the browsing, storage and retrieval of video, the presentation of video summarization technology is an effective way to solve such problems. In this paper, the convolutional neural network VGG19 model is used to extract the depth features of video frames on the TVSum50 dataset. The training set is trained by the sorting learning ListNet algorithm to obtain a model that can mark the frames of the test set. According to the scoring result, the top 25 frames in the test set are extracted as key frames, and the video summarization is synthesized. The video frames in digest which is came into being by automatically generated and manual work can use pHash algorithm as assimilation judgment, and get the F1-score of the model. Compared with the current video summarization method, this method is to mark the frame, not the frame set, so the calculation efficiency is reduced, but it can improve accuracy. Through the test on the TVSum50 dataset, the method of this paper is significantly better than other video summarization methods.
Performance and Usability Testing for Online FYP System

Abstract Online management systems have been progressively developed and adopted by most management in higher learning institutions, schools, and offices in the public and private sectors as well as in business management. The developed system can facilitate all process management and thereby benefit many organizations and individuals involved. But before applying a system, usability testing should be made so that all the problems and errors can be identified prior to implementation. This paper analyzes the survey results about the usability of an online management system which is currently being developed: Online Project Evaluation and Supervision System (oPENs). Currently the system has partially developed a few processes in managing FYP such as submission of project approval forms, weekly reporting management process, and group registration process. For the next step, response from system users is needed so that this system can be completed better by fulfilling the features required by all users in addition to identifying problems that could arise before this system is implemented. To identify the system performance and to obtain lecturers’ responses about this system, a usability test is carried out where lecturers need to explore the system practically and answer the questionnaire in the end. There are three usability testing components that have been highlighted in the survey questions i.e. system appearance and interface, user-friendliness and system efficiency. During the system testing, Think Aloud method has been applied whereby the respondents are free to give any comment or suggestion for the system improvement. Most of the respondents strongly agree that this developed system can assist in the FYP supervisory process and also greatly assist coordinators in ensuring the FYP process runs smoothly. There is also a few suggestions that has been listed for the system’s improvement.
Robust correlation filter that combined temporal regularization with spatial weight L1 constraint

Abstract The parameter optimization of the Object tracking algorithms has achieved excellent performance based on parameter regularization. Adaptive spatial regularization correlation filter can obtain the optimal weight coefficient for a specific target. Inspired by this, we propose a filter combined temporal regularity with spatial L1 regularization (LTASRCF). The time regularizer is used to remember the data of the previous frame to update the current target position. It performs well in the process of occlusion, deformation and motion blur. Meanwhile, L1 regularization of spatial weights reduces the computational complexity of the coefficients, and improves the robustness of the spatial weight matrix. We have conducted comparative experiments on the OTB-2015 dataset, and the proposed LTASRCF model ranks first among the five popular algorithms, which outperforms by 9.2% and 18.9% than KCF, respectively, and reaches real-time performance.
Critical Analysis of Cryptographic Algorithms

Security is the most important role in data communication due to the increase of the intruder’s possibility of alteration and eavesdropping to the sensitive data. Hence, cryptography is the fundamental tool used to protect these data. The purpose of using cryptography is to achieve privacy via preventing disclosure of the communication where the data are converted from a readable form into an unreadable form and vice versa. The cryptography algorithm has two types namely: Symmetric key and Asymmetric key. Asymmetric key algorithm mainly invented to overcome the dilemma of key distribution in symmetric key. There are several algorithms classified under these two types based on different parameters. Hence, this variation leads to different levels of performance and efficiency of these algorithms. This paper presents a critical analysis of these systems based on a detailed study and literature review of some cryptography algorithms.
Parameter and Mixture Component Estimation in Spatial Hidden Markov Models: A Comparative Analysis of Computational Methods

Hidden Markov models incorporating the Potts model for the labelling process are an important class of mixture models in spatial statistics. These models have been applied to problems in statistical mechanics, image analysis and disease mapping, among other areas. Jointly estimating the model parameters, the discrete state variables and the number of states (number of mixture components) is recognized as a difficult combinatorial optimization problem. We make comparisons between iterated conditional modes (ICM), simulated annealing (SA) and hybrid ICM with ant colony system (ACS-ICM) optimization for pixel labelling, parameter estimation and mixture component estimation. These comparisons are made for different levels of spatial dependence in the underlying true image. Our studies demonstrate that estimation based on ACS-ICM when carefully tuned exhibits performance that is uniformly superior to both ICM as well as a carefully tuned SA algorithm.
INTERNATIONAL AND NATIONAL STANDARDS AS AN INDICATOR OF LIBRARY ENVIRONMENT TRANSFORMATION

In the international standardization on the direction “documentation and informatics”, including librarianship, an active process of revising standards in force is taking place. In the renovated wordings of international standards, tendencies of document environment evolution reflect themselves through description of partnership modes between libraries and partner organizations, means of supplying users with information products and services. The main trend of international standardization is the revision of outdate standards and the inclusion of objects descriptions and technologies of the electronic environment, renovation of standards in force, development of new documents for innovative directions of library activities – that is what the article is aimed at.
한국어 학습자의 숙달도에 따른 표현 문형 사용 양상 연구

The purpose of this study was to explore the relationship between Korean learners’ use of expressive sentence patterns and their proficiency. A total of 75 learners of Korean as a second language (KSL) participated in the study. The participants were divided into three groups: beginner (N=25), intermediate (N=25), and advanced (N=25). The participants were asked to respond to a Discourse Completion Test (DCT), and to take a C-test on proficiency. The DCT included two questions for each discourse: suggestion, order, request, apology, refusal, and wish scenarios. In order to examine learners’ use of expressive sentence patterns, this study considered both types and tokens. The analysis of Spearman’s Rho revealed a low positive relationship between learners’ use of expressive sentence patterns and their proficiency. Moreover, Fisher''s exact test results showed that there were significant differences among the three groups in the use of expressive sentence patterns for order and refusal. The study showed that learners increased their use of polite expressions in the advanced group, and indicated that proficiency is associated with pragmatic knowledge of expressive sentence patterns.
MEANS OF SCIENTIFIC COMMUNICATION IN FORMING RESEARCH COMPETENCE OF FUTURE PSYCHOLOGIST

The article presents modern means of scientific communication in forming research competence of the future psychologist. Based on the study of modern research approaches to the development of scientific communication, the use of scientific communication in the process of forming the research competence of the future psychologist is presented. On the basis of theoretical study of the material, the research of the development of scientific communication of future applicants of the second (Master’s) level of higher education in the process of the project assignment in the discipline «Professional foreign language communication» is presented. The peculiarities of the formation of research competence and usage of scientific communication by future psychologists are determined. The study of the ability to use scientific communication was performed by the following indicators: 1) the ability to justify the purpose of the research problem, 2) the ability to describe the stages of solving the scientific problem, 3) understanding of research data (digital and visual), 4) ability to substantiate findings from scientific research, 5) ability to present the results of the study, logically justify them. In addition to examining the ability to use scientific communication during project presentation, each student was asked a questionnaire with questions designed to assess his or her personal ability to use scientific communication during project preparation and presentation. As a result of our research, it was determined that future psychologists have no significant experience in scientific communication, in particular in the process of presentation and discussion of scientific research. On the other hand, working in the course of your own research as a form of scientific communication does not cause such extraordinary difficulties for future psychologists.
Fuzzy-Based Methods in Data Analysis with the Focus on Dimensionality Reduction

We analyze a space with a fuzzy partition and show how it determines a measure of closeness. In the space with closeness, we characterize the corresponding Laplace operator and its eigenvectors. The latter serve as projection vectors to reduce the dimension of the original space. We show that the F-transform technique can be naturally explained in the language of dimensionality reduction.
The Development the Knowledge Base of the Question-Answer System Using the Syntagmatic Patterns Method

This paper presents an ontological model of a text document of a large electronic archive. In addition, the article contains original algorithms for extracting syntagmatic patterns from text documents. The paper also describes the developed search algorithms in the knowledge base of the electronic archive using the mechanism of syntagmatic patterns. In conclusion, the results of the experiments on the developed question-answer (QA) system in comparison with existing information systems are presented.
A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials

A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. This invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. The results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.
Integrated Development of Artificial Intelligence and Big Data, with Foreign Language Discipline

Abstract In the new era of rapid change and development, the connotation and denotation of foreign language discipline research is in constant deepening and expansion. Electronic traces, social media, digital texts, spatial location information, and other large-scale data have been extensively applied to the research of foreign language discipline. The new scene of modern foreign language discipline research is worthy of our attention and consideration.
Localized mixture models for prediction with application

This paper explores how localized mixture models can be used for prediction using time series data. The estimation method presented in this study is a kernel-weighted version of an EM-algorithm, where exponential kernels with different bandwidths are used as weight functions. Nadaraya–Watson and local linear estimators are used to carry out localized estimations. Furthermore, in order to demonstrate suitability for prediction at a future time point, a methodology for bandwidth selection and adequate methods are outlined for each model, and then compared with competing forecasting routines. A simulation study is executed to assess the performance of these models for prediction. Furthermore, real data is used to investigate the performance of the localized mixture models for prediction. The data used is predominately taken from the International Energy Agency (IEA).
Evaluation model of knowledge management systems implementation using factor analysis and regresion analysis at the corporation

Abstract The ability to manage knowledge is becoming increasingly important in today’s knowledge economy, especially for companies. In order to obtain the effectiveness of the process of sharing knowledge within the company, it is necessary to evaluate the implementation of the knowledge management system that is currently running. The purpose of knowledge management is to change the paradigm of existing knowledge in employees into knowledge that can explicitly become corporate or organizational knowledge, so that the company can be used to improve employee performance because there are sufficiently managed references or knowledge or experience or expertise that has been collected and disseminated by previous employee.The data obtained were then analyzed using the factor analysis method, and the results that five factors can affect employee performance through knowledge management systems implementation, these are the factors: human resources ability, effective information, knowledge management systems, Quality for Users, accurate information transfer, and adequate IT Infrastructure. Furthermore, from these five factors by using linear regression analysis a mathematical model can be built which can be simulated to illustrate the results of the evaluation.
End-to-End Neural Network for Shadow Detection using Combined Color Spaces

O ver the years, many techniques for shadow detection have been proposed. Howe ver, most of them are computationally expensive and cannot be used in further applications. In this research, a new technique for shadow detection is presented, which is computationally efficient and can be used for further computer vision tasks. Our method uses images converted into different color spaces as input to a framework based on deep learning to detect shadow features from single images in an end to end manner. Our framework observes given image completely to learn and preserve the shadow features in various global and local contexts simultaneously. Our proposed method is trained and assessed on shadow detection benchmark dataset (SBU Shadow Dataset) and performs better than the state-of-the-art convolutional methods for shadow detection as it improves shadow error rate (SER) by 9% while also pointing out some irregularities in the dataset.
Forecasting competition for sales of local and national bottled drinking water in madura using fuzzy sugeno method

Abstract Bottled Drinking Water (AMDK) is processed water, without other food ingredients and food additives, packaged, and safe to drink. AMDK itself which is widely known in the community is national product AMDK. However, local products have emerged to try to compete with national product AMDK. As for local product AMDK which is produced mainly in Madura, the average product is made from Islamic boarding schools. From the level of competition, the idea is emerged for forecasting primarily in the field of sales using the fuzzy Sugeno method. The results obtained from fuzzy Sugeno are turned out to produce a MAPE that the value for local AMDK is 6.4795 and a national AMDK is 2.5353, so that the conclusion about national production is closer to the real value than local AMDK. This study contributes that the predictive value of sales competition is quite accurate based on the MAPE value is below 10. This study provides recommendations to local sellers to make improvements in factors in order to be better than national sellers.