Reviewerly/exHarmony huggingface dataset:
The Feasibility Analysis of the Application of Ensemble Learning to Operational Assistant Decision-Making

It is urgent to develop artificial intelligence technology and extend the brain of warship commanders with intelligent machines, thus assisting battlefield situation cognition and decision making. Since the 1990s, ensemble learning has become a new research focus in the field of machine learning. Ensemble learning can improve the generalization ability of the classification algorithms, and achieve the effect of “1 + 1 > 2”. Whether the performance bottleneck of single classifier can be broken by ensemble learning, so as to improve the ability of operational assistant decision of warship? This is a topic worth to discuss. In this paper, we focus on whether the torpedo can find the warship after the warship launched a torpedo decoy, and transform it into the two classification problem in machine learning. First, the classification data set is generated through offline simulation. Secondly, select decision trees as base classification method and Bagging as the typical of ensemble learning, and then the performance improvement of ensemble learning over single classifier is analyzed under ideal conditions. Finally, label noise, small training dataset size is taken into account, and the comparison experiments between ensemble learning and single classifier are performed further. The experimental results verify the feasibility of applying the ensemble learning to operation assistant decision.
Lasso Regression Based Channel Pruning for Efficient Object Detection Model

Deep convolutional neural networks have achieved remarkable performance on object detection tasks. Regression based models include YOLO and SSD are faster and more accurate, but they still run slowly on devices with limited computational and memory resources. Many work focus on network pruning so that they can be effectively deployed. However, most of them require expert knowledge and massive experiments to determine which layers to prune, and how many channels to prune in every single layer. In this paper, we firstly define the importance score of a channel as the product of its L1-norm of weights and scaling factor of batch normalization layer. Then we adopt Lasso regression and fine-grained regularization coefficient to learn sparse scaling factor. By pruning channels with small importance score then retraining, we can get a compact model without significant accuracy drop. Compared to other rule-based pruning method, our method achieves higher accuracy on Pascal VOC2007 dataset while requires less human effort on channel selection.
Applications of Machine Learning for Software Management

Software firms plan all development and management activities strategically to provide the best products and solutions to their user. IT professionals are involved in the process of studying the bugs reported and assign severity to make decisions regarding their resolution. To make the task fast and accurate, developers use automatic methods. Herein, the authors have used feature selection-based classification technique to decide about the severity of reported bugs. TF-IDF feature selection method is used to select the informative terms, determining the severity. Based on selected terms the support vector machine and artificial neural network classifiers are used for classification. A number of performance measures have been used to test the performance of classification. The bug reports of Eclipse project for JDT and platform products were collected from Bugzilla. The results show that classifying bugs on the basis of severity can be effectively improved by feature selection-based strategy.
Investigating ‘One-Day Flies’ Users in The StackOverflow: Why Do and Don’t People Participate?

The paper will mainly investigate factors causing the critical issue of inactive users in one of online community, the Stack Overflow. The focus of inactive user itself will be `one-day flies', those who posted once in Stack Overflow, then never come back. To establish the investigation, the author uses the systematic literature review approach. A new user is a valuable asset for Stack Overflow to keep their pace in the growing area. However, the facts that numerous users from this category become lurkers can be a huge burden for the community. The threat of unanswered questions, negative feedbacks, and deleted questions create a massive discouraging impact for the Stack Overflow, and therefore, raising a question about the health of this community - persistently intimidate new users may lead this giant community to the shrinking phase. However, there is also proves that through the user reputation and badge, some of them has passed the `novice' stage and become actively participate in the community.
Bounded-Error LQR-Trees

We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the constructed policy by minimizing the Bellman Residual, which is estimated in the overlapping regions of LQR controllers. We prove a sample-based upper bound on the true Bellman Residual, and demonstrate a five-fold reduction in cost over previous methods on a simple underactuated nonlinear system.
What Is Cloud Computing?

Cloud computing is a distributed environment for multiple organizations to use remotely and get high scalability, reliability on anytime, anywhere, and pay-as-you-go concepts. An organization has to create data centres to store, manage, and process the information to achieve benefits from data and make decisions. Cloud gives organizations a successful approach that leads to profit without maintaining the cost of data centres and technical staff to manage the services. Cloud has different types of architectures, types of clouds, and cost packages for using the cloud. These services can be scaled up or down when required by an organization. Cloud has unbeatable future because IT world is acquiring it and giving a boost to their businesses. Many cloud providers are using it and the remaining are moving to cloud. Cloud computing also gives birth to edge computing, fog computing, and many more zero downtime solutions.
Models set pre-processing for genetic programming based evolvement of models of models

Abstract We describe in this paper an extension of standard Genetic Programming where the terminal set of the algorithm is expanded with a set of basic models generated offline using a deterministic approach. The new algorithm called EMM-GP (Genetic Programming based Evolvement of Models of Models) uses a specialized mutation operator to sample this set during the recombination phase in order to create models that reuse valuable building blocks. In this work, a preprocessing of model set by means of clustering is considered.
Covariance Matrix Decomposition Using Cascade of Linear Tree Transformations

The tree model can be computed efficiently using the Chow-Liu algorithm to minimize the Kullback-Leibler (KL) divergence. This paper goes beyond tree approximations by systematically forming a cascade of linear transformations where each linear transformation represents a tree structure. The linear transformation is found via a Cholesky factorization to provide sparsity to the inverse covariance matrix. We show that each successive additional cascade linear transformation improves the approximation with respect to the KL divergence. We conclude by showing some simulation results on synthetic data examining the quality of tree and non-tree approximations.
Hate speech in migrant-related cases: Analysis of occurence in Serbian discourse

Hate speech has had a very common occurrence in the Balkan Peninsula, especially during the wars in the former SFRY. At the time, it emerged as a catalyst for violence, intolerance, human mobilization, and raising ethnic and national tensions. In recent times, national tensions have been partially replaced by racist and religious intolerance and disparagement - specifically targeting the lowest values and insisting on diversity by displaying the worst aspects of a culture, or one's descent with the intention of disparaging it, and of its representatives being satanized and stigmatized. The ultimate goal is, again, the mobilization of a certain national feeling, and the routes used towards that goal are most often linked to the electronic and virtual environment. Special analysis presented in this paper was carried out on several websites and web pages, most often news or media coverage, regarding the tendency of reporting - ways of using certain terms in order to reach precisely defined and targeted social groups and individuals. In addition to the analysis of the websites, there was a special analysis of the comments of visitors to such websites and the methods used in addressing the public. Directed influence on public opinion creation is indicated and methods of influencing website users are presented.
Trunk Detection and Tree Disparity Calculation in Uncontrolled Environments

Computer vision is an area have proven to play an essential role in urban and rural applications like medical, agriculture, and remote sensing. The use of image processing methods for simulating the visual capability of robots plays a crucial role in the consolidation of smart farming. The understanding of the complexity of outdoor environments, where the robot performs its task, is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination, unpredictable weather conditions, and different color shades. In this study, we present a new method to detect and segment tree trunks from unstructured environments where natural properties such as lighting and terrain shape form a variety of non-controlled conditions. We prepared a dataset with stereo image pairs and ground truth maps to calculate disparities and to evaluate the proposed method in the application of smart farming. The results show that the presented approach can segment trees with high precision, which is an important step in calculating the disparity of external components by systems that use the stereoscopic view.
A Novel Procedure to Speed up the Transcription of Historical Handwritten Documents by Interleaving Keyword Spotting and user Validation

We propose a novel procedure to speed-up the content transcription of handwritten documents in digital historical archives when a keyword spotting system is used for the purpose. Instead of performing the validation of the system outputs in a single step, as it is customary, the proposed methodology envisaged a multi-step validation process to be embedded into a human-in-the-loop approach. At each step, the system outputs are validated and, whenever an image word that does not correspond to any entry of the keyword list is mistakenly returned by the system, its correct transcription is entered and used to query the system in the next step. The performance of our approach has been experimentally evaluated in terms of the total time to achieve the complete transcription of a subset of documents from the Bentham dataset. The results confirm that interleaving keyword spotting by the system and validation by the user leads to a significant reduction of the time required to transcribe the document content with respect to both the manual transcription and the traditional end-of-the-loop validation process.
Integrating Fluid Simulation with Virtual Die Casting Machine for Industry 4.0 and Operator Training

High Pressure Die CastingDie Casting is a complex manufacturing process in which molten metal is forced into a mold cavity under high pressure where it then cools and solidifies. An interactive virtual trainingTraining simulator is being developed which integrates fluid flow simulations with die castingDie Casting machine parameters to improve operator understanding of the process and ultimately improve casting quality. Methods for combining fluid simulationSimulation results with tools from the game industry for virtual realityVirtual reality and augmented reality applications in industry 4.0 Industry 4.0 are discussed.
Recent Developments in Pedestrian Detection Using Deep Learning

Pedestrian Detection is utilized in various applications such as driverless cars, traffic control system, etc. In this research work, four recent research works' performances are analysed with several other research works. The first work is Parts and Context network, second is Scale Aware Fast R-CNN, third work is Extended Filtered Channel Framework, and the fourth is See Extensively while Focusing on Core Area (ExtAtt). All these four research works are described in detail. Further, the performances of these four research works are analysed using three datasets, KITTI, Caltech, and INRIA. Average Precision in percentage is used for KITTI dataset and Log average miss rate in percentage is used for Caltech and INRIA datasets, as evaluation metrics. These values are compared with over 20 other research works previously performed on the same datasets. Among all the pedestrian detection models considered, ExtAtt achieves the highest average precision with KITTI dataset and PCN achieves the lowest log average miss rate on Caltech and INRIA datasets.
Interactive Multi-model Tracking of a Highly Maneuvering Target using MSPDAF with Least Squares Virtual Fusion

For target cognition and data fusion, the sensors are usually considered as synchronous sampling. This assumption is often no longer valid as the data rate of infrared sensors is significantly higher than that of radar. In this paper, a novel algorithm named LS IMM/MSPDAF is proposed for the least squares (LS) virtual fusion method by involving time calibration due to multi-sensors asynchronous sampling. As for the interactive multi-model (IMM) part, it is designed for fusing multimodal observations in order to track a highly maneuvering target with different dimensions. Experimental results of two case studies have demonstrated that our algorithm can conquer the defect of asynchronous sampling, and achieve improvement in the accuracy of track estimation than MSPDAF.
Research on Data Compression Algorithm of Heating Data Center Based on Adaptive Swinging Door Trending

The 21st century is the century full of information. The data compression is an indispensable key technology in the information society. We use adaptive swinging door trending in the historical data compression of the heating data center. The experimental results show that the algorithm can compress the historical data faster, obtain a higher compression ratio, and ensure that the decompressed data has higher accuracy.
Writing for publication as innovative work behavior: An argument for supporting undergraduate student authors

New Horizons in Adult Education and Human Resource DevelopmentVolume 32, Issue 1 p. 62-66 Writer's Forum Writing for publication as innovative work behavior: An argument for supporting undergraduate student authors Maria S. Plakhotnik, Corresponding Author Maria S. Plakhotnik mplakhotnik@hse.ru National Research University Higher School of Economics, RussiaCorresponding Author: Maria S. Plakhotnik, Department of Management, National Research University Higher School of Economics – Saint Petersburg Kantemyrovskaya, 3A, Saint Petersburg, Russia Email: mplakhotnik@hse.ruSearch for more papers by this author Maria S. Plakhotnik, Corresponding Author Maria S. Plakhotnik mplakhotnik@hse.ru National Research University Higher School of Economics, RussiaCorresponding Author: Maria S. Plakhotnik, Department of Management, National Research University Higher School of Economics – Saint Petersburg Kantemyrovskaya, 3A, Saint Petersburg, Russia Email: mplakhotnik@hse.ruSearch for more papers by this author First published: 29 January 2020 https://doi.org/10.1002/nha3.20275Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume32, Issue1Winter 2020Pages 62-66 RelatedInformation
Developing a Mobile Application for an Abdominal Exercise Machine Using an Accelerometer

This paper proposes a method of developing a mobile application for an abdominal exercise machine. Currently, inexpensive exercise machines, which are sold internationally, do not give feedback to users about the workout they engage in. Therefore, it would be useful to develop an app that could monitor physical activity on low-cost exercise machines. An additional benefit of using smartphones is that the exercise data can be cast to larger TV screens and monitors. Leg raise and lower cycles, as well as the number of back raise and lower cycles, were determined from angles measured by smartphones equipped with accelerometers connected to the bars of an abdominal exercise machine. In addition, an Android application for abdominal exercise machines was created based on the method. The app allows users to monitor their abdominal exercises by setting parameters related to raising and lowering their legs and the backs of their bodies. The results of physical activity were displayed on a smartphone screen and spoken using Text to Speech technology.
Users Preferences Regarding Types of Help: Different Contexts Comparison

Despite the significant advance in usability and intuitiveness of software interfaces, users still use help systems to seek solutions concerning doubts, whether they are beginners or advanced in the use of the software. Therefore, entirely intuitive, self-communicating interfaces that, regardless of task and user, do not require help systems, still do not exist. This study, as an exploratory type, aimed to verify if users refer to types of help significantly at different levels of intensity and preference when compared an initial user contact with software to a specific moment of doubt concerning any tool or task after being familiar. Thus, a user preference study was conducted by applying a questionnaire to 104 students of Software Engineering undergraduate course at the University of Brasília, Brazil. After that, the results from the two contexts were compared. Analysis of the data suggested that users refer more to types of help at an initial contact with software, and usually, they do not search directly for help documentation, referring more to search engines.
Crawler Technology Based on Scrapy Framework

Abstract With the development of the times and the popularization of scientific and technological products, the Internet has become inseparable from our lives, and search engines have become a daily necessity of people.In view of the growing needs 。This topic requires the design of a prototype crawler system based on Scrapy framework. The specific requirements and contents are as follows: analyzing the structure and rules of the target website, looking for data items that need to be crawled. Based on Scrappy framework, a crawler prototype program is implemented by customizing crawling rules.Select the appropriate database for data access and analysis.
A Survey on Multi-objective based clustering techniques for solving real life problems

Clustering is a popular data mining technique which can be applied to a given data set to identify the data objects that belong to a single class, such that data objects in different clusters are distinct while similarity exists for data objects belonging to the same cluster. Usually, clustering techniques are based on optimizing single objective function criteria, which may not be capable of performing well in many real time scenarios. Motivated by this many multi-objective based optimization techniques are discussed in this paper. Multi-objective based optimization techniques are capable of optimizing several conflicting objective functions simultaneously. Under this context, evolutionary based approach and simulated annealing based techniques are adopted in various MOO techniques and proven well in case of noise, non-spherical and high dimensional feature space. The paper further discusses various validity measures to evaluate the goodness of clustering techniques.
Collaborative Convolution AutoEncoder for Recommendation Systems

Against traditional collaborative filtering algorithm often face the problems of high sparsity and low recommendation accuracy, we propose collaborative convolution autoencoder algorithm CCAE, which replaces the matrix decomposition training method in the collaborative filtering algorithm with the convolution autoencoder method. First, the input data drop sampling through the convolution layer and the downsampling layer to learn its efficient compression characteristics, then the data reconstruct through the deconvolution layer and the upsampling layer, and calculate the score ranking for recommendation. Experimental results show that the algorithm CCAE achieves lower RMSE than those based on autoencoder AE, stack denoising autoencoder SDAE and collaborative filtering matrix decomposition MF on movie-lens. Therefore, CCAE algorithm can effectively solve data sparsity and improve recommendation accuracy.
Finite Inverted Dirichlet Mixture Optimal Pixel Predictor

Image pixel prediction is a very important task in image processing. To model images, many researchers use Gaussian distributions. However, some data are obviously nonGaussian, such as the image clutter and texture. In such cases, predictors are hard to derive and to obtain. In this paper, we analytically derive a new non-linear predictor based on inverted Dirichlet mixture. The non-linear combination of the neighbouring pixels and the combination of the mixture parameters demonstrate a good efficiency in predicting pixels. In order to prove the efficacy of our predictor, we use two challenging tasks, which are; object detection and image restoration.
The combination of term relations analysis and weighted frequent itemset model for multidocument summarization

Abstract Nowadays, it is necessary that users have access to information in a concise form without losing any critical information. Document summarization is an automatic process of generating a short form from a document. In itemset‐based document summarization, the weights of all terms are considered the same. In this paper, a new approach is proposed for multidocument summarization based on weighted patterns and term association measures. In the present study, the weights of the terms are not equal in the context and are computed based on weighted frequent itemset mining. Indeed, the proposed method enriches frequent itemset mining by weighting the terms in the corpus. In addition, the relationships among the terms in the corpus have been considered using term association measures. Also, the statistical features such as sentence length and sentence position have been modified and matched to generate a summary based on the greedy method. Based on the results of the DUC 2002 and DUC 2004 datasets obtained by the ROUGE toolkit, the proposed approach can outperform the state‐of‐the‐art approaches significantly.
Corrections to “Deep Neural Networks With Random Gaussian Weights: A Universal Classification Strategy?” [Jul 1, 2016 3444-3457]

Following a comment correspondence paper, we agree that there is a mistake in one of the formulas in the paper “Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?” We show that this error only impacts one claim in the original paper.