Reviewerly/exHarmony huggingface dataset:
Large population sizes and crossover help in dynamic environments

Abstract Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the $$(1+1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -Evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for dynamic binval , the extreme form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for $$(\mu +1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -EA is not close the optimum, but far away from it. In contrast, for the $$(\mu +1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -GA, the region around the optimum is the hardest region in all studied cases.Kindly check and confirm the inserted city name is correctly identified.Correct.
Measuring Traffic Volumes Using an Autoencoder with No Need to Tag Images with Labels

Almost all vision technologies that are used to measure traffic volume use a two-step procedure that involves tracking and detecting. Object detection algorithms such as YOLO and Fast-RCNN have been successfully applied to detecting vehicles. The tracking of vehicles requires an additional algorithm that can trace the vehicles that appear in a previous video frame to their appearance in a subsequent frame. This two-step algorithm prevails in the field but requires substantial computation resources for training, testing, and evaluation. The present study devised a simpler algorithm based on an autoencoder that requires no labeled data for training. An autoencoder was trained on the pixel intensities of a virtual line placed on images in an unsupervised manner. The last hidden node of the former encoding portion of the autoencoder generates a scalar signal that can be used to judge whether a vehicle is passing. A cycle-consistent generative adversarial network (CycleGAN) was used to transform an original input photo of complex vehicle images and backgrounds into a simple illustration input image that enhances the performance of the autoencoder in judging the presence of a vehicle. The proposed model is much lighter and faster than a YOLO-based model, and accuracy of the proposed model is equivalent to, or better than, a YOLO-based model. In measuring traffic volumes, the proposed approach turned out to be robust in terms of both accuracy and efficiency.
A Suggested Method For Analyzing Ciphertexts Using Shimada's Scheme With Different Parameters

In this paper, we deal with specific cases in which we can analyze the code of Shimada based on the status of sending the same plaintext from more than one user with different public keys.Wewere able to analyze this codewith the help of the Chinese remainder theorem.
An Intelligent Book Sorting Management System

At present, the book management mode of most libraries in our country is still in the manual management stage, which is low efficiency, waste of manpower and easy to make mistakes. In this paper, an intelligent book sorting system is designed to solve these problems. The system is applied to small and medium-sized libraries. It involves technologies of color recognition sensor, single-chip control, human-computer interaction, etc. It can realize books classification automatically and accurately, and no manual operation is required except for placing books, which is very convenient for library management. The results provided in this paper proved that this system is accuracy, stability and scalability, which can meet the need of library daily management work.
Adaptive estimation in the supremum norm for semiparametric mixtures of regressions

We investigate a flexible two-component semiparametric mixture of regressions model, in which one of the conditional component distributions of the response given the covariate is unknown but assumed symmetric about a location parameter, while the other is specified up to a scale parameter.The location and scale parameters together with the proportion are allowed to depend nonparametrically on covariates.After settling identifiability, we provide local M-estimators for these parameters which converge in the sup-norm at the optimal rates over Hölder-smoothness classes.We also introduce an adaptive version of the estimators based on the Lepskimethod.Sup-norm bounds show that the local M-estimator properly estimates the functions globally, and are the first step in the construction of useful inferential tools such as confidence bands.In our analysis we develop general results about rates of convergence in the sup-norm as well as adaptive estimation of local M-estimators which might be of some independent interest, and which can also be applied in various other settings.We investigate the finite-sample behaviour of our method in a simulation study, and give an illustration to a real data set from bioinformatics.
An automatic update framework for fault diagnosis machine on BOG compressor

Abstract The fault diagnosis onBoil-off Gas(BOG) compressor is necessary for the safety and economic benefits in operation. This paper proposed an automatic update framework for diagnosis machine on BOG compressor. Data discretization is applied to transform data through expert experience to Bayesian network. This framework is able to update diagnosis models from expert experience provided by operators.
Reconfigurable Minimum-Time Autonomous Marine Vehicle Guidance in Variable Sea Currents

In this chapter, we present an approach of reconfigurable minimum-time guidance of autonomous marine vehicles moving in variable sea currents. Our approach aims at suboptimality in the minimum-time travel between two points within a sea area, compensating for environmental uncertainties. Real-time reactive revisions of ongoing guidance followed by tracking controls are the key features of our reconfigurable approach. By its reconfigurable nature, our approach achieves suboptimality rather than optimality. As the basic tool for achieving minimum-time travel, a globally working numerical procedure deriving the solution of an optimal heading guidance law is presented. The developed solution procedure derives optimal reference headings that achieve minimum-time travel of a marine vehicle in any deterministic sea currents including uncertainties, whether stationary or time varying. Pursuing suboptimality, our approach is robust to environmental uncertainties compared to others seeking rigorous optimality. As well as minimizes the traveling time, our suboptimal approach works as a fail-safe or fault-tolerable strategy for its optimal counterpart, under the condition of environmental uncertainties. The efficacy of our approach is validated by simulated vehicle routings in variable sea currents.
INTERACTIVE TECHNOLOGIES IN TEACHING MATHEMATICS IN PRIMARY SCHOOL: FROM PLANNING TO RESULTS

The article states the author's definition of the concept interactive technologies at mathematics lessons. They are defined as the set and the certain sequence of pedagogical methods that are aimed at the realization of a didactic objective, the activation of the participants’ subject-subject interaction in the educational process, which affects the quality of mathematics preparation and enhances pupil’ thinking skills. The structure of interactive mathematics lesson at primary school is determined: motivation activity; announcement, presentation of the topic and learning outcomes; providing the necessary information; interactive activity; reflection; evaluating the results of the lesson. Interactive technologies are selected and classified according to their didactic aim and function and are appropriate for use at mathematics lessons of the New Ukrainian School.
Mechanical Engineering and AI- Adapting to the New World

The field of mechanical engineering is not only by definition extremely broad but it is also constantly increasing in scope due to technological advancements. While there are many interesting developments worth investigating, this paper highlights the cross-disciplinary potential blockchain-powered digital twins bring to a variety of industries as well as their future potential.
Local directional intensity pattern for facial expression recognition

This paper presents a new face descriptor, local directional intensity pattern (LDIP), for human facial expression recognition. Considering the edge information and pixel intensity changes of face images, local features extracted from not only frequency domain but also spatial domain are encoded in a compact way, which makes features complementary to each other and much more discriminative. Additionally, the histogram refinement further reduces the feature dimensions while enhancing the performance of the texture descriptor. Extensive experiments are conducted to verify the effectiveness of our proposed method. The experimental results demonstrate that the LDIP is a discriminative and efficient facial expression descriptor, which achieves higher recognition rate than other descriptors.
Havana's StreetNet: Innovation from necessity

In Havana, technology enthusiasts have designed StreetNet, a community network that serves as an alternative to the worldwide web. This article describes the deeply relational practices that go toward the maintenance of StreetNet, highlighting elements of struggle that accompany innovative strategies that result from necessity.
Likelihood-based Sensor Fusion in Radar/Infrared System Using Distributed Particle Filter

In this paper, the distributed data fusion problem in radar/infrared system which is composed of radar and infrared, is considered. Generally, the different dimensions of local measurements and the strong nonlinearity of infrared measurement equation are two major issues in radar/infrared system. For these issues, a parameterized likelihood-based distributed particle filter (P-L-DPF) algorithm is used, where the local likelihood function (rather than posterior or measurement) is regraded as the filtering results since the likelihood function can preserve the most original measurements information. Meantime, we approximate the likelihood function using polynomial expansion, and transmit polynomial coefficients to the fusion center, which efficiently reduces the transmission requirements. In the simulation, an example that a radar/infrared system tracks a moving target is given, the results show that the tracking performance of the P-L-DPF algorithm outperforms the posterior-based DPF (P-DPF) algorithm and is very close to the measurement-based centralized particle filter (M -CPF) algorithm.
Spatial Feature Collaborative Network for Trademark Image Retrieval

Trademark is the mark of the brand and widely used in various types of advertising media and e-commerce sales. As the number of trademarks grows at high speed, efficient trademark image retrieval and management become more and more critical. Traditional trademark retrieval models introduce low-level visual, textual, and labels information for retrieval, but are of relatively poor performance. Recently, benefitting from the Deep Learning model, content-based image retrieval makes excellent progress. Typically, the trademark image contains sufficient semantic visual information. In this paper, we proposed a model named Spatial Feature Collaborative Network (SFCN) for trademarks image retrieval. SFCN fuses AlexNet and Faster R-CNN to extract global and region features, which are used for global and local collaborative retrieval. We conducted extensive experiments on a specific trademark dataset, and the experimental results show that our proposed method achieves better performance than other baseline models. Moreover, our proposed framework has been running in a real-world environment.
An Efficient Image Compression Technique for Big Data Based on Deep Convolutional Autoencoders

Big organizations and enterprises store a large amount of data and images comprise a huge portion of it. Thus, compression of image data becomes important to remove redundancy as well as to make file size manageable. Autoencoders can be used for image compression in such scenarios. Deep autoencoders train on a large set of images to compress them and represent using fewer codes. In this paper, we build deep convolutional autoencoders and use them to reduce dimensionality of images present in the CIFAR-10 dataset and MNIST dataset. We train the autoencoder using commonly used loss functions and quantize images to achieve a superior identification performance. The compressed image vectors are later decompressed to obtain a near approximation of the original images.
Effects of Code Cloning in Mobile Applications

Developers usually copy and paste code during software development that results in similar patches of code spread in different files. Such type of code clones cause difficulty in making changes and result in bugs. In this study, we want to analyze the extent of the use of code cloning in the development of mobile applications and their effect on post-release bugs. This information should help developers and designers to develop and design better applications and software that present their results to the best advantage.
Investigation of Different Time-Series Segmented Windows from Inertial Sensor for Field Hockey Activity Recognition

Sport activity recognition has become one of the primary sport performance analysis contributions as it offers a notational data for tactical analysis and planning. In developing of activity recognition algorithm, searching for the best window segmentation size within time-series data is one of the parameters that contribute to the performance of algorithm in term of the accuracy rate. Yet, previous studies on activity recognition have implemented a different fixed size of window in segmenting the specific activities and the performance of activity recognition algorithm along with the changes of window size is still uncertain. Thus, this study is investigating the performance of field hockey activity recognition algorithm based on inertial sensor time-series data with different window segmentation size. The study was conducted on 11 subjects who worn the inertial sensors on their chest and waist while performing the six common field hockey activities which are passing, drive, drag flick, dribbling, receiving and tackles. The performance of each size of windows were observed and evaluated by using Cubic support vector machine (SVM). Among the different window sizes studied, this study found 1.5 s and 2.0 s are among the top in producing high accuracy rate for recognizing the field hockey activity that represent the movement from chest and waist with 89.6% and 91.4% accuracy respectively.
Underwater Video Compression Using Threshold Value Provided by Optimizing Algorithms

The underwater video has many applications such as real-time environment monitoring, supervisory control, and inspection in the offshore oilfield environment, marine defense application, etc. Underground videos are affected by poor lighting, low contrast, and color variations. In this paper, we compare the performance of threshold value provided by optimization techniques such as PSO, DPSO, and FODPSO on underground video frames for background and foreground estimation. The segmented video frame is subjected to further compression using H.264 to estimate motion compensation. FODPSO outperforms other techniques as it provides good PSNR value for underground videos.
User-friendly Technology at PT PLN (Persero) Pekanbaru: OE Analysis on Procurement of Goods and Services

Abstract Nowadays, the development is more likely to have user-friendly technology, which means that it is easier for someone to do any activity. PT PLN (Persero) Pekanbaru utilizes information technology in the field of procurement of goods and services. The initial stage in the procurement of goods and services is to make an Owner Estimate (OE) analysis. In determining OE, procurement executives must have price references that can be obtained from market prices. The prototype method is a new paradigm in software development. In the prototype/ prototyping method, the resulting software is then presented to the client and the client is given the opportunity to provide input and criticism. The performance achievement of procurement of goods and services in semester 2 of 2017 for OE manufacturing activities at PT PLN (Persero) Pekanbaru is equal to 116% of the 100% target. After the implementation of the OE application in the first semester of 2018 was carried out, the performance of the procurement of goods and services in OE manufacturing activities increases to 120% of the 100% target. By the implementation of the OE application, the performance of the procurement of goods and services has increased from 116% to 120% of the target in OE manufacturing activities.
Likelihood Adjustment Based on Informativeness of Observation for Extended Object Tracking

In this paper, we consider the problem of extended object tracking when a few measurements are available. For automotive safety, it is important to estimate the state of a vehicle, that is, position, velocity, and vehicle's extent, using multiple measurements from unique objects. Many observation models of extended object tracking for the vehicle tracking problem have been proposed, but they have paid less attention to a number of measurements that can degrade the likelihood of target. We propose a novel likelihood adjustment method based on the informativeness of observation, which corresponds to the number of observed points and their formation, in a context of extended object tracking. Numerical simulations with a simplified but essential model figure performance of the proposed method by comparing with conventional methods.
Cryptanalysis of Proxy Re-Encryption with Keyword Search Scheme in Cloud Computing

In this work, we review the article published by Tang et al. IN CMC 2018. We show that their construction of proxy re-encryption with keyword search exists some errors and it does not hold the correctness requirement. We point the problems out and proposed a new protocol.
Towards a Domain Specific Modeling Tool for Home Automation Systems

There is a steadily growing market for applications enabling home automation system connectivity to control various applications and home appliances for managing and security purposes. Development of Home Automation (HA) systems involves a lot of complexity resulting from the heterogeneous nature of the system and its need to be able to provide data security and authenticity. The software development life cycle can be shortened if there exists a solution that enables a suitable transformation from design to crude implementation. There are less known integrated framework for HA system development from model to implementation. This solution can be obtained through Model-driven Development. This paper aims to propose a Model-driven Development based solution which models the Home automation domain using meta-modeling technique and a platform-independent Domain-specific Modelling Language (DSML) which can be used for developing platform dependant DSML to achieve automated code generation using a model. The DSML proposed has been validated with the help of a proof of concept, using case studies. This approach can shorten the development duration and more emphasis can be given for the better design of the system.
The Journey to the Future of Social, Organizational, and Individual Performance: A Migration From How to What to Why

Performance ImprovementVolume 59, Issue 2-4 p. 6-11 Feature Article The Journey to the Future of Social, Organizational, and Individual Performance: A Migration From How to What to Why Roger Kaufman PhD, CPT, Corresponding Author Roger Kaufman PhD, CPT roger@megaplanning.com Correspondence Roger Kaufman Email: roger@megaplanning.comSearch for more papers by this author Roger Kaufman PhD, CPT, Corresponding Author Roger Kaufman PhD, CPT roger@megaplanning.com Correspondence Roger Kaufman Email: roger@megaplanning.comSearch for more papers by this author First published: 23 April 2020 https://doi.org/10.1002/pfi.21910 Correction added on May 21, 2020 after first online publication: slight modifications from initial online publication Read 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 Volume59, Issue2-4February-April 2020Pages 6-11 RelatedInformation
Multi-Target Tracking based on the EK-GLMB Filter in 3D Using Active Radar with Velocity Measurement

The measurement collected by 3D (3-dimensional) active radar is usually a non-linear polar coordinate measurement and capture true targets and false clutter information. Furthermore, the true targets are usually unknown and time-varying and the correlation between targets and measurements is uncertain. Hence, multi-target tracking in 3D is extremely challenging. In this paper, we firstly establish 3D polar coordinate measurement model. Specifically, for the radar which can measure line-of-sight (LOS) rate and target Doppler information, we build 3D velocity-measurement model by modifying the former. Then, combined with the non-linear measurement model, a tracking algorithm based on the extended Kalman (EK) generalized labeled multi-Bernoulli (GLMB) filter is proposed for multitarget tracking in 3D. Finally, numerical results demonstrate the validity of the proposed method in 3D using active radar in the presence of detection uncertainty, false observations, and noise.