Reviewerly/exHarmony huggingface dataset:
Analisa Kinerja Guru Sekolah di Dinas Pendidikan Dengan Menggunakan Metode Rought Set (Studi Kasus: Dinas Pendidikan Provinsi Sumatera Utara)

The teacher is an important resource in supporting the teaching and learning process. The quality of the teacher needs attention because it determines the quality of the teaching and learning process. Data mining has grown rapidly and added value to the information stored in the database. One fairly simple data mining algorithm is Rough Set. Teacher performance measurement can be done using the rough set method. Aspects or attributes assessed using the rough set method consist of: the process of teaching and learning, guidance and consultation, research and community service, and other tasks outside the main task. The research on teacher performance was conducted at the North Sumatra Provincial Education Office. The rough set method can be used to produce an output in the form of a Teacher's achievement. The purpose of applying this rough set method is to help the management in knowing the possibility of teacher achievement based on teacher data that has been stored so far. The benefit gained is that it can be determined early on that the prospective teacher's achievement is based on the knowledge gained through the rough set method. Tanagra is a data mining software in which several data mining methods are provided ranging from exploring data analysis, machine learning, and databases. Tanagra is an open source project because each researcher can access to the source code, and add his own algorithm, as long as he agrees and complies with the software distribution license.
Parents’ Reflection with a Digital Family Calendar: A Case of Using Scrapbook Calendars in Japanese Families

HCI research is increasingly focused on how technologies can help parents understand children’s needs and state by enabling them to reflect on their children’s activities. Use of a digital family calendar could have great potential in helping facilitate this process. However, relatively few studies have been carried out on this topic. This study investigated (1) how parents reflect on children’s activities with a digital family calendar, (2) how parent-child interaction is like at the time of the reflection, and (3) how the parents’ reflection and the parent-child interaction change over time. A three-month research study was conducted involving nine Japanese families using a digital family scrapbook calendar to share past, current and future events. Based on the results, we have proposed several design suggestions for reflective use of family calendars.
Neuromemetic Evolutionary Optimization

Discrete and combinatorial optimization can be notoriously difficult due to complex and rugged characteristics of the objective function. We address this challenge by mapping the search process to a continuous space using recurrent neural networks. Alongside with an evolutionary run, we learn three mappings: from the original search space to a continuous Cartesian latent space, from that latent space back to the search space, and from the latent space to the search objective. We elicit gradient from that last network and use it to perform moves in the latent space, and apply this Neuromemetic Evolutionary Optimization (NEO) to evolutionary synthesis of programs. Evaluation on a range of benchmarks suggests that NEO significantly outperforms conventional genetic programming.
Single Color Image Dehazing Based on Vese-Osher Model and Dark Channel Prior Algorithm

A new image dehazing method based on Vese-Osher model and dark channel prior algorithm is proposed in this paper. Vese-Osher model is used to decompose a hazy image into texture part and structure part. According to many experiment results, it can be seen that the haze is mainly distributed in structure part. Dark channel prior algorithm is used to dehaze structure part, and then fuse the dehazed structure part and orginal texture part to obtain final haze-free image. The simulation results show that the proposed method could get clear natural images and maintain edges and texture features. The evaluation indicators are also better.
Enhancement of Automated Guided Vehicle System for Heavy Trucks

Current steering automation techniques are well serving most of the commonly used vehicles especially commercial cars, however those vehicles with small steering angle (SSA) such as heavy trucks would be the most difficult to apply these techniques to, as these vehicles have higher tendency to exhibit under-steer on curvature turns of the road. In this paper an automated guided vehicle (AGV) system is suggested to automate specific maneuvers for SSA vehicles, this system is based on conventional kinematics modeling and specially designed maneuvering trajectory for the concerned type of vehicles. Moreover as an application for this methodology, this research presents an automated parking system of SSA vehicle with innovative solution for the stubborn issue of curvy trajectories by converting them mathematically into linear ones. The operation principles of the designed path algorithm are explained with geometric logic interpretation, while output results are depicted using MATLAB simulation to verify the system advantages.
Drawing Computerization and Intelligent Database Building Algorithm

In engineering, manufacturing, electronic industry, transportation and other industries, a large number of paper drawings have been accumulated. These paper drawings are an important resource accumulation of the companies. Paper drawings have some disadvantages, such as inconvenient to save, unable to establish database to realize resource reuse and so on. Therefore, this paper proposes a new algorithm of unattended drawing computerization and database intelligent establishment. Firstly, the position of the key text area is located according to the deep learning object detection algorithm, then the text lines in the text area are detected and the text is recognized. Finally, the database is established according to the recognition results. The experimental results show that the average accuracy of the proposed algorithm is 98.6%. Compared with the existing drawing retrieval algorithm, the algorithm uses deep learning object detection algorithm to initially locate the text area. It can further improve the accuracy of drawing information extraction, and realize the unattended paper drawing computerization and intelligent establishment of database.
Modeling the Probability of a Collision Given the Lateral Offset and Size of a Narrow-Fixed Object

Determining the conditional probability of a crash with a narrow-fixed object (NFO) given an encroachment has occurred, P(C|ENCR) has been computationally burdensome leading to the development and reliance on software by roadside safety engineers. The objective of this study was to develop a simple model to represent P(C|ENCR) when the size of and the offset to an NFO are known. This paper presents a model developed from the field collected data available within the third version of the roadside safety analysis program (RSAPv3) to represent P(C|ENCR) for individual NFOs. This model is an equation which can be applied to NFOs across a variety of diameters and offsets from the travel way. The use of this model will allow for quick computations to determine the probability of a crash with an NFO across a variety of offsets and diameters without the necessity to run software. Models are presented for both heavy vehicles and passenger vehicles. The methodology, datasets, and analysis used are documented. This research led to an improved understanding of the influence of offset and diameter on the probability of a crash. Models such as these reduce the dependence on computationally intense software programs and allow the analysis of safety to be more transparent.
A Natural Language Processing Approach to Represent Maps from Their Description in Natural Language

With the re-emergence of role playing games, interactive adventures, fantasy novels and tabletop games, the storytelling industry has a renewed interest to create engaging stories that require an interactive world-building process, in which the scenario where the story occurs is constructed, establishing the different regions, cultures and people that inhabit that land. This process usually relies on the creation of a map to locate themselves while the story develops. The main objective of this paper is to describe an approach to interpret a textual description of a map written in natural language and extract the main features and elements characterizing that map in order to produce a visual representation of the information provided by a user.
An Automatic Image Segmentation Algorithm Based on Three-Way Decisions

Image segmentation plays an important role in pattern recognition and computer vision. However, several problems not currently addressed affect the performance in segmenting objects from the background. First, color images tend to be divided into small patches, called over-segmentation. Second, most image segmentation algorithms are two-way decisions models, which will cause the inaccurate results when available information is insufficient. To address these problems, a three-way decisions based image segmentation algorithm is proposed in this study. Instead of dividing images into foreground and background as other methods have done, the proposed algorithm enables non-commitment for uncertain pixels and re-grows them using the information from neighboring regions. Due to the capability of reducing uncertainty and imprecision, three-way decisions is an effective method for image segmentation. The experimental results using PASCAL VOC 2007 demonstrate the effectiveness of the proposed algorithm. In addition, adopting the proposed algorithm in applications like CT image analysis results in a significant performance boost.
The squareness errors compensation for Coordinate Measuring Machines based on virtual factory

Faced with fierce competition, the concept of virtual factory comes out. As an important part of virtual factories, Coordinate Measuring Machines' accuracy is important. The paper introduces the efficient and economic method, error compensation, to enhance the Coordinate Measuring Machines precision. As the basic steps of error compensation, the significant error sources-geometric errors are found out; the errors relationship is established and the squareness errors' measurement is analyzed. The experiment is done, which validates the feasibility of the method.
Privacy Preserving Mining System of Association Rules in OpenStack-Based Cloud

As an efficient data analysis tool, data mining can discover the potential association and regularity of massive data, and it has been widely used and played an important role in business decision, medical research and so on. However, the data mining technology is also a double-edged sword, in bringing convenience at the same time, will also cause the user’s privacy leak problem. In order to solve the problem, the symmetric searchable encryption technology is introduced into the association rule mining system to protect the privacy, and privacy preserving mining system of s (PP-MSAR) in OpenStack-based Cloud environment is designed. In order to solve the problem that the existing data mining algorithms can’t deal with large-scale data, this paper uses the computational power of Hadoop platform and add the global pruning technique to the existing algorithm based on MapReduce association rules, so that the counting of frequent item sets get reduced. At the same time, this paper add frequent matrix storage method into the distributed association rules algorithm and realize he algorithm of mining association rules for frequent matrix storage based on MapReduce. In addition, the introduction of symmetric searchable encryption technology to support the cloud server-side ciphertext retrieval, on the one hand to ensure that users stored in the database information will not be leaked to the outside for others, on the other hand also to ensure that the user data for the system staff confidential. Finally, we test the system, and the results show that the system can carry out association rules mining under the premise of protecting user privacy, and provide the correlation degree between data, which has certain practical significance and application value.
Rule-based Expert System for Chinese Patent Law

With the increasing importance of intellectual property in the information age, patent application in China has attracted more and more attention. In this paper, we discuss the development of a rule-based expert system for Chinese patent law by adopting an expert system development software Oracle Policy Automation. The development process is introduced in detail, and the expert system is analyzed. The system covers a great many of the concerns of target users, English speakers filing patents in China. After the development, the system is tested and and suggestions for further improving the patent expert system are put forward.
Vis4Vis: Visualization for (Empirical) Visualization Research

Appropriate evaluation is a key component in visualization research. It is typically based on empirical studies that assess visualization components or complete systems. While such studies often include the user of the visualization, empirical research is not necessarily restricted to user studies but may also address the technical performance of a visualization system such as its computational speed or memory consumption. Any such empirical experiment faces the issue that the underlying visualization is becoming increasingly sophisticated, leading to an increasingly difficult evaluation in complex environments. Therefore, many of the established methods of empirical studies can no longer capture the full complexity of the evaluation. One promising solution is the use of data-rich observations that we can acquire during studies to obtain more reliable interpretations of empirical research. For example, we have been witnessing an increasing availability and use of physiological sensor information from eye tracking, electrodermal activity sensors, electroencephalography, etc. Other examples are various kinds of logs of user activities such as mouse, keyboard, or touch interaction. Such data-rich empirical studies promise to be especially useful for studies in the wild and similar scenarios outside of the controlled laboratory environment. However, with the growing availability of large, complex, time-dependent, heterogeneous, and unstructured observational data, we are facing the new challenge of how we can analyze such data. This challenge can be addressed by establishing the subfield of visualization for visualization (Vis4Vis): visualization as a means of analyzing and communicating data from empirical studies to advance visualization research.
Generating More Effective and Imperceptible Adversarial Text Examples for Sentiment Classification

In this paper, we propose a novel white-box attack against word-level CNN text classifier. On the one hand, we use an Euclidean distance and cosine distance combined metric to find the most semantically similar substitution when generating perturbations, which can effectively increase the attack success rate. We’ve increased global search success rate from 75.8% to 85.8%. On the other hand, we can control the dispersion of the location of the modified words in the adversarial examples by introducing the coefficient of variation(CV) factor, because greedy search sometimes has poor readability for the modified positions in adversarial examples are close. More dispersed modifications can increase human imperceptibility and text readability. We use the attack success rate to evaluate the validity of the attack method, and use CV value to measure the dispersion degree of the modified words in the generated adversarial examples. Finally, we use the combination of these two methods, which can increase the attack success rate and make modification positions in generated examples more dispersed.
Interpreting Word Embeddings Using a Distribution Agnostic Approach Employing Hellinger Distance

Word embeddings can encode semantic and syntactic features and have achieved many recent successes in solving NLP tasks. Despite their successes, it is not trivial to directly extract lexical information out of them. In this paper, we propose a transformation of the embedding space to a more interpretable one using the Hellinger distance. We additionally suggest a distribution-agnostic approach using Kernel Density Estimation. A method is introduced to measure the interpretability of the word embeddings. Our results suggest that Hellinger based calculation gives a 1.35% improvement on average over the Bhattacharyya distance in terms of interpretability and adapts better to unknown words.
Semi-automatic segmentation of knee cartilage in longitudinal MR images by seed transfer

In this paper, we propose an efficient and effective semi-automatic segmentation framework for knee cartilage in longitudinal MR images. The proposed method provides a seamless and efficient combination of initial automatic segmentation and interactive segmentation. The main idea is to generate and transfer the seeds, rather than the labels, of the manual segmentation results of the initial image, to segment sequential images. Here, segmentation seeds are defined as sample voxels that are highly likely to be either the organ of interest or background. Similar to user annotations by scribbles, seeds can be used to determine labels for remaining voxels. Compared to methods that transfer segmentation labels, the proposed method is able to reduce the amount of required user annotations, since it does not require the user to identify and correct erroneous regions in the transferred segmentation labels, while still providing a robust initial segmentation based on transferred initial seeds. We evaluate the effectiveness of the proposed framework by measuring the interaction time of the user to segment the cartilage within the images. We compare the results with previous methods and show that the proposed method reduces the required interaction time.
Research and Application of Image Registration Based on Singular Value Decomposition

Image registration has a wide range of applications in pattern recognition, medical image analysis, image mosaic and other image processing, which is a very important technology in the field of image processing. Aiming at the shortcomings of traditional image registration algorithms in terms of matching accuracy and speed, a fast image registration algorithm based on singular value decomposition (SVD) is proposed by combining point set matching with variable template hierarchical search. Experimental results show that the improved algorithm has obvious advantages in image registration speed compared with the traditional algorithm.
Graphical Representation of Fourier Series from Fourier Transformation

Understanding the derivation process from Fourier's number to Fourier transformation is an important part in the study of Fourier transformation. This paper uses the graphical representation method of mathematics subject knowledge, clarifies the connection between the knowledge points in the process of Fourier's transformation, chooses the appropriate kind of illustration, and represents the derivation process of the Fourier series by illustration, and achieves the effect of image specific and reduces the difficulty of understanding in learning.
Matrix-Type Neural Dynamical Methods for Matrix-Variable Nonlinear Programming with Linear Constraints

Recently, a matrix-type neural dynamical method for matrix-variable nonlinear optimization with box constraints was presented. This paper proposes two matrix-type neural dynamical optimization methods for matrix-variable nonlinear programming with linear constraints. Each matrix-type neural dynamical method consists of continuous-time and discrete-time models. The two continuous-time models significantly generalize two existing vector-type projection neural networks, while the two discrete-time state models have low complexity and can be implemented parallelly by matrix operation. Under proper conditions, the proposed two matrix-type neural dynamical methods are guaranteed to converge globally to the optimal solution. Finally, computed examples show that the proposed matrix-type neural dynamical methods for matrix-variable nonlinear programming with linear constraints are superior to current matrix-type neural dynamical methods in fast computation.
A Statistically Significant Test to Evaluate the Order or Disorder of a Binary String

This paper addresses a basic problem in regard to the analysis of a finite binary string or bit stream (of compact support), namely, how to tell whether the string is representative of non-random or intelligible information (involving some form of periodicity, for example), whether it is the product of an entirely random process or whether it is something in between the two. This problem has applications that include cryptanalysis, quantitative finance, machine learning, artificial intelligence and other forms of signal and image processing involving the general problem of how to distinguishing real noise from information embedded in noise, for example. After providing a short introduction to the problem, we focus on the application of information entropy for solving the problem given that this fundamental metric is an intrinsic measure on information in regard to some measurable system. A brief overview on the concept of entropy is given followed by examples of how algorithms can be design to compute the binary entropy of a finite binary string including important variations on a theme such as the BiEntropy. The problem with computing a single metric of this type is that it can be representative of similar binary strings and lacks robustness in terms of its statistically significance. For this reasons, the paper presents a solution to the problem that is based on the Kullback-Leibler Divergence (or Relative Entropy) which yields a measure of how one probability distribution is different from another reference probability distribution. By repeatedly computing this metric for different reference (simulated or otherwise) random finite binary strings, it is shown how the distribution of the resulting signal changes for intelligible and random binary strings of a finite extent. This allows a number of standard statistical metrics to be computed from which the foundations for a machine learning system can be developed. A limited number of results are present for different natural languages to illustrate the approach, a prototype MATLAB function being provide for interested readers to reproduce the results given as required, investigate different data sets and further develop the method considered.
Design and Implementation of a Solar Tracker System with Dual Axis for Photovoltaic Panels in El Oued Region of Algeria

In this work, a solar tracker system dedicated to the photovoltaic (PV) conversion panels is designed and implemented. This system has dual axis tracker, vertical and horizontal, to continuously tracks the sun from east to west during daytime. The experimental model operation of the device is based on two DC motors controlled by an Arduino card which the signals received from four light sensors called Light Dependent Resistor (LDR). In addition, this system was installed at El Oued region at the south-east of Algeria. Further, this region has a desert climate which it is almost sunny along the year. Experimental results show that the solar tracker system gives good efficiency compared to the fixed one.