Reviewerly/exHarmony huggingface dataset:
Fast Motion Planning via Free C-space Estimation Based on Deep Neural Network

This paper presents a novel learning-based method for fast motion planning in high-dimensional spaces. A deep neural network is designed to predict the free configuration space rapidly given the environment point cloud. With a generated roadmap as an approximate view of the free C-space, LazyPRM is applied to find and check the path with A* search. Due to the application of LazyPRM, the presented method can preserve probabilistic completeness and asymptotic optimality. The new algorithm is tested on a 3-DOF robot arm and a 6-DOF UR3 robot to plan in randomly generated obstacle environments. Results indicate that compared to planners including PRM, RRT*, RRT-connect and the original LazyPRM, our method is of the lowest time consumption and relatively short path length, showing good performance on both planning speed and path quality.
Visual Object Tracking by Using Ranking Loss

This paper introduces a novel deep neural network tracker for robust object tracking. To this end, we employ a ranking loss which provides a fine-tuning of the target object position and returns more precise bounding boxes framing the target object. This is achieved by systematically learning to give higher scores to the candidate regions better framing the target object than the regions that frame the object with less accuracy. As a result, the risk of tracking error accumulation and drifts are largely mitigated, and the object is tracked more successfully. When the proposed network is used with a simple yet effective model update rule, our proposed tracker achieves the state-of-the-art results on all tested challenging tracking datasets. Especially, our results on the OTB (Object Tracking Benchmark) datasets are very promising. The proposed tracker outperforms both deep neural network and correlation filter based trackers, MDNet and ECO, by about 2%, which is a significant improvement over the previous state-of-the-art.
A Two-Level Function Evaluation Management Model for Multi-Population Methods in Dynamic Environments: Hierarchical Learning Automata Approach

The fitness evaluation (FE) management has been successfully applied to improve the performance of multi-population methods for dynamic optimisation problems (DOPs). In this work, we extend one of its variants to address DOPs which was recently proposed by the authors. The aim of our proposal is to increase the efficiency of the FE management. To this end, we propose a technique based on hierarchical learning automata that manages FEs at two level: at first level the algorithm decides which population should be executed, and at the second level it specifies the operation that should be performed by the selected population. A detailed experimental analysis shows the effectiveness of our proposal.
Machine Learning Approaches for Face Identification Feed Forward Algorithms

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Rating Prediction on Movie Recommendation System: Collaborative Filtering Algorithm (CFA) vs. Dissymetrical Percentage Collaborative Filtering Algorithm (DSPCFA)

Recommendation system is one of many solutions for getting information rapidly from the many data available and one of its applications is the movie recommendation system. Movie recommendation system filters information then recommends movies based on rating preferences or user information. One of the most widely used algorithms is the user based collaborative filtering algorithm (CFA) to predict movie ratings which will be recommended based on similarity between target user and other users regardless of common items or the number of movies that have been rated by both. One different approach of the CFA algorithm is a dissymmetrical percentage collaborative filtering algorithm (DSPCFA) that involves common items as a consideration of measuring similarity. This study also uses two similarity measurement methods, namely the pearson correlation similarity method and the cosine similarity method as a comparison to determine the characteristics of each measurement method. The experiment results show that the DSPCFA algorithm produces a lower error value than the CFA algorithm with an error decrease of about 5% for the RMSE evaluation method (Root-mean Squared Error) and an error decrease of about 7% using the MAE (Mean Absolute Error) evaluation method. While measurement method tested shows that the pearson correlation similarity method produces a lower error value than the cosine similarity method.
Robust principal component analysis for compositional tables

A data table arranged according to two factors can often be considered a compositional table. An example is the number of unemployed people, split according to gender and age classes. Analyzed as compositions, the relevant information consists of ratios between different cells of such a table. This is particularly useful when analyzing several compositional tables jointly, where the absolute numbers are in very different ranges, e.g. if unemployment data are considered from different countries. Within the framework of the logratio methodology, compositional tables can be decomposed into independent and interactive parts, and orthonormal coordinates can be assigned to these parts. However, these coordinates usually require some prior knowledge about the data, and they are not easy to handle for exploring the relationships between the given factors. Here we propose a special choice of coordinates with direct relation to centered logratio (clr) coefficients, which are particularly useful for an interpretation in terms of the original cells of the tables. With these coordinates, robust principal component analysis (rPCA) is performed for dimension reduction, allowing to investigate relationships between the factors. The link between orthonormal coordinates and clr coefficients enables to apply rPCA, which would otherwise suffer from the singularity of clr coefficients.
Dynamic Games in Federated Learning Training Service Market

With the great success of deep learning and increasingly powerful mobile devices, federated learning gains growing attentions from both academia and industry. It provides high-quality on-device model training services while preserves data privacy. In this paper, we consider a federated learning training service market which consists of model owners as consumers and mobile device groups as providers. A two-layer dynamic game is formulated to analyze the dynamics of this market. In particular, the service selection processes of model owners are modeled as a lower-level evolutionary game while the pricing strategies of mobile device groups are modeled as a higher-level differential game. The solutions of the dynamic games, i.e., dynamic equilibrium are analyzed theoretically and verified via extensive numerical evaluations.
Fast Laplacian of Gaussian Edge Detection Algorithm for Quantum Images

Quantum image processing, which is the application of quantum theory in image processing, has attracted increasing attention in recent years. In this paper, we propose a framework of Laplacian of Gaussian (LoG) edge detection algorithm for quantum images. The LoG is commonly employed as a second-order edge detector in image processing. Firstly, discrete LoG mask is obtained from the continuous Gaussian operator within certain detection error. Secondly, filter the quantum image with the LoG mask. Thirdly, to obtain the edge image, the searching for zero-crossing point of the filtered image is proposed. Finally, the time complexity is analyzed, which shows that this quantum LoG operator has polynomial-time complexity.
Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

Summary The problem of tracking multiple mobile targets, using a wireless sensor network, is investigated in this paper. We propose a new sensor grouping algorithm, based on the maximum sensor separation distances (G‐MSSD), for estimating the location of multiple indistinguishable targets, either jointly or individually, depending on the distances between the generated groups. The joint tracking algorithm is formulated as a maximum likelihood (ML) estimator and solved through a modified version of the well‐known Gauss‐Newton (MGN) iterative method. We propose two candidate initial guesses for MGN based on G‐MSSD in joint tracking mode, while for the individual mode, the information of each group is used to estimate the location of only the corresponding target. The Cramer‐Rao lower bound (CRLB) for the variance of the proposed ML estimator is derived, and the potential conditions for reducing the CRLB are presented. Since tracking efficiency is affected by poor estimates, we present two criteria to evaluate the quality of estimates and detect the poor ones. An approach is also proposed for correcting the poor estimates, based on additional initial guesses. We demonstrate the effectiveness and accuracy of our proposed dual‐mode algorithm via simulation results and compare our results with the Multi‐Resolution search algorithm.
Non-local Means Denoising Algorithm Based on Local Binary Patterns

Previously, the document flow was mainly through the using of documents in paper form. It created the series of problems during the archiving and searching of the necessary documents. The archive paper documents take a lot of noise points, therefore it is the problem to search of documents in the archive, because there are mistakes in documents, and searching requires a long time. While information technology, it became possible using scanners to convert documents from paper to electronic form. In the process of scanning and due to the fact, that the documents are not always in good shape, the output images are obtained with various defects in the form of noise. Various noise reduction algorithms are used to improve the image quality and remove the noise from scanned documents. This chapter discusses a possibility of using Local Binary Patterns (LBP) operator to make changes into the operation of Non-Local Means (NLM) noise reduction algorithm. As a result, it was possible to improve a quality of scanned images after their processing by the proposed modified algorithm.
Longitudinal Study on Retrospective Assessment of Perceived Usability: A New Method and Perspectives

Abstract This paper reports a longitudinal study on the retrospective evaluation of perceived usability. We propose a retest method based on SUS questionnaires to reduce the multiple-observation effects typically present in longitudinal research. We designed different forms of the SUS questionnaire to eliminate memory effects and distributed duplicate SUS copies to subjects. We first measured the alternative-form reliability of duplicate in Experiment I ($\textbf{r =0.814}$). We then found that the duplicates we designed significantly reduce memory effects compared to the standard SUS questionnaire. Experiment II involved tests on the retrospective evaluation of perceived usability at five measurement points after initial use. The retrospective evaluation of perceived usability changes over time. After 5 min (at point ${\mathrm{T}}_2$), we found that each subject’s retrospective evaluation changed significantly regardless of whether the task had higher or lower usability levels. Moreover, the `directions’of this change tended to differ. This indicates that time is an important factor in perceived usability evaluation. Our results altogether indicate that time affects the retrospective evaluation of perceived usability as well as the accuracy of perceived usability evaluation, which suggests that the process of perceived usability assessment requires strict time constraints.
Evaluation of Mean Absolute Error in Collaborative Filtering for Sparsity Users and Items on Female Daily Network

Female Daily Network is a beauty platform company that has a feature to share women's experiences of beauty items by rating them and talk about that. Female Daily Network doesn't have a recommendation engine for the next user will review the beauty item. Collaborative Filtering is a method to make a recommendation based on users' reviews of items. Sparsity users and sparsity items are problems to make a recommendation engine based on collaborative filtering. Comparing it with cut the sparsity users and items can be used to find the best recommendation engine. In this paper observe the cut of sparsity users and sparsity items to find the best Mean Absolute Error for the recommendation engine. Experimental research shows us that the best minimal times users give rating is 3 and the minimal item rating given is 3 also with Mean Absolute Error Value: 0.548. But MAE results not significant tell us if the cutting data affect the sparsity users and items. Because of the difference MAE, only 0.05 average from other results of the test and also fluctuating.
Rolling Pixels

This article introduces Rolling Pixels, that are essentially robotic Steinmetz solids, for constructing frame-by-frame physical animations. As a bicylinder-shaped Rolling Pixel rolls back and forth or left and right, the shape and color of the top view of the pixel changes repeatedly without using any additional shape- or color-changing techniques. Implemented using off-the-shelf products and technologies, Rolling Pixels are easy-to-build, reproducible, and customizable kinetic design material. We describe the design and implementation of the current prototype of Rolling Pixels. We also illustrate the potential of the Rolling Pixels as building blocks for physical animations through a set of simulated examples.
Image Super-Resolution Reconstruction Algorithm Based on FSRCNN and Residual Network

In order to effectively improve the resolution of the image and restore more edge and texture details, an image super-resolution reconstruction algorithm based on FSRCNN and residual network is proposed. Firstly, a deep channel is constructed, based on the FSRCNN network, a simple residual network is added to estimate the high-frequency information, and the output of FSRCNN network is used as the low-frequency information to be combined with the high-frequency information to obtain a reconstructed image. Then, the information distillation module is fused as a shallow channel to further estimate the edge and texture details. Finally, the outputs of the two channels are combined to obtain the final reconstructed image. Experiments results illustrate that, compared with the FSRCNN approach, the proposed approach has better subjective visual effects corresponding to the reconstruct images on the Set5, Set14 and B100 data sets, and its average PSNR is increased by 0.26dB, 0.18dB and 0.15dB, respectively.
An Object Detection Network for Embedded System

Object detection in images has a wide range of applications in various fields. However, many of the convolutional neural networks recently proposed have higher requirements on computing resources while achieving higher precision, which cannot guarantee good real-time performance on embedded platforms with limited resources. This paper proposed an object detection network suitable for embedded systems. The M-YOLO (Mobile-YOLO) model proposed in this paper combines depthwise separable convolution and residual blocks in feature extraction layers, which helps to reduce the amount of computation of the network. Multi-scale feature fusion is applied to the output layers to improve the accuracy. Experiments show that the M-YOLO model has 9.68M FLOPs (Floating Point Operations), which is about 22% of Tiny-YOLO model. The accuracy of the network reaches 56.61% on the PASCAL VOC dataset, and the speed in ARM is over 3 times faster than Tiny-YOLO model. The network is more suitable for embedded systems.
Relocating Local Outliers Produced by K-means and K-medoids Using Local Outlier Rectifier V.2.0

The extensive growth in the field of information and communication technology allows easy capture of massive amounts of valuable data in different areas. These data are used in various data mining techniques. However, in some cases, the presence of outliers in the dataset exists. One of the categories of an outlier is the local outlier. Local outliers are data points that deviate locally from the cluster center. They occur when the cluster center, known as centroid or medoid, cannot represent all the data members in the cluster. The unrepresented data are mistakenly classified to their closest clusters, making them local outliers. With this, the study aims to address the problem of local outliers produced by K-means and K-medoids. The Local Outlier Rectifier V.2.0 (LOR V.2.0) is a method used to relocate local outliers to their correct clusters. The simulations show that when LOR V.2.0 is partnered with K-means, it was able to relocate 35.37%, 34.78%, 25%, and 12.28% local outliers of Ionosphere, Breast Cancer Wisconsin, Iris, and Breast Cancer Coimbra datasets, respectively. On the contrary, when LOR V.2.0 is partnered with K-medoids, 29.67% of Breast Cancer Wisconsin, 29.11% of Ionosphere, 25.0% of Iris, and 10.34% of Breast Cancer Coimbra local outliers were transferred to their correct clusters. The result also indicates that the method works better when partnered with K-means.
An efficient secret sharing-based storage system for cloud-based IoTs

Internet of Things is the newfound information architecture based on the Internet that develops interactions between objects and services in a secure and reliable environment. As the availability of many smart devices rises, secure and scalable mass storage systems for aggregate data is required in IoTs applications. In this paper, we propose a new method for storing aggregate data in IoTs by use of ( t, n) -threshold secret sharing scheme in the cloud storage. In this method, original data is divided into t blocks that each block is considered as a share. This method is scalable and traceable, i.e., new data can be inserted or part of original data can be deleted, without changing shares, also cloud service providers' fault in sending invalid shares are detectable.
Artificial Intelligence and Deep Learning Application in Evaluating the Descendants of Tubo Mgar Stong Btsan and Social Development

The effective usage of artificial intelligence technology can greatly improve the shortcomings of traditional system. In this paper, the author analyze the artificial intelligence and deep learning application in evaluating the descendants of tubo geer dongzan and social development. On the basis of the introduction of artificial intelligence, BP neural network is used to calculate historical development and Character relationship. The function of this design is that it can improve the complexity of the logic of the client, which can greatly enhance the complex logic computing power. In a word, throughout the whole process of the auxiliary government of the Gaer family, it has experienced the peak period of its rise and development. The Gar Family reached its peak in the era of Gar Zannie and on the Qinling Tomb. However, at this time, it was also the beginning of the decline of the family forces. For the active implementation of the external expansion strategy of Tubo, this paper argues that the geographical and natural factors of the Qinghai-Tibet Plateau, the political system of Tubo at that time and the fierce internal power struggle inevitably prompted Tubo to implement the expansion strategy.
Knowledge Risk Management for State-Owned Enterprises-Indian Scenario

Knowledge risk management (KRM) has emerged as a mechanism for riskRisk assessmentRisk assessment and response associated with organizational knowledge. Evolution of KRM being in its infancy, it was decided to study KRM in state-owned enterprises (SOEs) where the implementation of KM too is rather underdeveloped. Idea was to use SOEs as platforms for KM practice and also study them in the context of KRM. SOEs are important contributors to GDP in countries like India and China, and this study aims to provide an exploratory insight into the KM and KRM practices in SOEs. The approach towards this chapter involved studying literature about KM models in India and elsewhere besides impact study of KRM and amelioration strategies. The chapter introduces new dimensions that ring-fence KM initiatives from the Risks and enhance sustainability of KM by contributing towards converting organizational ‘tacit knowledge’ to ‘explicit knowledge’ with the help of ‘Intra-organizational and Inter-Organizational MentoringInter-Organizational Mentoring as a tool for transfer of knowledge’, ‘Benchmarking for transfer of knowledge about best practices prevailing in different organizations’, ‘Factors influencing KM in cross-organizational projects’ and ‘Employee AlumniEmployee Alumni as a KM tool’.
Liver Segmentation in CT Images Using a Non-Local Fully Convolutional Neural Network

Liver segmentation is a critical step in diagnosing various kinds of hepatic diseases. Based on the segmentation results, physicians can make further assessments more accurately. Although deep learning methods have achieved excellent performance in liver segmentation tasks, the traditional convolution encoder-decoder architecture may easily loss the spatial information due to the stacked convolution and pooling layers. In this paper, we present a non-local spatial feature based neural network (referred as NL-Net) to learn more spatial features of liver for more accurate segmentation. The NL-Net consists of an encoder block, a non-local spatial feature learning block and a decoder block. We utilized the pretrained ResNet model with transfer learning as the encoder. The non-local block can learn long range dependencies of the liver pixel position by computing the response at a position as a weighted sum of the responses at all positions, which can help the network learn more robust features. We applied the proposed model to ISBI 2019 CHAOs liver Segmentation Challenge task and evaluated it on the testing set. Experimental results show that the proposed NL-Net achieved an average dice of 0.972, RAVD of 1.593, ASSD of 1.926 and MSSD of 110.658 on the segmentation results.
Comments on “Provable Multicopy Dynamic Data Possession in Cloud Computing Systems”

Replication is a fundamental solution for the cloud service provider (CSP) to guarantee data availability. To provide users with convincing evidence that the copies required by them are all stored correctly, a number of multi-copy integrity auditing schemes were presented. Recently, Barsoum and Hasan proposed a map-based provable multi-copy dynamic data possession scheme (IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, pp. 485-497, 2015), which was claimed to be secure and can ensure that the CSP possesses all copies required by the contract. However, in this letter, we show that the scheme is easily subject to a copy-summation attack and a single-copy attack, by which a cheating CSP only needs to invest a storage cost of a single copy-while can still pass the verifier's challenge at all times. Therefore, the scheme is no longer secure in this case. Furthermore, we propose some simple but effective countermeasures and give a repaired scheme which is free from the above two attacks.
Estimating Biological Age from Physical Activity using Deep Learning with 3D CNN

We introduce an approach to predict biological age based on a 3-dimensional deep convolutional neural network (3D-CNN), using human physical activity as recorded by a wearable device. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method results in an improved performance. This work has significant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment.