Reviewerly/exHarmony huggingface dataset:
A Method of Vehicle Fault Diagnosis Supporting Multi-value-chain Collaboration

To improve the collaborative efficiency of multi-value-chain in vehicle maintenance, we designed a case-based vehicle fault diagnosis method by analyzing the traditional diagnosis methods of vehicle fault and collecting user's actual requirements. In this paper, we summarized corpus of vehicle fault descriptions, analyzed user's language features and extracted proper nouns in vehicle field as an extended dictionary to improve the accuracy of text segmentation. We also built a database of vehicle maintenance cases and a vehicle structure tree to support this diagnosis method. Then using vectorization method to process fault description corpus and trained the semantic vectorization model with both statistics and topics. After that, we used vector distance algorithm to compute semantic similarity and return the optimal case in database. Finally, we located the exact position of current fault in vehicle structure tree. The experimental result shows that the accuracy of vehicle fault diagnosis achieved 86.7% by this method. The accuracy of vehicle fault locating is also achieved 81.8%. This case-based vehicle fault diagnosis system can be used for online fault consultation. It can also expand the collaborative business mode of vehicle service value chain and improve the quality and efficiency of vehicle maintenance service.
Hur uppfattar elever omgivningens sociala tryck gällande skolfusk?

Both earlier Swedish research and reports in the Swedish media suggest that students and teachers perceive norm conflicts regarding test-taking and grading: on one hand, high-school-students (year 7 -12) and teachers feel expected to strictly adhere to rules prohibiting cheating in school; on the other hand, they feel expected to apply these rules flexibly to facilitate students achieving good grades. This study aims to address this conflict from student perspective and answer the following question: To what extent do high-school students perceive that social norms support rules against school cheating? The data for this quantitative study was collected from 199 high-school students. Using a norm-sociological model (Svensson, 2008), the data was subsequently analysed to illuminate in what way students understood the social norm against cheating in school and what significance they allocated to these values ​​in their social environments. The results show that the high-school students perceived the social norm against cheating to be significantly stronger than the social norm for cheating. Secondly, the students perceived that teachers, principals and parents clearly viewed cheating negatively, and imbued the no-cheating norm with great importance. Thirdly, the students perceived that their peers were ambivalent to cheating.
Improving Machine Translation Output of German Compound and Multiword Financial Terms

The present application translates German financial terms to the Greek language aiming to maximize the translation quality. The demand for better translation increases the need for more MT systems, journalists or professionals in the banking sector require accurate translations. A novel statistical method of MT is presented, where we use a probability distribution over sentence pairs from a parallel corpus (German-Greek). Both the MT System and the parallel corpus was built from scratch. The present application combines parameters in a new way and estimates the probabilities that extract the best translation.
Person Fall Recognition by using Deep Learning: Convolutional Neural Networks and Image category classification using bag of feature

The rate aging people living alone at home are increasing day by day. Fall is one of the major risks for elderly people. Most of the elder people may get into severe harm to their backbone and that may lead to loss of life. Most of the time in post fall condition the person is lying on ground for many hours once the fall event occurred. This is very significant aspect for person fall detection system to know the seriousness of event. The different techniques are proposed to detect person fall like sensor based, accelerometer and other is camera based. In this paper, two techniques are used to detect person fall such as the deep learning technique convolutional neural networks along with that the image category classification using bad of features is used. The algorithm is providing the promising results as compared to the previously used techniques. The suggested algorithm is described in detail. The algorithm accuracy leads to 100% level on most of the testing measures.
Recognition by Haar-Like Feature Under Different Light Environment Conditions Based on Machine Vision

Haar-like feature is the main way to detect some special features. To deal with Haar-like feature detection under different light environment, some experiments were designed according to illumination condition based on machine vision. During the experiments, face feature was chosen as the represent Haar-like feature on the objects. For the low illumination conditions, Taguchi method was chosen to optimize light parameters. The influence of the design parameters on the objects were separately evaluated by analysis of variance (ANOVA). And the parameters were optimized with desirable performance was designed at last. For the high illumination conditions, the influence degree of average illumination and relative distance and their interaction had indeed a significant effect on face feature recognition. A threshold value to assess the effect of variable on face feature recognition was set and a relation curve was fitted by the threshold value between average illumination and relative distance. Depend on the guidance of this curve, a good recognition effect was obtained. These results have filled the gap in estimating face feature recognition under different light environment and will support the researches of Haar-like feature detection analysis under different light environment.
Lightweight Image Segmentation Based Consensus Mechanism

Consensus mechanism is a fundamental technology of blockchain, ensuring the stability. The most popular consensus mechanism is proof-of-work mechanism. It attracts massive nodes through the distributed network and requires the nodes to generate nonces and hash them to accumulate workload. However, most of the generated nonces and hash values are meaningless and discarded. Such massive quantity of computational power are dedicated for nothing, which proves that the power are not used in an effective way. Thus, this paper proposes a neoteric MDL criterion of image segmentation based on an efficient chain code with Huffman coding and a novel consensus mechanism for blockchain using image segmentation with the proposed MDL criterion as the procedure of accumulating workload and generating nonces. The innovation points of this paper are: (a) it proposes a novel minimum description length model of pictures, which is applied to filtering the best segmentation of a picture; (b) it consummates the consensus mechanism with an image segmentation technology to replace the workload accumulating process, allowing nodes to segment images while mining blocks. The experimental results verified that blockchain nodes can segment images while mining blocks with this novel consensus mechanism, which makes full use of computational power.
Rain Streak Removal with Well-Recovered Moving Objects from Video Sequences Using Photometric Correlation

The main challenge in a rain removal algorithm is to differentiate rain streak from moving objects. This paper addresses this problem using the spatiotemporal appearance technique (STA). Although the STA-based technique can significantly remove rain from video, in some cases it cannot properly retain all the moving object regions. The photometric feature of rain streak was used to solve this issue. In this paper, a new algorithm combining STA and the photometric correlation between rain streak and background is proposed. Rain streak and moving objects were successfully detected and separated by combining both techniques, then fused to obtain well-recovered moving objects with rain-free video. The experimental results reveal that the proposed algorithm significantly outperforms the state-of-the-art methods for both real and synthetic rain streak.
Communication Protocol Between Humans and Bank Server Secure Against Man-in-the-Browser Attacks

In recent years, illegal money transfer using man-in-the-browser (MITB) attacks with malware in internet banking has become a social problem. This study focuses on transaction-tampering MITB attacks and considers countermeasures to combat them. The existing countermeasures require that another device, such as a token device, other than the device used to carry out the actual money-transfer operation is utilized to ensure a secure route different from that carrying out the actual money-transfer operation. However, because MITB attackers have become more sophisticated, malware will be able to infect all the devices in the end and the multiple routes approach will have become ineffective against this type of attack. Therefore, we constructed a human-to-machine communication protocol that requires only one device for each user and is secure against transaction-tampering MITB attacks, even if the device is taken over by malware.
Facial Kinship Verification using Tensor Discriminative Subspace based Color Components

In this work, we propose the evaluation of color spaces of 2D facial images in order to determine the most discriminating color component for kinship facial verification in uncontrolled environments. Using multidimensional facial data modeling, our approach is based on higher order tensors. Multilinear sub-space projection is provided by a new method called Multilinear Side-Information Based Discriminant Analysis (MSIDA). Several inter-connected subspaces are obtained on different tensor modes, so that the subspaces are learned iteratively by unfolding the tensor in the different modes. Using only weak information, MSIDA projects the face tensor into a new subspace in which the discrimination is improved. The input of our system is one of the color spaces RGB, YcbCr and HSV,in which, each component is evaluated separately. The experimental evaluation in the Cornell KinFace database shows that the proposed approach outperforms significantly the most state-of-the-art approaches.
Establishing Total Quality Management System in the Sports Clubs

This study purpose was to investigate the feasibility of establishing total quality management system in some selected sports clubs of Kerman province. This research used descriptive-analytical method. Also, Findings were analyzed using SPSS software, one sample T-test and Friedman test. In addition, staff training index with mean rank (90.4), physical and tangible factors of clubs with mean rank (65.3), staff participation with mean rank (64.3), statistics and information with mean rank (41.3) and responsibility and service index, senior management commitment with mean rank (11.3), respectively, have had the most influences on the feasibility of establishing total quality management in some selected sport clubs of Kerman province. This study results indicated that there was a significant difference between the scores average in all six variables of senior management commitment of the organization as responsibility and service, staff training, staff participation, statistics and information and physical and also tangible factors of the organization. This research results demonstrated that it is possible to establish total quality management in those selected sports clubs of Kerman province in terms of all six indexes, senior management commitment, responsibility and service, staff training, staff participation, statistics and physical and tangible factors.
CHTree: A Technique for Data Clustering to benefit the Reclustering process based on the Huffman Tree

This article propose a novel clustering technique, using Huffman Trees to improve the execution time of reclustering process. The reclustering process is the assignment of data to new clusters when a new number of clusters is needed and occurs after a normal clustering process. The proposed new technique called CHTree (Clustered Huffman Tree) allow sort and group the data in more efficient way, achieving substantial improves in time of reclusterization over the classic clustering methods as K-means, with no major impact in the quality of the resultant clusters. This is achieved since the parameters of internal validation are equals in the most cases and are the same for optimal number of clusters. The experimental results allow to conclude that CHTree reach better clustering time compared with K-means (and its variant K-means++) with minimal impact in terms of separation and compactation in datasets with a gaussian distribution and well separated clusters.
Kampanye Sosial Red Readerhood 2019 Untuk Meningkatkan Minat Baca Anak

Big Bad Wolf Book Sale was first held in Kuala Lumpur, Malaysia, in 2009. seven years later, The Ministry of Culture and Education worked together with PT Jaya Ritel Indonesia to hold Big Bad Wolf Book Sale in Jakarta. Putting forward its vision and mission which is "Read to Enlight Nation", the book sale comes back in 2019 with its social campaign program known as "Red Readerhood". The purpose of the social campaign is to raise literacy by encouraging people to donate books within the sale event. This social campaign program employs Ronald D. Smith's 9 Steps of Strategic Planning to.The Social Campaign Program Red Readerhood as a part of Big Bad Wolf Book Sale consists of three main sub-events: (1) pre-event; (2) event, and (3) post-event. School visitation in the Jabodetabek area involving story-tellers and press conference is the pre-event phase. The phase then continues with Red Readerhood Charity, Partnership, and competitions such as storytelling and Blogger &amp; Vlogger competition. the post-event phase includes Book Charity Report, Radio Interview and Youtube Video Content
호텔기업 온라인 교육 학습자의 개인적 특성과 조직의 사회적 지원이 학습만족도 및 전이성과에 미치는 영향

The purpose of the study was to present a comprehensive view on relationships among employees'' individual factors, organizational support factors, learning satisfaction, and transition effect in a hotel industry. In total, 237 cases were analyzed by deluxe hotels which are located in Seoul. These results are based on research, and the outcomes are as follows: In individual factors, the levels of employees'' motivation were positively related to learning satisfaction and transition effect. However, employees'' career development planning & experiences did not have an effect on learning satisfaction. However, all factors in organizational support for employees'' online program were positively related to learning satisfaction and transition effect. In this case, as organizational efforts are needed to improve the results of online learning program effectively. Hotel companies have to make efforts to make cultures that can recognize employees'' improvement from learning and reward them in various ways. When organizational culture and support can make employees more satisfied about their learning programs and they come to apply learning to their works effectively.
Image Inpainting Exploiting Tensor Train and Total Variation

In this paper, we propose a novel approach to RGB image inpainting, which recovers missing entries of image by using low rank tensor completion. The approach is based on recently proposed tensor train (TT) decomposition, which is used to effectively enforce the low rankness of the image. In addition, our approach exploits the local smooth priors of visual data by incorporating the 2D total variation. Ket augmentation (KA) scheme is used to permute the image to a high order tensor, and then low rankness of balanced KA-TT matrices and total variation (TV) norm constraints are applied to recover the missing entries of the image. In order to reduce the computational complexity, in the proposed approach, nuclear norm is replaced by minimum Frobenius norm of two factorization matrices, which reduces the time for singular value decomposition (SVD). Lastly, in order to solve the proposed model, the efficient alternating direction method of multipliers (ADMM) is developed. The results of image inpainting experiments demonstrate the significantly superior performance of our approach.
Reproducibility of the Neural Vector Space Model via Docker

In this work we describe how Docker images can be used to enhance the reproducibility of Neural IR models. We report our results reproducing the Vector Space Neural Model (NVSM) and we release a CPU-based and a GPU-based Docker image. Finally, we present some insights about reproducing Neural IR models.
Fine-grained Image Caption based on Multi-level Attention

With the continuous development of image caption, the effects of it are getting better. However, there are still problems with the image caption. The complete description should contain different forms of information which include fine-grained information and labels. In order to cope with the above challenges, we propose a fine-grained image caption based on multi-level attention method which can complete label prediction and description of fine-grained images. (1)We firstly use the visual attention mechanism to fuse features of the global and local fine-grained features. (2)The joint attention mechanism is then used to fuse the visual feature and the label features of the image to generate a text description for a specific region of the image. (3)Finally, we use the attention-based Long Short Term Memory (LSTM), a language generation model, to generate fine-grained image caption.
Theoretical basis of machine learning

Learning ability is the basic characteristic of intelligent behavior. With the further development of artificial intelligence (AI), making machines have learning ability has gradually become one of the core contents of artificial intelligence research. Since the first machine learning seminar was held at Carnegie-Mellon University in 1980, the study of machine learning has attracted growing interest from AI researchers. The research into machine learning is based on understanding of the human learning mechanism in physiology and cognitive science. It establishes a computational model or cognitive model of the human learning process, develops various learning theories and methods, studies general learning algorithms, and makes theoretical analysis, and establishes a task-oriented learning system with specific application. It has been applied to various branches of artificial intelligence, such as expert system, automatic reasoning, natural language understanding, pattern recognition, computer vision, intelligent robots, and other fields.
Analysis of Influencing Factors of PV Based Ensemble Modeling for PV Power and Application in Prediction

According to the volatility and intermittent characteristics of photovoltaic power generation. Integrating PV power to the grid have an impact on the stability and safety. To address this challenge, the work learns the effect of support vector machine (SVM) and several algorithms on forecast. An algorithm model for improving the prediction accuracy of training data for multiple groups of factors has been proposed. The model consists of gradient boosting decision tree (GBDT), Particle Swarm Optimization (PSO) and SVM. Finally, according to the integrated algorithm, assigning weak learners' weights and integrating become strong learners. The GBDT algorithm is able to find the factors with high correlation coefficient in the data to establish the model, avoiding of using the empirical method to select the factors. The PSO algorithm finds the optimal parameters of the SVM algorithm and the optimal weight of the weak learner. Compared with BP and traditional SVM, the model established by the data without determining the weather type can obtain better prediction accuracy.
The Initial Stage of Development of a New Computer Program for the Processing of Psychophysiological Tests

Nowadays technologies tend to facilitate not only the learning process itself but also analyzing the results of this process, in order to create a detailed correction plan for every learner. Educational and study processes must be appraising in the terms of the perspectives in diverse electronic technology usage and development tendencies to interest contemporary youth in the learning process. The aim of this study is to depict the meaning of the computer programs in the processing of psychophysiological tests.
Learn Image Object Co-segmentation with Multi-scale Feature Fusion

Image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-the-art co-segmentation methods while the computation cost has been effectively reduced.
Analysis of neural network algorithm in determining high school student department

Abstract In senior high schools, especially in the first class were required to place a department that is in accordance with the value produced. The application predicts student majors based on the value of students using artificial neural network algorithms using rapid miner to be able to produce more precise and faster accuracy results. The results obtained from the analysis carried out obtained an accuracy value of 71.86%.ANN has a network architecture that is a single layer net. Networks that have more than one layer are called multilayer net and competitive layer networks (competitive layer net). The shape of a multilayer net 1 or more has between the input layer and the output layer, which weighs between 2 adjacent layers. ANN architecture using 3 layers is 7 input layers, 6 hidden layers, and 2 output layers. 20 neurons are the number of neuron outputs to artificial neural networks
An Efficient High Utility Pattern Mining for Finding Time Based Customer Purchase Behavior

Utility mining is a prospering trend in data mining area. High utility itemsets are those ones that yield high profit when sold together or alone, in other words groups of items that generate high returns/profits in the customer transaction database. Regard of finding high profitable products, this is necessary to understand the recent preference of the customer. Detecting of HUP in a single phase with customer purchase behavior for a time period is addressed in this paper. In this paper not only consider the individual profit and quantity of each item in a transaction but also consider the customer preference of an item for a given time period. We propose an algorithm and a list structure for finding high utility patterns over large volume of data on the basis of sliding window technique and time cube. The objective is to discover groups of items periodically purchased by customers and generate large profits to the sales revenue.