Reviewerly/exHarmony huggingface dataset:
Decision Tree Algorithm Based University Graduate Employment Trend Prediction

The employment situation of college graduates is becoming more and more serious.It is of great significance to find effective methods to predict the employment trend of students.In this study, C4.5 algorithm was used to predict the employment trend of students.Taking the 2016 graduates of Henan University of Animal Husbandry and Economy as examples, four attributes affecting employment units were extracted, the information gain rate was calculated, the decision tree was constructed, and the classification rules were obtained.After data collection, conversion and cleaning, 420 employment records were obtained; 320 records were taken as the training samples.The classification rules were tested using 100 experimental samples, and the accuracy rate was 81%.Finally, the employment trend of the 2018 graduates was predicted by C4.5 algorithm, which provides a theoretical guidance for the arrangement of employment work in schools.Predicting the employment trend of students with decision tree algorithm is feasible and of great significance to the employment guidance of schools and the employment choice of students.
A New Similarity Measure for Document Classification and Text Mining

Accurate, efficient and fast processing of textual data and classification of electronic documents have become an important key factor in knowledge management and related businesses in today’s world. Text mining, information retrieval, and document classification systems have a strong positive impact on digital libraries and electronic content management, e-marketing, electronic archives, customer relationship management, decision support systems, copyright infringement, and plagiarism detection, which strictly affect economics, businesses, and organizations. In this study, we propose a new similarity measure that can be used with k-nearest neighbors (k-NN) and Rocchio algorithms, which are some of the well-known algorithms for document classification, information retrieval, and some other text mining purposes. We have tested our novel similarity measure with some structured textual data sets and we have compared the results with some other standard distance metrics and similarity measures such as Cosine similarity, Euclidean distance, and Pearson correlation coefficient. We have obtained some promising results, which show that this proposed similarity measure could be alternatively used within all suitable algorithms, methods, and models for text mining, document classification, and relevant knowledge management systems.&#x0D; Keywords: text mining, document classification, similarity measures, k-NN, Rocchio algorithm
Two Cycle Learning: Clustering Based Regularisation for Deep Semi-Supervised Classification.

This works addresses the challenge of classification with minimal annotations. Obtaining annotated data is time consuming, expensive and can require expert knowledge. As a result, there is an acceleration towards semi-supervised learning (SSL) approaches which utilise large amounts of unlabelled data to improve classification performance. The vast majority of SSL approaches have focused on implementing the \textit{low-density separation assumption}, in which the idea is that decision boundaries should lie in low density regions. However, they have implemented this assumption by treating the dataset as a set of individual attributes rather than as a global structure, which limits the overall performance of the classifier. Therefore, in this work, we go beyond this implementation and propose a novel SSL framework called two-cycle learning. For the first cycle, we use clustering based regularisation that allows for improved decision boundaries as well as features that generalises well. The second cycle is set as a graph based SSL that take advantages of the richer discriminative features of the first cycle to significantly boost the accuracy of generated pseudo-labels. We evaluate our two-cycle learning method extensively across multiple datasets, outperforming current approaches.
Dynamic Spatio-Temporal Feature Learning via Graph Convolution in 3D Convolutional Networks

Video data owns strong dynamic features in both spatial and temporal domains. In the literature, 3D Convolutional Neural Networks (3D CNNs) serve as a successful technique to simultaneously learn the spatio-temporal features. However, due to the expensive computations, usually the kernel size of the convolutions used in 3D CNNs is rather small, and thus largely limits their learning capability. To address this issue, in this paper we attempt to enhance the learning capability of 3D CNNs for extracting the dynamic features. We capture the long-distance information by modeling the temporal and spatial features as graphs, and then learn the dynamic graph structure information from the feature maps of 3D CNNs. This corresponds to the powerful Graph Convolutional Networks (GCNs), whose adjacent matrix is determined dynamically based on the feature maps. With the learnt dynamic graph, we introduce and fuse a framewise GCN and a channel-wise GCN to enhance the temporal and spatial feature learning of 3D CNNs. Our proposed spatiotemporal graph convolutional network (STGCN) works as a general module that can be embedded into the popular 3D CNNs architectures (e.g., ResNeXt, P3D). Extensive experiments on two video datasets (UCF-101 and HMDB-51) for action recognition task demonstrate the state-of-the-art models with our STGCN module can achieve significant performance improvement.
Recognition and Segmentation of English Long and Short Sentences Based on Machine Translation

With the advent of the information age, long sentences which include many words and have more complex structures.. The translation of long sentences in English-Chinese machine translation has always been the focus of research. In this study, 400 long sentences were randomly selected from NTCIR-9 patent corpus for testing the recognition and segmentation effects of regular match method and error-driven method, and the accuracy rate of the translation was compared on Baidu Online Translation Platform. The results demonstrated that the regular matching method was effective in recognizing and segmenting long sentences, nevertheless there were many defects; the error-driven method was more effective in recognizing and segmenting long sentences; the former increased by 4.8% of the BLEU value of the translated text on Baidu Online Translation Platform and the latter increased by 12.1%, which showed that the error-driven method was more effective in machine translation.
Steganography via E-Books With the EPUB Format by Rearrangements of the Contents of the CSS Files

With the advance of information technology and mobile devices, e-book reading has become a popular way for people to gain knowledge. However, with the widespread uses of digital texts, issues about the copyright protection of e-books have arisen. The electronic publication (EPUB) format is currently one of the most widely used e-book formats. A novel data hiding method for steganographic applications is proposed in this study to hide secret messages into the e-book with the EPUB format. Specifically, the message bits are embedded into the cascading style sheets (CSS) file of the e-book using the lexicographical orders of the selectors and their declarations in the CSS rules. By making use of the fact that the selectors in a CSS file need not all be referred to, fake rules are created artificially, when necessary, to increase the data embedding capacity of the CSS file. The appearance of the e-book does not change after message embedding. Good experimental results and comparisons with other related methods show the feasibility and superiority of the proposed method.
Pengaruh Pendidikan, Pengangguran, dan Kemiskinan terhadap Ketimpangan Pendapatan di Indonesia

This research aims to analyze the effect of education, unemployment, and poverty on income inequality in Indonesia, both partially and simultaneously. This research uses secondary data with a quantitative approach. The type of research used is the type of associative research. The variables in this study are education, unemployment, poverty, and income inequality — data source from BPS and the Ministry of Education and Culture. The data analysis technique used is panel data regression analysis with cross-section 34 provinces and time series for 2015-2018. The results of the research obtained the random effect model, the best models. The results of data analysis show that education and poverty had a partial effect on income inequality in Indonesia, while unemployment had not to affect income inequality. Simultaneously, education, unemployment, and poverty affect income inequality in Indonesia. However, education, unemployment, and poverty can only explain 22.37% of the effect on income inequality in Indonesia. The rest is influenced by factors outside the model.
Geochemical and industrial properties of the Kejal kaolin deposit, NW Iran

The Kejal kaolin deposit, situated in the northwest of Iran, is considered a small part of the Hashtjin hydrothermal zone. The kaolinization process in this deposit has occurred in ignimbritic and volcanic tuff parent rocks in different grades such that severely kaolinized samples can be found in the middle section of the kaolinization profile. Kaolinite is the most abundant clay mineral in the studied samples. Quartz and cristobalite are the main minerals while anatase is the minor mineral of the kaolin samples. Among the major oxides, SiO2, Al2O3, and TiO2 show the highest concentrations in kaolin samples. The mass change calculations based on Ti show enrichment of Si, Al, Sr, and LREEs and depletion of alkali and alkali earth elements, HREEs, and HFSEs. The evaluation of REEs normalized to chondrite represents the higher concentration of LREEs comparing to HREEs. A remarkable positive Gd anomaly is seen in spider diagrams, which can be attributed to the primary CaO in the composition of ignimbritic precursor and also the Gd release due to the decomposition of Gd complexes and adsorption by clay minerals. The Eu negative anomaly is the other obvious characteristic of the studied samples and is most likely related to the alteration of feldspars, decomposition of plagioclase, and Eu liberation from the system. The significant positive correlations between REEs and Al2O3, TiO2, and P2O5 demonstrate the important role of clay minerals, REE-bearing phosphate minerals such as monazite, and titanium minerals like anatase and rutile in the kaolinization process and REE transport and establishment. The performed particle size test based on the hydrometer method shows about 20% of particles
An Efficient Scheme of Cloud Data Assured Deletion

Abstract With the rapid development of cloud storage technology, cloud data assured deletion has received extensive attention. While ensuring the deletion of cloud data, users have also placed increasing demands on cloud data assured deletion, such as improving the execution efficiency of various stages of a cloud data assured deletion system and performing fine-grained access and deletion operations. In this paper, we propose an efficient scheme of cloud data assured deletion. The scheme replaces complicated bilinear pairing with simple scalar multiplication on elliptic curves to realize ciphertext policy attribute-based encryption of cloud data, while solving the security problem of shared data. In addition, the efficiency of encryption and decryption is improved, and fine-grained access of ciphertext is realized. The scheme designs an attribute key management system that employs a dual-server to solve system flaws caused by single point failure. The scheme is proven to be secure, based on the decisional Diffie-Hellman assumption in the standard model; therefore, it has stronger security. The theoretical analysis and experimental results show that the scheme guarantees security and significantly improves the efficiency of each stage of cloud data assured deletion.
Research progress and development trend of cross-layer energy efficiency optimization in data centers

Energy consumption has become a main obstacle hindering the further development of data centers.To solve this problem, many data center energy efficiency optimization studies have emerged in academia and industry in recent years.This review introduces current global research on the data center energy saving problem from the perspective of optimizing cross-layer energy efficiency, including the refrigeration and power supply system energy efficiency optimization based on IT load dispatching as well as unified energy efficiency optimization of IT and refrigeration systems, and then projects trends in these areas.
Reconstructing piecewise planar scenes with multi-view regularization

Abstract Reconstruction of man-made scenes from multi-view images is an important problem in computer vision and computer graphics. Observing that man-made scenes are usually composed of planar surfaces, we encode plane shape prior in reconstructing man-made scenes. Recent approaches for single-view reconstruction employ multi-branch neural networks to simultaneously segment planes and recover 3D plane parameters. However, the scale of available annotated data heavily limits the generalizability and accuracy of these supervised methods. In this paper, we propose multi-view regularization to enhance the capability of piecewise planar reconstruction during the training phase, without demanding extra annotated data. Our multi-view regularization enables the consistency among multiple views by making the feature embedding more robust against view change and lighting variations. Thus, the neural network trained by multi-view regularization performs better on a wide range of views and lightings in the test phase. Based on more consistent prediction results, we merge the recovered models from multiple views to reconstruct scenes. Our approach achieves state-of-the-art reconstruction performance compared to previous approaches on the public ScanNet dataset.
Aspect oriented programming: Concepts, characteristics and implementation

Programming techniques have been passed through many development stages in their progressing path to cope with the increasing complexity of systems requirements. So, one of the main goals of the programming languages designers is how to develop programming language that can handle and manage the spread and overlapping of different functionality concerns. Because unmanageable and uncontrollable scattering of concerns inside the system may cause many problems during system running in present or/and during applying maintenance and developing the system in future. One of the most recent and powerful solutions to overcome these problems is via using Aspect-Oriented Programming (AOP) approach. This research is demonstrates the features and the problems with implying AOP techniques in the software development process.
Field-Aware Knowledge Tracing Machine by Modelling Students' Dynamic Learning Procedure and Item Difficulty

Knowledge tracing is essential for adaptive learning to obtain students' current knowledge states to provide adaptive service. However, the learning process is evolving constantly since students learn and forget over time. Moreover, the difficulty of learning materials can also have huge influence on their performances. This work shows an ongoing project on knowledge tracing by proposing a Field-Aware Knowledge Tracing Machine (FA-KTM) to integrate students' dynamic learning procedure (learning and forgetting) and adaptive item difficulty. We propose methods to model item difficulty and the dynamic learning procedure and present the framework to integrate them together. Preliminary analyses on a dataset show the promising results.
CNN-Based Fast HEVC Quantization Parameter Mode Decision

With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process. The optimal quantization parameter is calculated by comparing the rate distortion cost. In this paper, we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network, which saves video’s encoding time.
Comparison of Optim, Nleqslv and MaxLik to Estimate Parameters in Some of Regression Models

Our main goal in this article is to present the approaches and examples of three functions in R consist of optim, nleqslv and maxLik function to detect the optimization solution of the estimating function in the regression models. We then compare the results with numerous sample sizes (n=150, 300 and 500), the execution time of R code, as well as Normal Q - Q plots of three approaches through some of regression models such as the zero-inflated Binomial (ZIB) regression model, logistic regression model, the zero-inflated Poisson (ZIP) regression model and the zero-inflated Bernoulli (ZIBer) regression model. Finally, we discuss potential research directions in the coming times. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Face Recognition based Feature Extraction using Principal Component Analysis (PCA)

The human face is an entity that has semantic features. Face detection is the first step before face recognition. Face recognition technique is an identification process based on facial features. One feature extraction approach for facial recognition techniques is the Principal Component Analysis (PCA) method. The PCA method is used to simplify facial features and characteristics in order to obtain proportions that are able to represent the characteristics of the original face. The purpose of this research is to construct facial patterns stored in a digital image database. The process of pattern construction and face recognition starts from objects in the form of face images, side detection, pattern construction until it can determine the similarity of face patterns to proceed as face recognition. In this research, a program has been designed to test some samples of face data stored in a digital image database so that it can provide a similarity in the face patterns being observed and its introduction using PCA
Seam Carving Based Image Retargeting: A Survey

Image resizing is a frequently used phenomenon in both image processing applications and adaptation to different aspect ratio. Using conventional resizing approaches like uniform scaling and cropping does not satisfy the needs of visual perfection expectations since they may cause distortions and data or importance loss in foreground objects. To overcome these deficiencies, researchers are looking for content-aware image resizing methods recently. Seam carving is the starring method to serve the purpose. It is a retargeting method which considers the images in a semantic manner for resizing to bring them to target aspect ratio. In this paper, seam carving is searched in detail and the studies in the literature are examined under three groups. Also, some further research topics are mentioned to help researchers who want to work on seam carving.
Real Time Obstacle Estimation Based on Dense Stereo Vision for Robotic Lawn Mowers

Information on obstacles (e.g., distances, scales, and categories) in the unstructured outdoor environment is crucial for a variety of robotic navigation tasks, such as obstacle avoidance, path planning, and security mechanism for pedestrians. However, most of the previous obstacle estimation methods merely focus on the detection of obstacles. In this paper, we propose a novel automatic obstacle estimation system that not only estimates distance and scale information but also can distinguish pedestrians from other barriers. The designed system comprises two branches, namely obstacle estimation and pedestrian detection. Obstacle estimation infers scales and depth clues from the disparity map using stereo vision, while pedestrian detection detects the pedestrian category by machine learning algorithms. We conduct the experiments on the mowing robot platform, and results show that our proposed system is highly effective. The furthest measurable distance is over 10 meters, while the maximum distance error rate is less than 5%. In addition, the average accuracies of the obstacle estimation and pedestrian detection are 94% and 97.6%, respectively.
Influences of Experience and Visual Cues of Virtual Arm on Distance Perception

Egocentric distance perception refers to the perception of distance from a target to a perceiver, which is an important component of visual space perception. It is important to activities in virtual environments and influenced by several factors, such as action capacities and visual cues. However, few studies have investigated such aspects. Hence, Experiments 1 and 2 investigated the effect of using experience and visual cues, respectively, of virtual arms on egocentric distance perception in near and far spaces at equal, prolonged, and shortened lengths of a virtual arm. Results revealed that using experience and visual cues of the virtual arm had a significant effect on egocentric distance perception when the length of virtual arm was equal to the real arm and prolonged but not when shortened. The egocentric distance perception on the conditions of having using experience and virtual arm was most precise. The findings provide implications for the design and implementation of virtual body self-representation in virtual environments.
THE INFLUENCE OF TEXT PREPROCESSING METHODS AND TOOLS ON CALCULATING TEXT SIMILARITY

Text mining to a great extent depends on the various text preprocessing techniques. The preprocessing methods and tools which are used to prepare texts for further mining can be divided into those which are and those which are not language-dependent. The subject matter of this research was the analysis of the inﬂuence of these methods and tools on further text mining. We ﬁrst focused on the analysis of the inﬂuence on the reduction of the vector space model for the multidimensional represen-tation of text documents. We then analyzed the inﬂuence on calculating text similarity, which is the focus of this research. The conclusion we reached is that the implemen-tation of various text preprocessing methods in the Serbian language, which are used for the reduction of the vector space model for the multidimensional representation of text document, achieves the required results. But, the implementation of various text preprocessing methods speciﬁc to the Serbian language for the purpose of calculating text similarity can lead to great diﬀerences in the results.