Reviewerly/exHarmony huggingface dataset:
РАЗРАБОТКА И ОПТИМИЗАЦИЯ АЛГОРИТМА СЛЕЖЕНИЯ ЗА ТРАНСПОРТНЫМ СРЕДСТВОМ В ВИДЕОПОТОКЕ НА ОСНОВЕ ПИРАМИДАЛЬНОГО МЕТОДА ЛУКАСА – КАНАДЕ

The paper considers the task of traffic flow analysis when a stationary video sensor observes a road section. An algorithm for tracking vehicles moving along road lanes is presented. This algorithm includes the stage of extracting feature points of the object part located in a bounded area of the image. Object area extraction is based on preliminary segmentation results and information about scene geometry. The second stage of the algorithm uses the Lucas–Kanade method with pyramids to track the vehicle. The positive features of this method for optical flow calculation are considered. Approaches to optimizing computational costs in optical flow calculations are proposed. Information about movement direction and speed of the vehicle is used for this purpose. The developed tracking algorithm has been tested during experimental studies. The studies were carried out using real videos оn which vehicle movement on several road lanes is observed. The results of experimental studies have shown the high performance of the proposed algorithm in comparison with the basic algorithm for moving object detection without further tracking. As a result, the number of false detections and omissions of vehicles decreased. The percentage of classification errors also decreased which indicates an increase in the accuracy of estimating the length and speed of detected vehicles.
Research Progress on an Intelligent Production and Distribution of the Remote Sensing Products

Since the amount of remote sensing data that can be obtained every day has reached ten terabytes, the storage and sharing of massive remote sensing data and the construction of product production application architecture have become imminent research topics. The purpose of this study is to build an efficient system that can meet the needs of massive remote sensing data storage, calculation, and distribution. The article elaborates on the latest research progress of cloud service remote sensing data distribution and intelligent production from the four aspects of remote sensing cloud service development, data distribution technology, cloud service technology, and operation mechanism of remote sensing cloud computing processing model, and proposes cloud service remote sensing Key issues facing data distribution, and summarize and prospect related research, with a view to promoting the further development of research on cloud service remote sensing data distribution and intelligent production.
Digital Design of Bronze Based on Characteristic Outline Recognition

Analyze and study the key technology of characteristic profile recognition. Based on computer-aided technology and basic theories of graphics, construct the contour corner points with the local maximum value of the curvature of the target contour curve, the characteristic curve segment is used to express the outline curve, based on the characteristic curve segment, the curve chord length method is used to match the segmented contour lines, match and splice surface characteristic curves. Finally, the process and method for recognition the characteristic contour lines were established, and put forward the optimal design idea of curvature, used to repair the characteristic curve of the decoration. The experimental results show that the outline of the target object can be quickly obtained. Through the case analysis of the identification and extraction of bronze feature contour lines, it proves that this method has the characteristics of fast, intelligent and automatic in the identification and optimization of the characteristic curve.
Security model in XSA

This chapter explains the Cloud Foundry security model using the User Account and Authorization service (UAA) and how SAP HANA XSA implements it. The OAuth protocol and its vital role within XSA are part of this chapter. HDI containers along with cross-schema access on classic objects are explained along with other development artifacts such as synonyms, and grants are addressed as they are vital to the cross-schema development scenarios that are present in any enterprise who is ready to face the implementation of development in an XSA environment.
Bayesian Nonparametric Estimation with Shape Constraints

This thesis deals with a number of statistical problems where either censoring or shape-constraints play a role. These problems have mostly been treated from a frequentist statistical perspective. Over the past decades, the approach to statistics has gained popularity and this is the approach that is adopted in this thesis. We consider nonparametric statistical models, i.e. models indexed by a parameter that is not of finite dimension. For three different models we investigate the asymptotic properties of the posterior distribution under a frequentist setup. We derive either posterior consistency or posterior contraction rat es. Such results are relevant, as these provides a frequentist justification of using point estimators derived from the posterior. Besides theoretical results, we develop computational methods for obtaining draws from the posterior. Overall, this work is at the intersection of the research areas estimation under shape constraints and censoring, Bayesian nonparametrics and Bayesian computation.
The Effect of Feature Reduction in Click Fraud Detection: Review

It is almost impossible for online activities being without fraud.Online ads face a major threat represents by fake clicks which happen because of bots or some mischievous people.Several studies have solved the problem using machine learning algorithms.Some of them have solved only the problem of automatic click fraud (which carried out using bot), to classify physical or bot click.While many recent researches have detected click fraud problem in spite of clicks type.This paper presents a survey of methods used to detect fraud clicks on ads.It presents advantages, as well as disadvantages of each method, in general, Most recent studies in this field, have focused on features preprocessing before classification, because of the problems' type which imposed existence many related features and this may lead to overfitting.So the solution is applying dimensional reduction algorithms, to get better results and avoid overfitting.
Cascading Failure Analysis of Urban Rail Transit Network Based on Coupled Map Lattice Model

The theoretical method of complex network is used to analyze the cascading failure of urban rail transit network. Taking Shanghai rail transit as an example, L-space and P-space models are constructed. The coupled map lattice model used to simulate the cascading failure of the rail transit network separately analyzes the propagation speed and scale of cascading failure at different times as well as disturbance amplitudes and coupling strength under random attack and deliberate attack. The results show that the fault diffusion process under deliberate attack of rail transit network is more intense than random attack and has faster propagation speed. Other factors being the same, the greater the disturbance amplitude, the stronger the coupling strength, and the easier the rail transit network is to cause cascading failure. The P-space network is more sensitive than the L-space network.
Correction: Xiang, Y.M.; Pan, W.F.; Jiang, H.B.; Zhu, Y.F.; Li, H. Measuring Software Modularity Based on Software Networks. Entropy 2019, 21, 344

The authors wish to make the following corrections to the paper [...]
Model Checking in a Development Workflow: A Study on a Concurrent C++ Hash Table

In this paper, we report on our effort to design a fast, concurrent-safe hash table and implement it in C++, correctly. It is especially the latter that is the focus of this paper: concurrent data structures are notoriously hard to implement, and C++ is not known to be a particularly safe language. It however does offer unparalleled performance for the level of programming comfort it offers, especially in our area of interest – parallel workloads with intense interaction. For these reasons, we have enlisted the help of a software model checker (DIVINE) with the ability to directly check the C++ implementation. We discuss how such a heavyweight tool integrated with the engineering effort, what are the current limits of this approach and what kinds of assurances we obtained. Of course, we have applied the standard array of tools throughout the effort – unit testing, an interactive debugger, a memory error checker (valgrind) – in addition to the model checker, which puts us in an excellent position to weigh them against each other and point out where they complement each other.
ROLE OF EDUCATION IN GLOBAL PEACE

The world is changing at a very fast pace. Machines and materialistic behavior have substituted emotions and sensitivity that leads to developing tolerance. This article deals with several threats to global peace. Global peace is affected by various aspects like geographical (global warming), economic, political, social, religious, etc. All these threats are the result of the lack of real education. This study deals in detail the role of education in global peace, which is not just the absence of war but peaceful, cooperative and harmonious conduct of international relations with a view to secure all-round development of the people.
Using Offline Maps with Mapbox

Many mobile applications need to work in areas with no or limited cellular coverage. If you as a developer want to provide mapping functionality, there are a few ways to do this, but the best way would be to use a map framework that you can use either online or offline. The Mapbox SDK for iOS provides the capability to download offline map tiles and then store them on the user’s phone for use in a map view later. I’ve used this offline functionality to create a hiking app for the New Mexico mountains and to provide maps for kids with iPads on road trips.
Research on Reminder Sign Setting of Auxiliary Deceleration Lane Based on Short-Term Memory Characteristics

Short-term memory (STM) is a memory retention characteristic of drivers after recognition of traffic signs. Reasonable use of STM can greatly improve the recognition and memory of traffic signs during driving. Based on the analysis of drivers’ short-term memory characteristics, combined with the setting method of traffic sign height under the conditions of horizontal road surface and long downhill road condition, this paper studies the setting of sign height, front distance, and repetitive distance of auxiliary deceleration lane, and then reduces the potential traffic accidents caused by drivers’ forgetting of traffic information. Therefore, it is possible to improve the utilization of auxiliary deceleration lanes.
Using Google Places in Your iOS App

Google Places provides a rich database of locations and photos to iOS apps through the Google Places SDK. With the SDK, your mobile app can augment its location abilities with additional details and photos or even provide a places search with autocomplete.
A Methodology of Spatial Risk Estimation for Lane Change Conflicts Based on Real-Time Vehicle Trajectory Data

Drivers’ visual perception is subject to various constraints and heterogeneities, so lane-changing behavior often presents collision risk. To identify potential collisions more accurately, a new method of spatial risk estimation (SRE) considering vehicle trajectory prediction is proposed. Existing research mainly focuses on speed and accuracy in identifying instantaneous or overall risk during lane change, but this paper presents a mobile coordinate system to show relative positions of vehicles clearly. Then, 1,444 sample groups are established by using trajectory data provided by NGSIM. Finally, based on real-time trajectories of the surrounding vehicles, potential conflict events can be classified efficiently by the proposed model, while locations of potential conflict points are visualized by a dynamic coordinate system. Results show the proposed model can effectively identify potential conflict events during lane change. The accuracy of this model shows improvement in recognizing lane-changing conflict events. The findings can provide support for the design of an intelligent lane-changing warning system.
Block Chain Algorithm Rationality on Financial Game Rules Impact Study

Algorithm is the cornerstone of blockchain application. Blockchain makes the trust relationship of human society based on algorithm rationality and has the advantages of abstraction, time dimension and point-to-point, which gradually changes the production relationship and affects all aspects of economic society. In the field of finance, blockchain will change the power contrast, transparency and supervision mode of financial game, which will make great changes in the financial industry. To grasp the essence of blockchain algorithm rationality is the key point to understand and deal with financial reform.
Implementation of Online Dictionary Based on the Simplified Algorithm

With the continuous development of the embedded systems. The application of the APP in the embedded portable products is more and more extensive. In order to implement the using of the online dictionary, based on the simplest algorithm, a simple classic online dictionary was designed in this article. With the simplest algorithm, a simple online dictionary was developed based on the Internet. The dictionary has a simple structure and a simple model, which can meet the user’s requirements for multiple functions such as words, speech, sentences and translation. The program is designed and implemented using multiple processes, which can respond quickly to achieve the effect of querying words in the shortest time.
A Summary of Deep Learning Algorithms

The application of convolution neural network technology promotes the rapid development of artificial intelligence. Machine learning and deep learning have become research hotspot, It is widely used in computer vision and natural language recognition. This paper studies and describes the common deep learning algorithm, and compares the use scope and advantages of CNN and RNN algorithm.
EMPIRICAL STUDY OF CREATIVITY DEVELOPMENT IN STUDENTS WITH HIA

The article highlights the results of theoretical and empirical research on the problem of creativity development in students with special educational needs on the basis of application in educational and extracurricular activities. Involvement of students in a university should include all the main fields of activity: educational, extracurricular and research. The main methods of this involvement are seen as business games, situational tasks, trainings and creative techniques, such as «six hats of thinking», «W. Disney technique», method of brainstorming, the use of mental maps and clusters in solving problem situations. All of these techniques are based on making life experience of students with disabilities relevant and involve the solution of professional problems of the student. It is also noted that in modern inclusive and special education different, classical and innovative technologies of working with children, adolescents, young people and adults with HIA are needed, synthetic and flexible approaches are needed in educational, correctional, other types of activity of students and with students with disabilities. We associate the prospects of our research with the study of the entire complex of «vital»and hindering the development of the personality of the trainee with HIA and without HIA conditions, including the conditions for education in the median-saturated environment of inclusive and special secondary and higher, development-oriented different creative abilities and levels of creativity, education.
Using dialogue-games in Improving Speaking Ability of participants of advanced level of PIKIH Program

This research aims at finding out whether or not dialogue-games improves speaking ability significantly better than conventional method and whether or not dialogue-games enhances the interest of participants of advanced level of PIKIH program at UIN Alauddin Makassar in speaking class.  This research applied quasi-experimental method. The research data were collected using two kinds of instruments, namely: speaking test and questionnaire. The speaking test was given in the form of interview. The speaking test was to know the students' achievement on speaking ability and the questionnaire was to know the students' interest toward using dialogue-games in speaking class. The findings on speaking ability of the participants were analyzed by using independent sample t-test and the students' interest was analyzed by using descriptive statistic. The findings of the study indicated that the result of post-test was higher than of the pre-test. It means that using dialogue-games and conventional method in teaching English with emphasizes on speaking ability contributed to the students' speaking skill. The study also found that the participants are interested in learning English by using dialogue-games. The study concluded that: (1) Using dialogue-games improved speaking ability of participants of advanced level of PIKIH program at UIN Alauddin Makassar significantly better than conventional method, and (2) the participants were highly interested in learning English by using dialogue-games.
Library: Film and media: Websites

A guide to library support, resources and services for students studying screen and media Useful Websites
Comparison of Solar Tracking and Solar Fix Mode on the Efficiency Electric Energy Generation Based on Microcontroller

Abstract A Solar tracker is a device that is powered by solar panels motors, aiming to adjust the position of the panel with the movement of the sun so that energy can be absorbed optimally. This study will compare the use of solar panels and solar trackers fix the model, so on getting the efficiency of each system. The test site is on the campus of the Institute of Technology of Sumatra. Prototype solar tracker is controlled using a microcontroller Arduino, ethernet shield as a liaison Arduino with local networks that can be connected to the web, the stepper motor is used as a driver, is equipped with a voltage sensor, current sensor, the sensor LDR (Light Dependent Resistor) for parameter tracking the sun, solar charge controller as control the charging and battery as energy storage. Parameter solar energy tracker and fix displayed realtime models consist of the reading of voltage, current, and power in the form of a graph on a web monitoring. Calculation of energy that can be generated a model of solar tracker is the difference between the energy stored in the battery is reduced by the energy to activate the motor driver, so we get solar tracker models to use efficiency as compared to fixed models.
Dynamic resource allocation recommendation algorithm based on popularity

Aiming at the unreasonable resource allocation problem of traditional mass diffusion and heat conduction algorithms, this paper proposes a dynamic resource allocation algorithm based on popularity. Taking into account the different degree of influence of the popularity of items in different periods, this paper proposes the concept of item weighting to improve the traditional algorithm, and design the non-equilibrium mass diffusion and heat conduction algorithm. The experimental results prove that compared with the original mass diffusion algorithm and heat conduction algorithm and some improved algorithms based on mass diffusion and heat conduction, the algorithm proposed in this paper can significantly improve the performance of the Recommender system.
Computer-Aided Recognition Method for Bronze Decoration

Research the key technology of bronze decorative pattern recognition and digital design method of bronze based on computer aid. Analyzed the basic status quo of current digital design, based on computer-aided technology and triangular grid basic theory, combined with the anti-sharpening mask method, constructed an enhanced algorithm and process for feature recognition of decorative targets, match the target curve using the curve chord length method, localize the surface using moment vectors, match and splice surface characteristic curves. Finally, an intelligent method for the recognition of bronze decorative features was established, based on computer-aided technology, the complete process from the recognition of decorative features to the matching and splicing of spatial curves is used for the recognition of decorative features. The computer-aided design technology bronze feature recognition method has the characteristics of digitization, intelligence and efficiency, through algorithmic reasoning, this method has broad application prospects in target object pattern recognition and extraction.
Provably secure certificateless encryption scheme in the standard model

Recently, numerous certificateless encryption (CLE) schemes have been introduced.The security proofs of most schemes are given under the random oracle model (ROM).In the standard model, the adversary is able to calculate the hash function instead of asking the challenger.Currently, there is only one scheme that was proved to be secure in SM.In this paper, we constructed a new CLE scheme and gave the security proofs in SM.In the new scheme, the size of the storage space required by the system is constant.The computation cost is lower than other CLE schemes due to it needs only two pairing operations.
Tailored Pruning via Rollback Learning

Structured pruning of networks has received increased attention recently for compressing convolutional neural networks (CNNs). Existing methods are not yet optimal, however, because there is still an obvious performance loss after pruning. This conflicts with the learning theory’s parsimony principle that prefers the simpler model, when they explain the same facts, and thus maintain a consistent level of performance. In this paper, we propose rollback learning for pruning (RLP)-an algorithm to efficiently prune networks while maintaining performance. RLP provides a generic and tailored pruning framework that is easily applied to existing CNNs. Unlike existing work, we conditionally prune the kernels, and only those reaching the performance limit (high filter sparsity) are ultimately removed. This guarantees an optimized pruning by backtracking and updating the dense filters to their full potential. Our implementation adds rollback pruning into generative adversarial learning to compute a soft mask to scale the output of the network structures by defining a new objective function. The soft mask is backtracked and duplicated before and after the batch normalization layer, leading to an optimized fit to the structure pruning. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art methods resulting in a $$\mathrm{{0.25}} \times $$ FLOPs reduction and $$0.04\% $$ accuracy increase over the full-precision ResNet-18 model on CIFAR10, and an $$\mathrm{{0.18}} \times $$ FLOPs reduction and $$0.54\% $$ accuracy increase over the full-precision ResNet-50 model on ImageNet.
Template-Enhanced Aspect Term Extraction with Bi-Contextual Convolutional Neural Networks

Opinion always has a target that usually appears in the form of an aspect. Thus, aspect term extraction is a fundamental subtask in opinion mining. Although deep learning models have achieved competitive results, they have neglected the effectiveness of the local context feature in extracting aspect terms. This paper proposes a simple yet effective bi-contextual convolutional neural network model to capture the local context feature. Besides, the lack of annotated training data restricts the potential of deep model performance. Thus, this paper proposes a template-driven data augmentation strategy to alleviate the influence of data insufficiency. Experimental results demonstrate that the data augmentation strategy can effectively enhance the performance of the proposed model, which outperforms the existing state-of-the-art methods.