Reviewerly/exHarmony huggingface dataset:
A Research of Feature Matching Algorithm Based on Monocular Vision SLAM

In recent years, with the progress of image processing technology, the research on Simultaneous Localization and Mapping (SLAM) has gradually become a hot topic in the field of computer vision. Visual SLAM is mainly divided into front end and back end. In this study, aiming at the problem of low efficiency of feature point matching, an improved fast feature approximation nearest neighbor feature matching method is proposed, which can obtain higher precision image matching relationship in a short time. At the same time, an algorithm of Cluster Sampling Consensus (CLUSAC) algorithm is proposed, which reduces the probability of mismatching and makes camera pose estimation more accurate. Finally, a comparative experiment of the algorithm is performed. The results show that the improved feature matching algorithm and mismatching elimination algorithm can effectively improve the accuracy of feature matching and help to provide accurate and effective initial values for the back end.
Feature Decomposition and Attention-guided Boundary Refinement for Saliency Detection

In this paper, we propose a novel saliency detection model. We manage to balance the conflict between quality and efficiency by introducing Octave Convolution (OctConv). The proposed detection model is based on a typical encoder- decoder framework, and all encoded side-out features are de- composed into low and high frequency parts, respectively. The low frequency parts locate coarse salient regions, while the high frequency parts help capture edge details. And a two-stream attention module is proposed to emphasize important global and local context. A top-down attention transfers a global saliency prior to shallow layers and a bottom-up attention helps deep layers focus more on the edges. Compared to the state-of-the- art saliency methods, proposed model is of much lighter-weight (68.6 MB), runs faster (63.7 fps in real-time) and achieves competitive performances on six public benchmarks in terms of both quantitative and qualitative evaluation.
Digital Watermarking Hiding Technology for Copyright Information

With the wide application of information technology and the gradual development of army informationization construction, especially with the development of computer network communication technology, data exchange and transmission has become a relatively simple process, people can easily transmit data through various devices; however, the security problem of information transmission has arisen. Digital watermarking technology emerged in the 1990s as an important branch of information hiding technology. It determines the ownership of digital products or verifies the primitiveness of digital information content by embedding perceptible or imperceptible watermarking information in digital products. Firstly, this paper expounds the basic theory of information hiding and related theories. On this basis, it introduces the general classification of digital watermarking technology, and puts forward various forms of digital copyright information.
Mining Retail Telecommunication Data to Predict Profitability

Small or early-stage businesses often require sources of equity, loans, and/or debt funding to support their growth. An important part of the documentation accompanying funding requests can be derived from analytical data associated with the business. There are numerous commercial business intelligence (BI) tools to monitor data and generate business insights. However, most of the retail entrepreneurs still use manual and/or simple techniques, having little time to dedicate to sophisticated BI tools. In this work, we consider how supervised learning models can be used for retail telecommunications businesses. Specifically, we examine how nearest neighbour techniques, feed forward artificial neural networks, Bayesian classifiers, and support vector machines can be used with retail telecommunication data. As indicated by our initial results we have been able to achieve precision of 95.5%, recall of 94.7%, and f-measure of 95.1% which demonstrates that we can categorize retail telecommunication data based on the profitability.
THE EFFECT OF GRAPHIC ORGANIZERS AND READING ATTITUDE ON THE FIFTH GRADE PUPILS’ READING COMPREHENSION OF PALM KIDS ELEMENTARY SCHOOL, PALEMBANG

Abstract The thesis was concerned on the inquiry on the effect of Graphic Organizers and reading attitude on the fifth grade pupils’ reading comprehension of Elementary School Palm Kids, Palembang. The problem of this study was to find out whether or not there was significant effect of Graphic Organizers on the fifth grade pupils’ reading comprehension of Elementary School Palm Kids, Palembang. Graphic Organizers is a visual communication tool that uses visual symbols to express ideas and concepts, to convey meaning. Data collection employed pretest and posttest for experimental and control classes. Data analysis was undertaken by using paired sample t-test, independent sample t-test, and Two-way ANOVA. Findings showed that there was significant effect of Graphic Organizers and reading attitude on the fifth grade pupils’ reading comprehension of Elementary School Palm Kids, Palembang. Conclusion suggests that the alternative hypothesis (Ha) was accepted while the nul hypothesis (Ho) was rejected. It means that Graphic Organizers was effective to improve pupils’ reading comprehension. Keywords: Graphic Organizers, Reading Attitude, and Reading Comprehension
RETRACTED: Evaluation of vertical cooperative algebra connectivity in agricultural production-marketing chain based on control parameters

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. The retraction notice is available at https://doi.org/10.3233/JIFS-219326.
Artificial Intelligence of the Water Supply System of Plant Growing Objects

Abstract The article proposes a solution to the problem of achieving the maximum value of productivity of plant growing objects based on the introduction of artificial intelligence on the basis of neural networks in the water supply system of plants. Various factors affecting crop yields and plant growth are presented. Factors are considered as incoming signals for mathematical neurons of artificial intelligence. A diagram of the artificial intelligence neural network of the plant water supply system, its topology, and the corresponding mathematical model are constructed.
Automatic Focal Blur Segmentation Based on Difference of Blur Feature Using Theoretical Thresholding and Graphcuts

Focal blur segmentation is one of the interesting topics in computer vision. With recent improvements of camera devices, multiple focal blur images of different focal settings can be obtained by a single shooting. Utilizing the information of multiple focal blur images is expected to improve the segmentation performance. We propose one of the automatic focal blur segmentation using a pair of two focal blur images with different focal settings. Difference of blur features can be obtained from an image pair which are focused on an object and background, respectively. A theoretical threshold identifies the object and background in the difference of blur feature space. The proposed method consists of (i) the theoretical thresholding in the blur feature space; and (ii) energy minimization based on Graphcuts using color and blur features. We evaluate the proposed method using 12 and 48 image pairs, including single objects and flowers, respectively. As results of the evaluation, the averaged Informedness of the initial and the final segmentation are 0.897 and 0.972 for the single object images, and 0.730 and 0.827 for the flower images, respectively.
PV system performance evaluation by clustering production data to normal and non-normal operation

The purpose of this study is the development of an algorithmic tool to cluster the points of a scatterplot of "System Yield vs. Reference Yield" of photovoltaic systems, which tends to follow a linear regression. From such clustering one can distinguish data points that comply with the linearity (inliers) and the data points which do not (outliers). The tool then is applied to the scatterplot for two main purposes: a) to detect and exclude any data input anomalies or b) to detect and separate measurements where the PV system is functioning properly from the measurements that show that the photovoltaic (PV) system is malfunctioning.
Application of Network Cloud Resources in College English Listening Class

In the traditional college English listening class, there are few applications of network resources, and the improvement effect of students’ listening level is poor. Therefore, the application of network resources in college English listening class is studied. Select listening teaching materials through Krashen’s input hypothesis. According to the characteristics of the selected listening teaching materials, the listening teaching strategies are established from the perspectives of teachers and students. According to the advantages of network resources, teaching strategies are put into practice, and listening teaching process is designed in three steps: introduction, sorting and output. Realize the application of network resources in college English listening class and complete the application method design. The validity of the design method is demonstrated experimentally. The experimental data show that the design method enables the network resources to be widely used in college English listening classes, which can improve the students’ listening ability by 4% more than the traditional method, and has high effectiveness.
Application of the Japanese Teaching Methods Based on the Network Multimedia

Based on the application of the network multimedia in the Japanese teaching method, under the guidance of the teachers, students can do their own work, search for the information, analyze and summarize, and draw conclusions, which is conducive to the realization of the individualized education in accordance with their aptitude. It can fully reflect the teacher-led and student-centered teaching idea, and promote the reform of the teaching methods and teaching modes. The multimedia teaching is conducive to the cultivation of the basic computer knowledge and the basic operating skills of teachers and students, to improve their qualities and abilities of educational technology, and to meet the requirements of the network information age. The implementation of the network multimedia teaching will also promote the construction of the teaching resources and teaching environment. The application of the multimedia network teaching will lead to the fundamental changes in the teaching process and profound changes in the educational and teaching ideas, concepts and theories.
Continuous Monitoring and Development of an Intelligent Tutoring System through Data Mining

Continuous improvement of an intelligent tutoring system (ITS) involves fine-tuning its workings from time to time. We performed a series of data-driven analyses on student log data from Mindspark to understand usage patterns and identify ways to make the ITS more effective. We found that eighty percent of students did not complete all topics on Mindspark as they spent less than the recommended time on Mindspark and repeated concepts. The repetition of concepts was not because they used Mindspark insincerely. We are making improvements to the system based on the findings.
Ideation Support System with Personalized Knowledge Level Prediction

It is known that computers can improve ideation productivity. For example, conventional systems help users to improve ideations by presenting words that are related to the users' conversational topics. However, these systems usually do not consider the users' vocabulary levels and words that the users do not know may be displayed. This can happen quite often when they are discussing something in their non-native language, or sometimes even in their native language as technical terms may need to be explained in detail. Showing too many unknown words could discourage the users from paying attention to the system and the system would become completely useless. In this research, we introduce a method to estimate the users' vocabulary levels during the ideation process by taking advantage of machine learning techniques. We examine the system usability with a user study and discuss design guidelines for an ideation support system.
Hiding multiple secret information using dynamic image bit manipulation

Abstract In the New Paradigm of Industry 4.0, the main concern of high flow information transmission is security disruption. The transmission process should secure the information transmitted. Especially for secret information found out by nobody. One of the methods used to improved information security is the information hiding process. The hiding technique often applied is Steganography. In this technique, a multimedia data format such as the digital image embeds information. The problem occurs when the secret information is a digital image. Stored an image into another image will cause data distortion and the information will be damaged. These problems can be solved by improving the steganography method for digital images. Improvement of the steganography method in this study uses a bit manipulation technique on the Least Significant Bit (LSB) method. The secret information represented by the digital image will be hidden in a container image. The hiding technique method worked by manipulating the intensity value of the image in the sequence of binary bit format, so the container image intensity value can be used to hide many digital images. The result of this research is a steganography technique allowed to store three images in one image container at one time. The results of the improvement method can protect the intensity values of the container image from some failure. The results of this study also show that data integrity is maintained even though the value of image intensity changed by the bits of hidden images.
An enhanced access control and data security framework for cloud application

Cloud computing is the emergent computing paradigm and is highly in-demand in most of the field. The precise characteristics of cloud computing have an impact on privacy as well as security issues. Numerous cryptographic techniques are utilized to secure data present in the cloud, but still, it faces the concern of security lacking. It is a great challenge to develop a framework to secure data. This proposed scheme presented a framework based on proposed Two-Level Authentication (TLA), Elliptic Curve Integrated Encryption Scheme (ECIES), Advanced Encryption Standard (AES), Covariance Matrix Adaptation Evolution Strategies (CMA-ES), Avalanche effect (AE) and Proof of Retrievability (POR). In TLA, first, check the user's credentials then verify that user using One Time Password (OTP). ECIES with AES symmetric key gives data security with fast execution time. CMA-ES with AE is applied to find the optimized cipher for better security. POR is applied for data integrity and recovery. Proposed work ensures the privacy, integrity, and data security when accessing the cloud.
Enhance the Efficacy of Deep CNN with Auxiliary Labels

Auxiliary attributes can improve the performance of deep CNNs for image classification. However, not all auxiliary tasks are helpful for main task performance. Therefore, in this paper we focus on improving the efficacy of deep CNN models with the support of auxiliary attributes. On the principle of minimizing cross entropy loss, we derive a new algorithm to iteratively train the deep CNN model on target and auxiliary labels. We demonstrate that by introducing auxiliary attributes to training images, uncertainty can be reduced in target classification tasks, and adversarial effects avoided in multi-task formulation. We evaluated our learning approach on three categories of overlapping image sets for identical, partial overlapping, and disjoint situations. We performed three group of experiments on three popular deep CNN networks, and three challenging datasets. The results show that our method is able to improve efficacy for target tasks with auxiliary labels in situations where the multi-task learning fails, or is not applicable.
Multi-sensor information fusion nonlinear filter with multiple packet dropouts

For multi-sensor nonlinear systems with multiple packet dropouts, a weighted measurement fusion Unscented Kalman Filter is proposed. Measurement packet loss or state packet loss at time k will result in the loss of state estimation at time k, so that the Unscented Kalman Filter cannot be obtained at time k+1. In this paper, the state one-step predictor at the previous moment is used to approximate the state estimate of the current moment, and then the corresponding Unscented Kalman Filter is proposed. This filter is suitable for dealing with the packet dropouts problem at any time. In order to improve the filtering accuracy, a weighted observation fusion method is proposed. The example is given to show the effectiveness of the proposed algorithm.
Public Service of Retired Personnel in Colleges and Universities in the Internet Era

At present, the most remarkable characteristics of social development are networking and informatization, which provide a new choice for retirement service in colleges and universities. Colleges and universities are important institutions undertaking the responsibility of old-age service. How to do the old-age service of retired personnel well is of great significance and has a long way to go. Based on the investigation of the lifestyle, material needs as well as spiritual and cultural needs of retired personnel in colleges and universities, this work obtained the results, put forward the countermeasures, and gave full play to the subjective initiative of retired personnel, so as to help the retirement work office of colleges and universities improve the management style and enrich the elderly life, especially to meet the spiritual and cultural needs of retired personnel.
Inferring API Correct Usage Rules: A Tree-based Approach

The lack of knowledge about API correct usage rules is one of the main reasons that APIs are employed incorrectly by programmers, which in some cases lead to serious security vulnerabilities. However, finding a correct usage rule for an API is a time-consuming and error-prone task, particularly in the absence of an API documentation. Existing approaches to extract correct usage rules are mostly based on majority API usages, assuming the correct usage is prevalent. Although statistically extracting API correct usage rules achieves reasonable accuracy, it cannot work correctly in the absence of a fair amount of sample usages. We propose inferring API correct usage rules independent of the number of sample usages by leveraging an API tree structure. In an API tree, each node is an API, and each node's children are APIs called by the parent API. Starting from lower-level APIs, it is possible to infer the correct usage rules for them by utilizing the available correct usage rules of their children. We developed a tool based on our idea for inferring API correct usages rules hierarchically, and have applied it to the source code of Linux kernel v4.3 drivers and found 24 previously reported bugs.
Battery Degradation Prediction Model Based on Gaussian Process Regression

Lithium-ion batteries are increasingly used in today's society because of their excellent performance. In order to predict the fault and remaining life of lithium-ion battery, and provide the basis for the later maintenance guarantee, in this paper, the battery capacity and battery internal resistance are used to establish the health factor of lithium ion battery degradation condition, and the Gaussian process regression is introduced into the prediction to establish a lithium ion battery degradation prediction model. Finally, the rationality of health factor selection is illustrated by simulation experiments, which provides a basis for further evaluation of lithium ion battery health in the later stage.
A Web-based Software Product Line Engineering Framework

Software product line engineering (SPLE) is a paradigm to develop diverse mass products in a single development. It has proven to be more efficient compared to the single product paradigm. We believed by using SPLE as the foundation of a web application framework, development process can be cheaper, faster and better in terms of quality. However, conforming SPLE principle in a web application framework is not a trivial task, there are several problems to solve. In this paper, we described those problems and proposed a framework design to solve several of those problems. We use Abstract Behavioural Specification (ABS) and various SPLE tools to manage implementation variability and generate the end product. Our feasibility study shows the framework can be used for developing a family of web applications in the domain of Adaptive Information System for Charity Organizations (AISCO).
Comparative Experimental Study of Multi Label Classification using Single Label Ground Truth with Application to Field Majoring Problem

Researches on multi label classification methods generally use training data that already have multi label output as ground truth, but there are real-world problems where it is required to produce multi label prediction output but the available training data only have single label as ground truth. This study compared the performance of various multi label classification methods i.e. Ranking Support Vector Machine (Rank-SVM), Backpropagation for Multi Learning (BP-MLL), Multi Label K-Nearest Neighbor (ML-KNN), and Multi Label Radial Basis Function (ML-RBF) that were trained using multi label training data as intended and which were trained using single label training data. The dataset used in this research is an example of real-world problem, namely the personality-aptitude psychological test results is used to predict suitable majors in vocational high school. The results showed that hamming loss between the two was not far adrift so that it can be concluded that in certain problems, multi label classification methods can train single label and still produce multi label predictions with fairly good accuracy.
A new go-to sampler for Bayesian probit regression

This paper introduces an independent-proposal Metropolis–Hastings sampler for Bayesian probit regression. The Gibbs sampler of Albert and Chib has been the default sampler since its introduction in 1993. We conduct a simulation study comparing the two methods under various combinations of predictor variables and sample sizes. The proposed sampler is shown to outperform that of Albert and Chib in terms of efficiency measured through autocorrelation, effective sample size, and computation time. We then demonstrate performance of the samplers on real data applications with analogous results.