Reviewerly/exHarmony huggingface dataset:
Improving Efficiency of Machine Learning Model for Bank Customer Data Using Genetic Algorithm Approach

Machine learning techniques are very useful in extracting useful patterns from customer datasets. Although Machine Learning techniques give good results in most of the cases, there is a need to improve the efficiency of ML models in different ways. Feature selection is one of the most important tasks in machine learning. A genetic algorithm is a heuristic method that simulates the selection process. Genetic algorithms come under the category of evolutionary algorithms, which are generally used for generating solutions to optimization problems using selection, crossover, mutation methods. In this paper, we proposed a genetic algorithm-based feature selection model to improve the efficiency of Machine Learning techniques for customer related datasets. We applied a genetic algorithm-based feature selection for two different customer information datasets from UCI repository and achieved good results. All the experiments are implemented in Python language which provides vast packages for machine learning tasks.
Development of Language Capability Assessment Instrument In Writing Descriptive Text Based on Local Wisdom

The purpose of this study is to develop an instrument for assessing written language skills by writing descriptive texts based on local wisdom. The assessment instrument that was developed was in the form of a task that was designed to attract students to be able to speak written language through writing descriptive text. The development model used in this research is the R2D2 development model. The validation results of the validators on the six components that have been developed reached 84% of the validators stated that the assessment instrument that had been developed was feasible to be implemented in learning to write descriptions. The results of the validity and reliability tests show that each item of the task developed is valid and reliable. Besides, 88% of students' responses and impressions stated that they were very interested in the assignment that had been designed and developed in the assessment instrument. Based on the results of validation from experts, validity, reliability, and student responses, it can be concluded that the instrument for assessing the language skills of writing text descriptions based on local wisdom that has been developed is feasible to be implemented in learning to write descriptive texts.
Vertical-Intersection-Based Top-Down Algorithm for Frequent Itemset Mining on MapReduce

Frequent itemset mining (FIM) is a critical problem in association rule mining(ARM), sequence mining, and biomedical text summarization. Apriori-like parallel FIM algorithms scan a database multiple times and generate an excessive number of candidate itemsets for counting their frequencies. It suffers from potential problems such as high I/O traffic and a huge amount of memory usage. Dist-Eclat-like parallel FIM algorithms address the scalability problem. However, they are incapable of constructing an in-memory vertical form to accommodate a large-scale database. This paper proposes a vertical-intersection-based top-down algorithm for frequent itemset mining on MapReduce. The concept of transaction subcube is introduced as all the closed itemsets and their frequencies are derived by employing the vertical intersection method. The frequently closed itemsets are translated into frequent itemsets quickly in the top-down traveling manner with the aid of the negative form of subcubes. Thus, the proposed vertical-intersection-based algorithms have the capability of highly parallel processing, resulting in speeding up the system performance significantly.
Pruning in the Face of Adversaries

The vulnerability of deep neural networks against adversarial examples – inputs with small imperceptible perturbations – has gained a lot of attention in the research community recently. Simultaneously, the number of parameters of state-of-the-art deep learning models has been growing massively, with implications on the memory and computational resources required to train and deploy such models. One approach to control the size of neural networks is retrospectively reducing the number of parameters, so-called neural network pruning. Available research on the impact of neural network pruning on the adversarial robustness is fragmentary and often does not adhere to established principles of robustness evaluation. We close this gap by evaluating the robustness of pruned models against $$\ell ^0$$ , $$\ell ^2$$ , and $$\ell ^\infty $$ -attacks for a wide range of attack strengths, several architectures, data sets, pruning methods, and compression rates. Our results confirm that neural network pruning and adversarial robustness are not mutually exclusive. Instead, sweet spots can be found that are favorable in terms of model size and adversarial robustness. Furthermore, we extend our analysis to situations that incorporate additional assumptions on the adversarial scenario and show that depending on the situation, different strategies are optimal.
Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

Aiming at the impact and disturbance of dual-arm robots in the process of coordinated transportation, a dual-arm cooperative trajectory optimization control based on time-varying constrained output state is proposed. According to the constraint relationship of the end-effector trajectory of the dual-arm coordinated transportation, the joint space trajectory mathematical model of the dual-arm coordinated transportation was established by using the master-slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B-spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time-varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double-arm handling experiment.
Reinforcement learning with constraint based on mirror descent algorithm

An important issue in reinforcement learning is to make the agent avoid the dangers and risks during the task such as physical collisions. We propose the reinforcement learning algorithm based on the CoMirror algorithm, named CoMDS, for the problem that has a functional constraint. Besides, we modify the proposed algorithm CoMDS to Gaussian CoMDS for practical use. We evaluate our algorithms with the via-point task of a planar robotic arm with a forbidden area, that employs as a constraint, in the simulator. As a result, we find that Gaussian CoMDS explores the policy while satisfying the constraint.
Blockchain Technology in Open University Distance Open Education

Abstract For distance education, the application of computer network and software technology is very important to open teaching and management. This paper mainly studies the application of blockchain technology in the field of Open University distance education. Blockchain technology, as a new type of decentralized trust structure technology, provides necessary support for distance open education in teaching resource utilization, teaching process control, teaching effect evaluation and teaching management, and provides an effective foundation for teachers and students to participate in distance teaching practice together. This paper adopts a hierarchical network architecture and hierarchical storage structure, which has good scalability and node transparency, provides support for user service strategy in large-scale applications.
Color Contrast Enhancement on Pap Smear Images Using Statistical Analysis

In the conventional cervix cancer diagnosis, the Pap smear sample images are taken by using a microscope,causing the cells to be hazy and afflicted by unwanted noise. The captured microscopic images of Pap smear may suffer from some defects such as blurring or low contrasts. These problems can hide and obscure the important cervical cell morphologies, leading to the risk of false diagnosis. The quality and contrast of the Pap smear images are the primary keys that could affect the diagnosis' accuracy. The paper's main objective is to propose the best contrast enhancement to eliminate contrast problems in images and correct them in color images to ensure smooth segmentation. In this paper, the median and standard deviation are used for the image's global and local data where the problem region is normalized by using a special proposed formula. The expected resulting image shows only the object (nuclei and cytoplasm), and a background without any noise. The results were compared with CLAHE, HE, and Gray World, and the performance was evaluated based on PSNR, RMSE, and MAE. Proposed method shows higher PSNR and RMSE value while lower value for MAE compared to other methods. This paper's main impact will help doctors in identifying the patient's disease, such as cervical cancer, based on a Pap smear analysis, and increase the accuracy percentages as compared to the conventional method.
The techniques of formalization of OS Astra Linux Special Edition access control model using Event-B formal method for verification using Rodin and ProB

The paper presents techniques to specification access control model of OS Astra Linux Special Edition (the MROSL DP-model) in the formalized notation (formalized using the Event-B formal method), that are based on the use of several global types, separation of general total functions into specific total functions, reduction in the number of invariants and guard of events, which iterate over subsets of a certain set. The result of using these techniques was the simplification of automated deductive verification of formalized notation using the Rodin tool and adaptation of the model to verification by model checking formalized notation using the ProB tool. These techniques can be useful in development of the MROSL DP-model, and also in development of other access control models and verification using appropriate tools.
Improving The Quality Of Illustrations: Transforming Amateur Illustrations To A Professional Standard

We propose an amateur- to professional-level illustration translator that can modify amateur illustrations slightly to produce professional-level quality images. The proposed translator is a GAN-based image translation module. We focus only on the neighboring region of a contour to improve the quality of the illustration by applying image completion to the neighboring region of the extracted line drawing. We artificially augment amateur-level illustrations from professional-level illustrations to solve the lack of a pair of amateur-level and professional-level datasets. This enables us to automatically prepare a pair of amateur-level and professional-level images, through which we can train a translator network. Through experiments and user study, we show that the proposed method improves the quality of amateur illustrations.
Utilizing LOD Relationships and FOAF Vocabularies for Top-N Recommender System

The World Wide Web is transitioning from a Web of hyper-linked documents to a Web of linked data. A large amount of Resource Description Framework (RDF) data was published in publicly available datasets and connected to create the so-called Linked Open Data cloud. The semantics embedded in the Linked Open Data (LOD) can be utilized to enhance the recommender systems (RSs). Limited content analysis, cold-start and data sparsity are well-known issues in traditional RSs, which occurs when few or no features describe the items or no ratings to achieve a recommendation task. Because the LOD cloud contains many features, the knowledge encoded can help resolve this issue. The proposed system's main idea is to design a knowledge-based recommendation system that uses semantic features extracted from multiple datasets in LOD. Our approach will generate recommendations depending on direct and indirect relationships between resources. The results will be ranked according to their similarity score with the input before presenting them to the user. The results of our approach were comparable to the results of the Internet Movie Database (IMDb) website. Furthermore, an experimental evaluation using the MovieLens dataset was conducted. The results were encouraging and stimulating further research in this particular field of study. The usage of different kinds of relations in LOD can enhance the accuracy of the recommendations.
Comparative study on WPF and UWP Frameworks used in RSS Application

In last years, various frameworks for applications development have been released in order to create quick and better solutions. This paper describes and analyzes the performance of two frameworks used for application development, Windows Presentation Foundation (WPF) and Universal Windows Platform (UWP), based on the implementation of a RSS Reader application. Results show that an important factor in choosing the framework is the target device on which the application has to run, and also the type of business the application is used for.
Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning

Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., collision avoidance) with potential uncertainties coming from either disturbed system dynamics or noisy sensor measurements. However, existing motion planning methods cannot efficiently find the robust optimal solutions under general nonlinear and non-convex settings. In this paper, we formulate such problem as chance-constrained Gaussian belief space planning and propose the constrained iterative Linear Quadratic Gaussian (CILQG) algorithm as a real-time solution. In this algorithm, we iteratively calculate a Gaussian approximation of the belief and transform the chance-constraints. We evaluate the effectiveness of our method in simulations of autonomous driving planning tasks with static and dynamic obstacles. Results show that CILQG can handle uncertainties more appropriately and has faster computation time than baseline methods.
Spatiotemporal 2D Skeleton-based Image for Dynamic Gesture Recognition Using Convolutional Neural Networks

This paper presents a dynamic gesture recognition approach using a novel spatiotemporal 2D skeleton image representation that can be fed to computationally efficient deep convolutional neural networks, for applications on human-robot interaction. Gestures are a seamless modality of human interaction and represent a potentially natural way to interact with the smart devices around us, like robots. The contribution of this paper is the proposal of a visually interpretable representation of dynamic gestures, which has a two-fold advantage: (i) conveys both spatial and temporal characteristics relying on a technique inspired in computer graphics, (ii) and can be used with simple and efficient architectures of convolutional neural networks. In our representation, a 3D skeleton model is projected to a 2D camera’s point-of-view, preserving spatial relations, and through a sliding window the temporal domain is encoded in a fused image of consecutive frames, through a shading motion effect achieved by manipulating a transparency coefficient. The result is a 2D image that when fed to simple custom-designed convolutional neural networks, it is achieved accurate classification of dynamic gestures. Experimmental reuslts obtained with a purposely captured 6 gesture dataset of 11 subjects, and also 2 public datasets, give evidence of a strong performance of our approach, when compared to other methods.
STFT-LDA: An algorithm to facilitate the visual analysis of building seismic responses

Civil engineers use numerical simulations of a building’s responses to seismic forces to understand the nature of building failures, the limitations of building codes, and how to determine the latter to prevent the former. Such simulations generate large ensembles of multivariate, multiattribute time series. Comprehensive understanding of this data requires techniques that support the multivariate nature of the time series and can compare behaviors that are both periodic and non-periodic across multiple time scales and multiple time series themselves. In this paper, we present a novel technique to extract such patterns from time series generated from simulations of seismic responses. The core of our approach is the use of topic modeling, where topics correspond to interpretable and discriminative features of the earthquakes. We transform the raw time series data into a time series of topics, and use this visual summary to compare temporal patterns in earthquakes, query earthquakes via the topics across arbitrary time scales, and enable details on demand by linking the topic visualization with the original earthquake data. We show, through a surrogate task and an expert study, that this technique allows analysts to more easily identify recurring patterns in such time series. By integrating this technique in a prototype system, we show how it enables novel forms of visual interaction.
Towards the creation of accessible charts for the visually impaired

Visual representation has relevance in the process of representing data in a summarized way. Graphics are an example of this type of artifact. For this reason, they should be designed considering the most significant number of people, including people with visual disabilities, who, with the help of appropriate technology, can take advantage of their benefits. There are two central problems in this scenario: (i) many times the graphics are not designed adequately for the information that needs to be transmitted, which restricts the accessibility of the graphic; and (ii) even when a graphic is well designed, it may not have the necessary criteria for accessibility, making it impossible for a person with visual impairment or color blindness to understand it. Based on a literature analysis on Information Visualization (IV) and accessibility in charts, we propose a model to create accessible charts, which considers the stages of those two main areas in the design. Three experts in IV and Accessibility evaluated the model that was assessed as pertinent and adequate to this context. With the proposed model, we expect to decrease the number of charts and plots that people with visual disabilities can't interact with, generating a more accessible context.
A Multifeature Complementary Attention Mechanism for Image Topic Representation in Social Networks

Image topic representation in social networks is vital for people to get significant and valuable content. However, this task is difficult and challenging due to the complexity of image features. This paper proposes a multifeature complementary attention mechanism for image topic representation named CATR. CATR uses scene-level and instance-level object detection methods to obtain the object information on social networks. Here, the image features are divided into focused features and unfocused features. Focused features are used to learn and express semantic information, while unfocused features are used to filter out noise information in focused feature extraction. The attention mechanism is constructed by combining the object features and the features of the image itself, while the image topic representation in social networks is realized by the complementary attention mechanism. Based on the real image data of Sina Weibo and Mir-Flickr 25K, several groups of comparative experiments are constructed to verify the performance of the proposed CATR by leveraging different evaluation measures. The experimental results demonstrate that the proposed CATR obtains an optimal accuracy and significantly outperforms the other comparison methods in image topic representation.
An Automatic Calibration Method for Kerf Angle in Wafer Automated Optical Inspection

To improve the accuracy of kerf angle, an automatic calibration method for kerf angle in wafer automated optical inspection is presented. First, the error model of inspection system is established and system angle deviations are calibrated. Next, normalized positioning-based the kerf edges of interest in multiple images are extracted. Then, the coordinate transformation considering the system angle deviation compensation is performed. Finally, the kerf edge line is fitted based on the least squares method to obtain the kerf angle and the kerf angle can be automatically calibrated by rotating the stage. The experimental results show that the kerf angle obtained is relatively stable by coordinate transformation of multiple images to enhance the information of kerf edge and the accuracy of kerf angle can reach within 0.02 degree. Besides, the kerf angle is more sensitive to the system angle deviation and the result is basically a linear increase.
Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.