Reviewerly/exHarmony huggingface dataset:
Restructuring of Backrest and Drainage Provision System in the Campus Area

The University of Lampung area, especially in the Faculty of ISIP, FEB, and FT is a densely packed student area. This area has undergone many land-use changes. The condition of the land as a green open space has changed its function to become an area for lecture buildings and offices. One of the impacts is an increase in direct surface runoff and a decrease in the quantity of water that seeps into the ground, this condition causes flooding during the rainy season. To facilitate the rehabilitation of the drainage system in the University of Lampung area, it is necessary to redesign the drainage system of the University of Lampung area. Rehabilitation of drainage channels is carried out to resolve flood inundation points that occur during the rainy season. Rainwater that is channeled through drainage channels is directed to natural or artificial reservoirs. The collected rainwater is used to recharge groundwater through natural infiltration methods. The analysis carried out in this study includes hydrological analysis and analysis of the existing drainage sections and the solutions are given. The hydrological analysis aims to calculate the planned discharge using the rational method. Modeling with the application used in this study aims to determine the capacity of the water level in the existing channel. Based on the results of the analysis, in the area, the Faculty of Engineering experienced runoff and inundation. This situation requires rehabilitation of the Lampung University area drainage system.
Real-Time Traffic Light Detection and Interpretation using Circle Centroid

The paper proposes a new method to recognize the sequence of a traffic light using image processing algorithm. Invariant in factor lightning and weather condition that lead to misinterpret the color of traffic light is one of the factors of accident at traffic light conjunction besides the behavior of the driver itself. The process to identify the color and shape of traffic light are Image Acquisition, Pre-Processing, Detection, Feature Extraction and Interpretation. RGB normalization is performed and simple thresholding method that acts as color segmentation provides a better division of the traffic light colors. Circle Hough Transform and HSV color features based on the traffic light aspect are used to decide whether the spots on the frames are likely to be traffic lights’ color and shape. The detection of traffic light will be obtained after identifying the feature such as the centroid of the Circle Hough Transform that need to be extracted at the end of the result. The research has been improved by focusing on detection and interpretation of traffic light based on real time video rather than image sample as the input. The proposed algorithm can detect the green color accurately with maximum accuracy of 83.8%, yellow color of 75.6% and red color with minimum accuracy of 70.19%. This indicates that there is a possibility to use the proposed algorithm to detect the three different color of green, yellow and red color of traffic lights’ colour.
Wavelet-based image compression using mathematical morphology and self organization feature map

Image compression using transform coding technique has been widely used in practice. However, wavelet transform is the only method that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients from amongst the wavelet coefficients. Wavelet transform based image compression result in an improved compression ratio as well as image quality and thus both the signficant coefficients and their positions within an image are encoded and transmitted. In this thesis a wavelet based image compression system is presented that uses mathematical morphology and self organizing feature map (MMSOFM). The significance map is preprocessed using mathematical morphology operators to identify and creat clusters of significant coefficients. A self-organizing feature map (SOFM) is then used to encode the significance map. Experimental results are shown and comparisons with JPEG and JPEG 2000 are made to emphasize the results of this compression system.
Prediction of university students' academic level based on linear regression model

Aiming at the problems of the prediction results by using the existing methods for university students' academic level prediction, such as large error and long time-consuming prediction, this paper proposes a method based on linear regression model to predict the academic level of university students. In order to improve the accuracy and real-time of prediction, Firstly, the students' academic related information is denoised, and then the denoised academic level information is classified. Finally, the linear regression model is used to predict the academic level of university students. The experimental results show that compared with other methods, the prediction error rate of the proposed method varies from 1% to 8%, and the prediction accuracy rate is higher. The predicted duration of the students academic level is in the range of 1 minute to 5 minutes, and the prediction speed is faster.
Research on interaction between logistics industrial clusters and urbanisation based on system dynamics and cloud model

For the problem that some parameters in system dynamics model are difficult to quantify, cloud model, which has the excellent characteristic of qualitative and quantitative transformation, is used in the determination of parameters of system dynamics model. Firstly, the system dynamics model revealing interactive development between the logistics industrial clusters and urbanisation is proposed. Secondly, the specific procedures of parameters calibration based on cloud model are given. Finally, a case study based on Shenyang economic zone is employed to verify the proposed model. According to the simulation results, some best suggestions on interactive development between logistics industrial clusters and urbanisation are put forward.
ANN Classifier for Human Age Classification

Face recognition is an interesting research study with many researchers from computer vision and biometrics fields.The performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we propose a novel technique to group the age of a human dependent on facial skin maturing highlights. Artificial Neural Network (ANN) is proposed to characterize human age into different age gatherings. The ideal highlights are removed utilizing advanced picture handling methods like Local Binary Pattern (LBP), Elliptical Local Binary Pattern (ELBP) and Wrinkle Analysis. The proposed age characterization structure is prepared to test with human face pictures from SQL database with high precision.
The impact of colour visual attention for video summarization

A High Definition visual attention based video summarization algorithm is proposed to extract feature frames and create a video summary. Specifically, the proposed framework is used as the basis for establishing whether or not there is a measurable impact on summaries constructed when choosing to incorporate visual attention mechanisms into the processing pipeline. The algorithm was assessed against manual human generated key-frame summaries presented with tested datasets from the Open Video Dataset (www.open-video.org). Of the frames selected by the algorithm, up to 68.1% were in agreement with the manual frame summaries depending on the category and length of the video. Specifically, a clear impact of agreement rate with the ground truth is demonstrated when including colour-attention models (in general) into the summarization framework, with the proposed colour-attention model achieving stronger agreement with human selected summaries, than other models from the literature.
How large a vocabulary is needed for reading and listening?

This article has two goals: to report on the trialling of fourteen 1,000 word-family lists made from the British National Corpus, and to use these lists to see what vocabulary size is needed for unassisted comprehension of written and spoken English. The trialling showed that the lists were properly sequenced and there were no glaring omissions from the lists. If 98% coverage of a text is needed for unassisted comprehension, then a 8,000 to 9,000 word-family vocabulary is needed for comprehension of written text and a vocabulary of 6,000 to 7,000 for spoken text. © 2006 The Canadian Modern Language Review/La Revue canadienne des langues vivantes.
Repository Construction and Reuse of Software Requirement

Software reuse is not limited to reusing code used a lot but can be used in all steps and activities related to software production. In particular, the reuse of requirements has various benefits by reusing reliable requirements, and the development of requirements is an early stage of software development and may have higher efficiency than the utilization of reuse in later stages if reuse is utilized from the initial stage. However, despite its many advantages, the study on the reuse of requirements is insufficient. Therefore, to explore the possibility of the requirements reuse, we conducted a total of four stages in this paper: selection of targets and stakeholders, construction of requirements repository, reuse of requirements repository, and result analysis, and a case study of requirements repository construction and reuse was conducted. We have confirmed the fact that the reuse of requirements possible with a high proportion in practice through the application of actual case and we have also confirmed the possibility of research on the reuse of requirements. If we deal with the reliable requirements by increasing the utilization of requirements reuse, the possibility of the project's success will also be greatly increased.
FORMAL REPRESENTATION OF THE PIXEL-BY-PIXEL CLASSIFICATION PROCESS USING A MODIFIED WANG-MENDEL NEURAL NETWORK

The subject of research in the article are the processes of formalization of the pixel-by-pixel classification problem using the modified fuzzy neural production network of Wang-Mendel for segmentation of urban structures in the automated analysis of space and aerial photographs of the city. The purpose of the work is to develop the architecture of the modified fuzzy neural production network of Wang-Mendel as a classifier for image segmentation to increase the values of efficiency and reliability of urban monitoring. The following tasks are solved in the article: analysis of possibilities of Wang-Mendel network modification based on representation of membership functions in terms of interval fuzzy sets of the second type (IFST2) and realization of phasing, aggregation and activation operations using IFST 2 operations, development of the architecture of the modified fuzzy neural production network of Wang-Mendel as a classifier for image segmentation. The following methods and models are used: methods and models of fuzzy set theory (fuzzy Wang-Mendel neural network, interval fuzzy sets of the second type), methods and models of deep learning methodology (convolutional neural network for image segmentation (auto coder) U-net). The following results were obtained: the use of a fuzzy Wang-Mendel neural network as a classifier of a modified U-Net decoder based on the representation of membership functions in IFST2 and the implementation of phasing, aggregation and activation operations using operations on IFST2; introduction of an additional operation of type reduction in the phase of dephasification of the original variable based on the classical method of the center of gravity (centroid); introduction of several outputs of the network to recognize the appropriate number of classes (subclasses) of the subject area. To do this, the third layer is represented as a set of several pairs of adder neurons, and the fourth implements several normalizing neurons, the number of which corresponds to the number of pairs of the third layer. Conclusions: the use in the architecture of a convolutional neural network for segmentation of U-net images as a classifier of the modified fuzzy neural production network of Wang-Mendel will provide an additional increase in the accuracy of pixel-by-pixel classification of certain objects. Instead of fuzzy sets of the first type (FST1) in this network IFST2 are used. The proposed IFST2, on the one hand, provide a formalization of more additional degrees of uncertainty compared to FST1, on the other hand, are "implemented" in the development of fuzzy systems (models) and have less computational complexity, compared to fuzzy sets of the second type (FST2).
Innovative pedagogical system of institution of general secondary education as a condition of formation of social activity of pupils

Науковий часопис НПУ імені М. П. Драгоманова Ihnatenko V. D. Modern approaches to teaching students-philologists translation in self-study processThe article analyzes the current state of teaching translation and presents some conclusions that underlie modern changes in the training of future translators.Current trends in the translation services market are driving changes in the training of future translators and the focus shifts to independent and active learning.Thus, modern approaches to the process of organizing selfstudy of students-philologists are changing.Independent activity is generally regarded as a multifaceted and multifunctional activity.Topical in this context is the implementation of a personal-activity approach in the self-study of future translators, which is aimed at creating a positive intellectual-emotional context of educational activity in the educational process.The personal and activity components of the personal-activity approach are analyzed in the context of teaching translation, its concept, basic principles of approach and central categories in the context of self-study, which are secondary linguistic personality and independent, self-directed and productive activity of students.It is considered effective to implement this approach through information and communication technologies, in particular on the basis of the modern Moodle educational environment.The article identifies and describes the educational, control, motivational, informative and communicative functions of the Moodle educational environment.The peculiarities and advantages of using the Moodle educational environment for teaching philologists translation in self-study are analyzed, in particular, the ergonomics of such a learning tool, the accessibility, the self-sufficiency of materials, the possibility of constant updating, the professional direction, the ability to perform written tasks, the constant high quality management and the students' professional activity.
HIGHER EDUCATION IN THE SYSTEM OF DEVELOPMENT OF ENVIRONMENTAL BEHAVIOR OF RUSSIAN YOUTH

Objective of the study is to analyze the role of higher education in the development of environmental behavior of Russian youth. The methodological basis of the research is based on the survey methods of sociological research. Research results consist in the representation of emotional and value orientations in the formation of environmental behavior of students; the representation of the evaluation and activity level of the formation of environmental behavior of students; the typologization of environmental behavior of young people. Prospects of the study are connected with the further study of the specifics of the ecologization of the social behavior of students.
INDIGO DALAM NOVEL SUPERNOVA AKAR KARYA DEE LESTARI: KAJIAN PSIKOLOGI SASTRA

The background of this research is many indigo events in the community and many indigo literary works. Indigo research in the “Indigo dalam Novel Supernova Akar Karya Dee Lestari:Tinjauan Psikologi Sastra,” first aims to find the description of the indigo abilities experienced by the characters, secodly to find the causes of indigo experienced by characters, and thirdly to find out the type of indigo experienced by characters in the Supernova Akar. The research includes qualitative descriptive research on data analysis techniques using textual analysis or text analysis. The subject of this study is a character in the Supernova Akar. The result of research that is narated by Dee lestari source are; 1)levitasi, 2) procegnition, 30 psicometri, 4) teleportasi, and 5) clayvoyance. Indigo causes experienced by characters in the Supernova Akar are; 1) a gift from God, 2) from offspring, and from training. The type of indigo experienced by characters in the Supernova Akar are; humanis and artis.
The Effect of Perceived Organizational Support on Championing Behavior of IT Professionals: Mediating Role of Psychological Empowerment

The study's goal was to examine the mediation of Psychological empowerment in the relationship between Perceived Organizational Support (POS) and Championing Behavior of Information Technology (IT) Professionals.The research aimed to answer the questions regarding the effect of POS on Championing Behavior of IT Professionals with the indirect mediation of Psychological Empowerment.Data were collected from 220 IT professionals in an organizational setting.In contrast, data were collected through a cross-sectional design.Process Hayes model 4 was used for statistical analysis.The results depicted full positive mediation while revealing that POS increases when there is a mediation of Psychological empowerment in it.Additionally, it also explains that championing behavior is affected by organizational behavior and psychological factors.Results highlight HR systems' importance in providing organizational support and empowerment to their employees.This present study will contribute to the research world in a dynamic way.It tells us how we can achieve an environment that will support innovation, change and learning aptitude.In the fast-paced globalized world that is inclined to advancements and changes, we have to be on the pulse of the world and that requires innovation.
FFT enabled ECC for WSN nodes without hardware multiplier support

ECC is a popular cryptographic algorithm for key distribution in wireless sensor networks where power efficiency is desirable. A power efficient implementation of ECC without using hardware multiplier support was proposed earlier for wireless sensor nodes. The proposed implementation utilized the number theoretic transform to carry operands to the frequency domain, and conducted Montgomery multiplication, in addition to other finite field operations, in that domain. With this work, we perform in the frequency domain only polynomial multiplication and use the fast Fourier transform to carry operands between the time and frequency domains. Our ECC implementation over $GF((2^{13}-1)^{13})$ on the MSP430 microcontroller implements multiplications without using a hardware multiplier. It achieves scalar multiplication with fixed and random points in only 0.89 s and 1.74 s, respectively. Our implementation achieves ECC point multiplication of fixed and random points $10\%$ and $13\%$ faster, and consuming $12\%$ and $15\%$ less energy, in comparison to the existing work.
DESIGN OF BIOMECHANICAL SENSOR BASED LOWER LIMBS REHABILITATION TRAINING MACHINE

With the increase of patients who have difficulty in moving by lower limbs in recent years, how to make lower limbs recover effectively has attracted more and more attentions.Most of the patients are unable to do rehabilitation training independently.Therefore, robot technology is gradually used to design lower limbs training machine in medical field.Biomechanical sensor is efficient in collecting the gait information of patients because of its characteristics such as favorable dynamic performance and small volume.This study designed a lower limbs rehabilitation training machine based on biomechanical sensor and verified the practicality of the sensor in lower limbs rehabilitation training machine, which provides a reference and suggestion for studies on the development of rehabilitation machine.
Friend Recommendation using Unsupervised Machine Learning

Friend recommendation is one of a lot of accepted characteristics of amusing arrangement platforms, which recommends agnate or accustomed humans to users. The abstraction of friend recommendation originates from amusing networks such as Twitter and Facebook, which uses friends-of-friends adjustment to acclaim people. We can say users do not accomplish accompany from accidental humans but end up authoritative accompany with their friends’ friends. The absolute methods accept attenuated ambit of recommendation and are beneath efficient. Here in our proposed access, we are applying an added hierarchical clustering technique with the collaborative clarification advocacy algorithm as well the Principle Component Analysis (PCA) adjustment is activated for abbreviation the ambit of abstracts to get added accurateness in the results. The hierarchical clustering will accommodate added allowances of the clustering technique over the dataset, and the PCA will adviseredefining the dataset by abbreviating the ambit of the dataset as required. By implementing the above appearance of these two techniques on the acceptable collaborative clarification advocacy algorithm, the above apparatus acclimated for recommendations can be improved.
Pemanfaatan Middleware Layer untuk Multi Platform pada Mobile dan Desktop

Technology currently has many platforms from hardware and software, as we know some devices also have different programming languages.This is complicated if the existing system still uses the Client-Server method that only stands on one platform, so it is necessary to propose architecture for mobile application development using the existing infrastructure, so it appears A combination of programming logic with a distributed database called Middleware, which communicates to have unity.Multi-platform conditions also require the Internet to connect with application users because it uses HTTP or HTTPS protocols so that data exchange can run.The result of database process is formed in JSON format which is reliable and effective and has been proven reliability in generating output that can be read by multi platform.Development of Middleware using Rapid Application Development method to make software development process effective and fast error handling so as to build software that can serve various platforms and easy to develop.
Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter

Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.