Reviewerly/exHarmony huggingface dataset:
Physics textbooks and its network structures

We can observe self-organised networks all around us. These networks are, in general, scale-invariant networks described by the Barabasi-Albert model. The self-organised networks show certain universalities. These networks, in simplified models, have scale-invariant distribution (power law distribution) and the characteristic parameter Î± of the distribution has value between 2 and 5. Textbooks are an essential part of the learning process; therefore, we analysed the curriculum in secondary school textbooks of physics from the viewpoint of semantic network structures. We converted the textbook into a tripartite network, where the nodes represented sentences, terms and formulae. We found the same distribution as for self-organised networks. Cluster analysis was applied on the resulting network and we found individual modulesâ€”clusters. We obtained nine clusters, three of which were significantly larger. These clusters presented kinematics of point mass, dynamics of point mass and gravitational field with electric field.&#x0D; Keywords: Physics textbook, scale-invariant distribution, semantic network.
IMPROVEMENT OF SPEAKING BY USING PRESENTATION IN CULTIVATION PROGRAM STUDENT OF POLITEKNIK KELAPA SAWIT BEKASI

Speaking is one of the basic language skills that have to be mastered by English foreign learners due to its significant and its use for communication. It is very important to be able to speak English regarding that it is the most commonly accepted language in the world so that it will be very beneficial for those who comprehend it not only to improve their knowledge and skills but also easier for them to get a job. However, the researcher has found that there are many similarities and difficulties faced by the students in speaking English such as fear of making mistakes, fear of being laughed by their friend as they have no idea about pronunciation and grammar that they use. The objective of this research was to prove whether or not students speaking skills be improved by using presentation for students of Bekasi palm oil polytechnic cultivation program. The result of this study shows that At the beginning of the presentation assignment, students' speaking ability is still very limited. But when the presentation assignments went on for quite a long time, students were getting used to the presentation of English and they were also more used to doing public speaking. Based on the results and discussion, it can conclude that presentation can improve student’s speaking skill for Cultivation Program Student of Politeknik Kelapa Sawit Bekasi.
An Improved Crawler Based on Efficient Ranking Algorithm

With the increase in number of pages being published every day, there is a need to design an efficient crawler mechanism which can result in appropriate and efficient search results for every query.Everyday people face the problem of inappropriate or incorrect answer among search results.So, there is strong need of enhance methods to provide precise search results for the user in acceptable time frame.So this paper proposes an effective approach of building a crawler considering factors of URL ranking, load on the network and number of pages retrieved.The main focus of the paper is on designing of a crawler to improve the effective ranking of URLs using a focused crawler.
Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

Workflow scheduling is a key problem to be solved in the cloud to increases the quality of services. Few research works have been designed for performing workflow scheduling using different techniques. But, scheduling performance of existing techniques was not effective when considering a larger number of user tasks. Besides, the makespan of workflow scheduling was higher. In order to solve such limitations, Gene Optimized Deep Neural Round Robin Scheduling (GODNRRS) Technique is proposed. The designed GODNRRS Technique contains three layers namely input, hidden and output layer to efficiently perform workflow scheduling in the cloud. The GODNRRS Technique initially gets the number of user tasks as input in the input layer and forwards it to the hidden layer. After taking input, GODNRRS Technique initializes gene population with the assist of virtual machines in Amazon cloud server at the first hidden layer. Next, GODNRRS Technique determines fitness function for each virtual machine using their energy, memory, CPU time, bandwidth capacity at the second hidden layer. Afterward, GODNRRS Technique defines a weight for each virtual machine at the third hidden layer depends on their fitness function estimation. Consequently, GODNRRS Technique distributes the user tasks to optimal virtual machines according to their weight value at the fourth hidden layer in a cyclic manner. At last, the output layer renders the scheduled tasks result. Thus, GODNRRS Technique handles workflows in the cloud with improved scheduling efficiency and lower energy and makespan. The GODNRRS Technique conduct the experimental evaluation using metrics such as scheduling efficiency, makespan, and energy consumption with respect to a different number of user tasks from LIGO , Montage and cybershake real-time applications. The experimental result show that the GODNRRS Technique is able to increases the efficiency and also reduces the makespan of workflows scheduling in the cloud as compared to state-of-the-art works.
Reduction and optimal performance of acyclic adders of binary codes

The conducted studies have established the prospect of increasing productivity of computing components, in particular, combinational adders, based on applying principles of computation of digital signals of the acyclic model.Application of the acyclic model is designed for:‒ the process of series (for low-order digits of the adder circuit) and parallel (for the rest of the digits) computation of sum and carry signals. Due to this approach, it is possible, in the end, to reduce complexity of the hardware part of the device and not increase the circuit depth;‒ setting the optimal number of computational steps.The assumption that the number of computational steps of the directed acyclic graph with two logical operations (AND and XOR) determines optimal number of carry operations in the circuit of the n-bit parallel adder of binary codes was experimentally proved. In particular, this is confirmed by presence of the 8-bit parallel acyclic adder with the circuit depth of 8 standard 2-input logic elements. Connection between the number of computational steps of the acyclic graph and the number of operations of a unit carry to the high-order digit causes the process of comparison of the adder structure with the corresponding acyclic graph. The purpose of this comparison is to set the minimum sufficient number of carry operations for adding binary codes in the circuit of a parallel adder using the parallel carry method.Use of the acyclic model is more advantageous in comparison with counterparts due to the following factors:‒ less development costs since the acyclic model requires a simpler adder structure;‒ presence of an optimization criterion, i.e. the number of computational steps of the acyclic graph indicates the minimum sufficient number of operations of a unit carry to the high-order digit.This provides the possibility of obtaining optimum indicators of the adder structure complexity and circuit depth. Compared to counterparts of known 8-bit prefix adder structures, this provides a 14–31% increase in the 8-bit acyclic adder operation quality, e.g. power consumption or chip area depending on the chosen structure,There are grounds to assert possibility of increasing productivity of computing components, in particular, binary code adders applying the principles of computation of digital signals of the acyclic model
Bayesian Differential Privacy for High-Dimensional Medical Data Optimization

With the popularization of the Internet and electronic medical records, the security and privacy protection of medical data are increasingly important. The existing differential privacy protection technology cannot effectively handle the problems of the publication of high-dimensional medical data. Based on the current issues of high-dimensional medical data, we proposed a new method, which is a new Bayesian differential privacy protection method, so as to release the high-dimensional medical data safely. Through the experimental evaluation, we confirmed that this method can not only ensure the privacy of the published high-dimensional medical data, but also improve the data availability of the original dataset. The constructed Bayesian network can effectively solve the problem of excessive noise caused by high-dimensional medical data due to scalability and signal-to-noise ratio.
Project management in educational systems

The article discusses the problems of the modern textbooks and teaching aids on pedagogy, where the solution of management problems is the content of the chapter, which is often called “Management of educational systems”. Then authors define, that in scientific papers devoted to the problems of pedagogical management, the view of his specific object and subject is substantiated. So, it is considered that pedagogical management as a science of management of educational systems and processes is a branch of pedagogy, the subject of which is the organization of management in the field of education and in educational institutions. The structure of pedagogical management includes the following levels: a. management of the teaching staff; b. teacher activity management; c. student activity management. The authors believe that in pedagogical management there are two levels of management: the head of the educational institution and the teacher. The activity of the head is aimed at creating conditions for the effective functioning and development of the entire educational system of the institution; the task of the teacher-manager is the organization of educational work with the aim of developing cognitive activity and the formation of the student's personality.&#x0D; According to the authors, project management is among the “young” and most demanded areas of scientific knowledge and practice. Development of it over the past 50–60 years has gone from its understanding as a technological scheme for organizing work on the implementation of individual projects to the idea of project management as a methodology for system changes, which carried out in organizations, firms, corporations, territorial entities, across the country and interstate associations.&#x0D; The article also reviewed the most diverse areas of scientific knowledge. It is the project culture. We think that it is institutionally manifested in project activities, although it does not boil down to it. All of the above gives grounds for identifying historical and cultural sources of project management as a person’s ability, an independent type of professional activity and, finally, a cultural phenomenon.&#x0D; The authors think that in modern management concepts, there are two types of management - process (management of recurring activities in fundamentally unchanged external conditions) and project management (change management of the managed system). The latter is precisely the subject of research in the field of scientific knowledge and practice, called “project management” and the subject of one of the branches of management science - project management.&#x0D; In conclusion, the authors define the methodological foundations of project management in pedagogical management are system-activity, process, and resource approaches, which, being internally consistent, set the fundamental methodological orientation of research in the considered field of scientific pedagogical knowledge.
High Dimensional Quantum Key Distribution with Biphoton Frequency Combs through Energy-Time Entanglement

Biphoton frequency combs offer a photon-efficient way to achieve a very high-rate quantum key distribution by utilizing both time and frequency-bin encoding, while offering theoretically proven unconditional security. © 2019 The Author(s)
1.25 Gbits/s-message experimental transmission utilising chaos-based fibre-optic secure communications over 143 km

Chaotic optical secure communications (COSC) are a kind of fast-speed hardware encryption technique at the physical layer. Concerning the practical applications, high-speed long-haul message transmission is always the goal to pursue. In this paper, we report experimentally a scheme of long-haul COSC, where the bit rate reaches 1.25 Gbits/s and the transmission distance up to 143 km. Besides, a distinct advantage of low-cost is guaranteed with the off-the-shelf optical components, and no dispersion compensating fibre (DCF) or forward-error correction (FEC) is required. To the best of our knowledge, this is the first experimental evidence of the longest transmission distance in the COSC system. Our results show that high-quality chaotic synchronisation can be maintained both in time- and frequency-domain, even after 143 km transmission; the bandwidth of the transmitter is enlarged by the external optical injection, which leads to the realisation of 2.5 Gbits/s-message secure transmission up to 25 km. In addition, the effects of device parameters on the COSC are discussed for supplementary detail.
3D model retrieval and pose estimation for indoor images by simulating scene context

In this paper we propose a single image-based 3D model retrieval and pose estimation method for indoor scenes. By simulating the scene context of the input image, our method is able to handle several challenging scenarios featuring cluttered backgrounds and severe occlusions. To use our system, the user only needs to drag a few semantic bounding boxes for the query objects. The proposed approach then retrieves the most similar 3D models from the ShapeNet model repository, and aligns them with the corresponding objects automatically. This requires that the 3D models are represented by calibrated view-dependent visual elements learned from the rendered views. With the estimated occlusion relationships, the rendered view images are stacked at the corresponding locations to simulate the scene context. By conducting matching between these composed scenes and the input image, the most similar 3D models under the approximate poses are retrieved. Experimental results on public datasets demonstrate the effectiveness of our proposed method. Moreover, we show that the retrieving time can be significantly reduced based on a novel greedy algorithm. Extensive experiments on synthesized images show that the proposed algorithm achieves a high retrieval accuracy, outperforming state-of-the-art methods.
STUDY OF PALMAR PATTERNS IN CACINOMA BREAST PATIENTS OF CENTRAL INDIA

Background: Breast cancer is very common among females these days, it's occurrence has a strong genetic basis which has been already established.Palmar patterns also develop under genetic control during early development almost at the same period when the mammary buds develop in intrauterine life.And they do not get altered significantly after that, and thus remains almost constant during rest of the life.So these patterns may be assessed as a non-invasive anatomical marker to find the risk of developing breast cancer later in life.Methodology: In this study assessment of the palmar patterns of both hands of 109 female breast cancer patients were taken and compared with the patterns of 109 age matched normal healthy females.The Patterns were obtained by Ink method in an A4 size white paper.Palmar ridge count (a-b ridge count), palmar angles ( atd angle and adt angle) of both the groups was measured and analysed.Results: On statistical analysis it was observed that mean a-b ridge count of right and left hands of cases were significantly lower when compared with the mean a-b ridge count of right and left hands of the normal healthy females respectively in Madhya Pradesh. Conclusion:The palmar ridge counts are significantly associated with breast cancer and in a highly populated country like India with limited medical resources, it may be employed as an inexpensive and effective tool for screening the high-risk population for breast cancer.
Multi-scale HOG feature used in object detection

Object detection is one of the most popular and difficult field in computer vision. Although deep learning methods have great performance on object detection. For specific application, algorithms which use hand-crafted features are still widely used. One main problem in object detection is the scale problem. Algorithms usually use image pyramid to cover as many scales as possible. But gaps still exist between scale levels in image pyramid. Our work extends some sub scale level to fill the gaps between image pyramids. To this end, we use Gaussian Scales Pyramid to generate sub-scale image and extract HOG feature on the sub-scale. We use framework offered by DPM algorithm and make modification on it. We compare the result of our method with DPM baseline on Pascal VOC database. Our work has great performance on some categories and makes an improvement on the overall performance. This work can be used in other object detection frameworks. We apply multi-scale HOG feature on pre-process procedure of our own detection framework and test it on our own dataset. Then the framework gains performance improvement on precision and recall rate of the pre-process procedure comparing to the original HOG feature architecture.
Data-Driven Model for Non-Functional Requirements in Mobile Application Development

The incredible development in the utilization of smartphones has driven the development of billions of software applications famously known as 'apps' to accomplish roles outside phone call and SMS messages in the day-to-day lives of users.Current assessments show that there are a huge number of applications developed at a meteor pace to give clients a rich and quick client experience.Mobile apps users are more concerned about stability and quality now more than ever despite the increase in the scale and size of apps.As such, mobile apps have to be designed, built, and produced for less money (maintainability, portability, and reusability), with greater performance, reliable security and fewer resources (efficiency) than ever before.This paper aimed at providing support for mobile application developers in dealing with the evereluding non-functional requirements by proposing a data-driven model that simplifies the non-functional requirements (NFR) p in the development of an application for mobile devices.The study tries to find out if NFR can be treated the same way as functional requirements in mobile application development.Finally, this paper shows the experimental evaluation of the proposed data-driven model of dealing for nonfunctional requirements in the development of mobile apps and the results obtained from the application of the model are also discussed.
PERANCANGAN SISTEM INFORMASI GEOGRAFIS (SIG) SEBAGAI MEDIA INFORMASI PRAKERIN BERBASIS WEB DI SMKN 6 TANGERANG SELATAN

Industry Work Practice or commonly called the PRAKERIN is an activity that must be followed by every vocational students during school as a form of implementation of science or as an addition to an insight into the working world they will face in the future . But obstacles that was often faced by student is in the search company locations the industrial work practice . From the problem, we need to make a web based geographic information system (GIS) that provides information about the location of company in Tangerang. In search of the location or place, it would require a geographic information. Nowadays, people mostly use Google Maps application. To overcome the obstacles mentioned above, the authors usea SWOT (Strength, Weakness, Opportunity, Thread) to identify the obstacles encountered in greater detail.To create a model or design of an expert system, the authors use UML (Unified Modeling Language) as a tools. and taking data sources form map's that author got from Google Maps API,location’s data sources form SMKN 6 Tangerang selatan that has been cooperated with the company. The author hopes the student can easyly access the location place the industrial work practice .
MSF-ACNN: multi-scale feature fusion atrous convolutional neural networks for pedestrian fine-grained attribution detection

Pedestrian fine-grained attribution detection has drawn significant interest in areas such as smart video surveillance analysis and pedestrian re-identification. Current object detection methods based on deep convolutional neural networks (CNNs) have achieved great progress. However, detections of small parts of pedestrian are still challenging due to their limited resolution and information in images. In this paper, we propose a novel CNN-based framework for improving the accuracy of small objects detection, dubbed MSF-ACNN, using atrous convolutions in cascade along with multi-scale feature fusion: 1) Atrous convolution effectively expands the field-of-view of small regions without increasing the number of parameters and computation. 2) Multi-scale feature fusion obtains more meaningful fine-grained information from both the low-level and high-level feature maps and can handle a variety of image scales. Our results show that MSF-ACNN can obtain better mean average precision (mAP) than the current state-of-the-art methods with faster detection speed, achieving significant improvements on certain small parts of pedestrian such as shoes, bag and hat.
Evolving neural networks to follow trajectories of arbitrary complexity

Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest. However, current methods are limited with respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution of robot trajectories as an example, we show that by adding four features, namely (1) freezing of previously evolved structure, (2) temporal scaffolding, (3) a homogeneous transfer function for output nodes, and (4) mutations that create new pathways to outputs, to standard methods for the evolution of neural networks, we can achieve an approximately linear growth of the complexity of behavior over thousands of generations. Overall, evolved complexity is up to two orders of magnitude over that achieved by standard methods in the experiments reported here, with the major limiting factor for further growth being the available run time. Thus, the set of methods proposed here promises to be a useful addition to various current neuroevolution methods.
Training of Qualified IT Specialists in the Conditions of Digital Transformation of Education

The article discusses issues of training specialists in the IT industry. The requirements for employees in the field of digital transformation are formulated and argued. A dynamic multi-level learning system is proposed, providing quality knowledge to students at each level of education.