Reviewerly/exHarmony huggingface dataset:
DEVELOPING INFORMATION SYSTEM OF MANAGEMENT ARCHIEVING CORRESPONDING MAIL BASED ON WEBSITE

Administration is part of the institution authorized to handle documents and archiving. The archiving mail is required for management mail data. The problem is the mail archiving still using hardfile like the result of print out from Office Application such as Microsoft Word. The archiving mail is like writing the data on incoming and outgoing mail in the report’s book. Because of that, The management of letters using the print out is oftenerrors in storing mail data. Applications corresponding aims to change the way storage is hardfile letter into tangible softfile mail archives, making it easier to perform archiving these letters as a database.This application is built using waterfall method which the plot carried out gradually. The result of this application is the officer used this application more helpful for correspondences and improve the satisfaction level of its work is increasing by12.19 % from83.13% for manually to the digitally at 83.13.
Comparative Study of Data Mining Classification Techniques for Detection and Prediction of Phishing Websites

Data mining is the process of discovering or extracting information from large amount of data that are stored in databases or datasets such as phishing dataset.Phishing is a vital web security problem that involves simulating legitimate websites to mislead online users in order to steal their sensitive information.This paper aims to detect and predict the type of the website to either legitimate or phishing class label.It investigates different data mining classifiers that are applied to the phishing dataset aiming to determine the effective ones in terms of classification performance.The comparison between nine classifiers with help of rapid miner software was conducted.Here, for comparing the result, five different metrics were used including accuracy, precision, recall, sensitivity and F-Measure.In this study, it has been able to identify the classifiers that precisely recognize fake websites especially with respect to the evolutionary nature of the information attacks.
Study of an Electromagnet for Digital Hydraulics - Numerical Simulation and Experimental Model Testing

This paper presents the construction design and numerical simulation, using FEM of an electromagnet for digital hydraulics, which is aimed to reduce the power losses and making fast switch for on/off valves used in hydraulic circuits. The numerical simulation results present the electromagnetic forces, magnetic field and inductivity for the analyzed electromagnet. The design of this electromagnet and results from numerical simulations conduct to the realization of an experimental model. Also preliminary measurements of the electromagnet are presented in this paper.
Application of combination of neural networks and fuzzy logic in planning of mobile robot motion in unknown environment

The significance of automated control systems to contemporary society is profound, as large-scale industrial robots are used in industrial plants, autonomous vacuum cleaners are applied at home, and autonomous cars are driven in the streets.Regardless of the nature of the robot, one of its main tasks is to do no harm to the person and the environment, and that is the greatest problem of robotics -orientation.Therefore, the problem of orientation of autonomous robots is being solved by a robot exploring the environment, remembering the space and time-varying environmental changes.On that ground, the task of this article is to develop such scientific researches, which define the mobile robot motion in an unknown environment, based on fuzzy logic and the analysis of neural networks, setting an aim to work out methods for developing intellectual systems for planning a mobile robot motion fuzzy logic and neural networks to ensure that the robot performs the planned and adjusted on the way safe trajectory in an environment with unknown obstacles.Therefore, the entire study in the article is aimed at the analysis of fuzzy logic that is analysed as the entirety of the mathematical description methods of fuzzy sets with the formalization of logical conclusions from the fuzzy assumptions, as in this case the decision-making mechanism always allows the generation of the robot's responsive motions caused by the appearance of obstacles in its trajectory; as well as the analysis of neural networks, which links between neurons determine the complexity and flexibility of the operation of the entire neural network, by addressing the problem of planning a mobile robot motion in an unknown environment and which basically depends upon the level of specific network training.As a result of the study, the method of a mobile robot motion in an unknown dynamic environment was created by using a multi-agent system, involving a combination of neural networks and fuzzy blocks.
Automation Methods for Processing Medical Images Based on the Application of Grids

The problem of reducing the complexity of the process of automated recognition and obtaining a quantitative assessment of the graphic objects parameters in medical images are considered.A method for applying a grid to an image is proposed.It makes possible to provide acceptable accuracy of calculations, a high level of generalization of data, as well as reduce the complexity of calculations.The software that implements the proposed methods is developed.The developed software is used in the analysis of metallographic images of materials for medical application.
ARIMA Model Selection for Composite Stock Price Index in Indonesia Stock Exchange

Composite Stock Price Index (CSPI) can be used as a reflection of the national economic condition of a country because it is an indicator to know the development the capital market in a country. Therefore, the movement in the future needs to be forecast. This study aims to build a model for the time series forecasting of Indonesia Composite Index (ICI) using the ARIMA model. The data used is the monthly data of ICI in Indonesia Stock Exchange (IDX) from January 2000 until December 2017 as many as 216 data. The method used in this research is the Box-Jenkins method. The autocorrelation (ACF) and partial autocorrelation function (PACF) are used for stationary test and model identification. The maximum estimated likelihood is used to estimate the parameter model. In addition, to select a model then used Akaike’s Information Criterion (AIC). Ljung-Box Q statistics are used for diagnostic tests. In addition, to show the accuracy of the model, we use Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) and the most appropriate model is ARIMA (0, 1, 1).
Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset

Sensor-based human motion detection requires the subtle amount of knowledge about various human activities from fitted sensor observations and readings.The prevalent pattern recognition methodologies have made immense progress over recent years.Nonetheless, these kind of methods usually rely on the particular heuristic variable extraction, which could inhibit generalization realization.This paper presents a distributed and parallel decision forest approach for modeling the Human Activity Recognition Using Smartphones Data.We made an attempt to achieve an optimal generalization performance with possible reduction in overfitting.Later, we compared the performance of proposed procedure with some existing approaches.It is observed that our adopted procedure outperforms with comparatively better statistical performance measures.It also gained 4.7x speed up in computation.
Human Activity Recognition Based on Smart Chair

We present a smart chair that can detect and classify some common daily activities of elderly people.The chair has the potential to be a huge source of information on the behaviors of people since most indoor activities are performed in sedentary positions.The proposed smart chair comprises six pressure sensors mounted in a chair, together with a Raspberry Pi to collect raw data.The mounted pressure sensors collect signals and transmit them to a server for processing and analysis while the user sits in the chair.Five different activities are detected and classified by these sensors: working at the desk, eating, napping, coughing, and watching TV.In an effort to achieve the best classification of these activities, three different machine learning algorithms are employed and their accuracy scores were compared.These algorithms are the random forest (RF), extremely randomized trees (ERTs), and support vector machine (SVM).The experimental results have proven the ERT to be the best classifier in this survey, since it yielded a classification accuracy above 98% over the testing data.
Visualizing Code Bad Smells

Software visualization is an effective way to support human comprehension to large software systems.In software maintenance, most of the time is spent on understanding code in order to change it.This paper presents a visualization approach to help maintainers to locate and understand code bad smells.Software maintainers need to locate and understand these bad smells in order to remove them via code refactoring.Object oriented code elements are visualized as well as their bad smells if they exist.The proposed visualization shows classes as building and bad smell as letter avatars based on the initials of the names of bad smells.These avatars are shown as warning signs on the buildings.A framework is proposed to automatically analyze code to identify bad smells and to generate the proposed visualizations.The evaluation of the proposed visualizations showed they reduce the comprehension time needed to understand bad smells.
Critical Design in Daily Life: Lifelike Products

(2019). Critical Design in Daily Life: Lifelike Products. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1227-1234.
A Recommendation Algorithm Based on Item Genres Preference and GBRT

Abstract The most mature and widely used collaborative filtering algorithm is facing the problem of data sparsity, which is not conducive to the acquisition of user preferences, thus affecting the recommendation effect. Introducing item genres into recommendation algorithm can reduce the impact of data sparsity on recommendation effect. The personalized preferences of users can be extracted more effectively from the preference information of item genres, and the recommendation accuracy can be further improved. On this basis, this paper proposes a recommendation algorithm based on item genres preference and GBRT, which divides similar users by K-means clustering algorithm, extracts user preferences of item genres and auxiliary features, and establishes a rating prediction model combined with GBRT algorithm. The experiments on the common datasets of Movie Lens 100K and Movie Lens 1M show that the proposed algorithm achieves 0.8%-7% optimization on the evaluation index MAE, which indicates that the impact of data sparsity is reduced to a certain extent and the recommendation efficiency is better than the existing recommendation algorithm.
Clustering of text documents with keyword weighting function

In this digital world, data is available in abundance everywhere and it is growing at a phenomenal rate. Making data available readily for decision making is an important task of data analyst. In this article, we propose an unsupervised learning algorithm for text document clustering by adopting keyword weighting function. Documents are pre-processed and relevant keywords based on their weights are grouped together. Clustered keyword weighting (CKW) takes each class in the training collection as a known cluster, and searches for feature weights iteratively to optimise the clustering objective function, in order to retrieve the best clustering result. Performance of CKW is validated by clustering BBC news collection text collections. Experiments were conducted with simple K-means, hierarchical clustering algorithms and our keyword weighting and clustering approach has shown improved cluster quality compared to the other methods.
Tennis Player Training Support System based on Sport Vision

Sports vision based tennis player training support system is proposed.In sports, gaze, dynamic visual acuity, eye movement and viewing place are important.In sports vision, Static eyesight, Dynamic visual acuity, Contrast sensitivity, Eye movement, Deep vision, Instant vision, Cooperative action of eye, hand and foot, and Peripheral field are have to be treated.In particular for the tennis, all of the items are very important.Furthermore, trajectory of gaze location and tennis racket stroke gives some instructions for skill-up of tennis play.Therefore, sports vision based tennis player training system is proposed.Through experiment, it is found that the proposed system does work well for improvement of tennis players' skills.
Social Design Fiction. New Methods for the Design of Emerging Technology.

There is no shortage of Emerging Technologies all vying for our attention; a recent example of this is 3D Printing. However, a great deal of this technology fails to deliver on its potential. The implications of these technologies on our everyday lives are often very difficult to envision and even more difficult to predict, so the difficult questions surrounding social adoption and the domestication of these technologies are often absent from the discussion. We describe new design methodologies that seek to resolve this issue and discuss existing studies in the ‘real world’, to highlight the key challenges and opportunities for this method. We then describe the findings of two workshops, to discover how the public envisage they will engage with 3D Printing technology in the future. Ultimately, this paper highlights how Design Fiction can contribute to resolving key social challenges associated with the widespread adoption and exploitation of emerging technology.
Development Strategies of Fiscal Programs for Universities in Korea - With Focused on Estimation of Effect of Restructuring of University Financial Support Project on University Competitiveness

In Korea, as various university financial support projects such as CK, PRIME, ACE, WE-UP project will be completed in 2018.A reorganization plan should be prepared for the direction and
Secure Symmetric Block Cipher Design for Encrypting the Bitcoin Wallets in Cryptocurrencies Applications

This paper proposes a new symmetric secret-key cipher for construction of block cipher model.This new approach is considered as a smart step that can be applied to the long process research of modern designing methods used in developing earlier symmetric algorithms.The present cipher can encrypt block lengths of 128-bit by employing Substitution-Permutation Network (SPN) structure.The present cipher uses three options of strong ciphering-key along with variable lengths of 192-bit for 12 rounds, 128-bit for 10 rounds and long ciphering key of 256-bit for 14 rounds similar to standard ciphers.The proposed algorithm has been designed to trust applications that are based on Bitcoin and cryptocurrencies.The current algorithm intended to encrypt addresses of Bitcoin wallet that work quite similar to the e-mail address.The bitcoin wallet includes sensitive information like private secret keys and derived password that are highly confidential.The Advance Encryption Standard (AES) is employed to encrypt most of the bitcoin wallet database.The proposed cipher can act as a good substitute for the standard cipher that allows trusting the Bitcoin wallet database with high security and with a much more complex scheme.The key goal of the submitted algorithm is to build a new enhanced modern cipher with a secure and efficient applicable algorithm that can be used for crypto currencies applications employing a wide trail design strategy.
Relationships between category theory and functional programming with an application

The most recent studies in mathematics are concerned with objects, morphisms, and the relationship between morphisms. Prominent examples can be listed as functions, vector spaces with linear transformations, and groups with homomorphisms. Category theory proposes and constitutes new structures by examining objects, morphisms, and compositions. Source and target of a morphism in category theory corresponds to input and output in programming language. Thus, a connection can be obtained between category theory and functional programming languages. From this point, this paper constructs a small category implementation in a functional programming language called Haskell.
Improved Student’s t-Based Unscented Filter and its Application to Trajectory Estimation for Maneuvering Target

To estimate the systems with one-step randomly delayed measurement and time-correlated heavy-tailed measurement noises, on the basis of robust Student’s t based unscented filter (RSTUF), an improved Student’s t based unscented filter (ISTUF) is proposed. Referring to the measurement differencing method, a reformed measurement model was built. Then, by augmenting the system state vector, the conditional probability distribution of the measurement noise with respect to delayed measurement was taken into consideration. Based on the reformed measurement model and the augment state vector, a novel estimator was designed to solve the one-step randomly delayed problem. Maneuvering target tracking systems were used for simulation. Compared with unscented Kalman filter (UKF) or RSTUF, ISTUF had higher accuracy.
PEMBELAJARAN KOOPERATIF STAD DENGAN STRATEGI PENUGASAN MULTI LEVEL INSTRUCTION PADA MATERI MATEMATIKA

The purpose of this study was to determine the activities, responses, and student learning outcomes with multi level instruction learning. Multi level instruction assignment learning strategy is a selling system adoption from multi level marketing strategy with special characteristics that there are students in first level and second level downlines and a teacher as an upline. Type of research is quasi-experimental by comparing two experimental groups. Experimental group one was given the STAD model with multi level instruction, and the other group was given regular STAD model. The research instruments used were student activity observation sheets, student response questionnaires, and learning outcomes test. The data obtained were analyzed descriptively and inferentially. The result indicates that both of them are effective to be applied in mathematic learning, but inferentially the outcomes of the experiment group one was higher than the experiment group two.
Control limit on quantum state preparation under decoherence

Quantum information technologies require careful control for generating and preserving a desired target quantum state. The biggest practical obstacle is of course decoherence. Therefore, the reachability analysis, which in our scenario aims to estimate the distance between the controlled state under decoherence and the target state, is of great importance to evaluate the realistic performance of those technologies. This paper presents a lower bound of the fidelity-based distance for a general open Markovian quantum system driven by the decoherence process and several types of control including feedback. The lower bound is straightforward to calculate and can be used as a guide for choosing the target state, as demonstrated in some examples. Moreover, the lower bound is applied to derive a theoretical limit in some quantum metrology problems based on a large-size atomic ensemble under control and decoherence.
RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN KARYAWAN BARU DENGAN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) BERBASIS WEB DI LOSE STORE

Recruitment is an important thing for the company, in acquiring prospective new employees to occupy a position. Most companies, recruitment process is still not done by a professional. This happens because there is no systematic method to assess the feasibility of prospective new employees. Recruitment decision support system applications are built using the Simple Additive Weighting (SAW) method. This method was chosen because it can determine the weight values for each attribute, and then proceed with the ranking process that will select the best alternative from several alternatives. In this case, the alternative is entitled to be accepted as a new employee in accordance with the criteria specified. Based on test results, a system built to simplify and speed up the selection process for recruitment, and assist Human Resources Department (HRD) managers in decision-making to determine a new employee at a company.
Towards Effective Service Discovery using Feature Selection and Supervised Learning Algorithms

With the rapid development of web service technologies, the number and variety of web services available on the internet are rapidly increasing.Currently, service registries support human classification, which has been observed to have certain limitations, such as poor query results with low precision and recall rates.With the huge amount of available web services, efficient web service discovery has become a challenging issue.Therefore, to support the effective application of web services, automatic web service classification is required.In recent years, many researchers have approached web service classification problems by applying machine learning methods to automatically classify web services.The ultimate goal of our work is to construct a classifier model that can accurately classify previously unseen web services into the proper categories.This paper presents an intensive investigation on the impact of incorporating feature selection methods (filter and wrapper) on the performance of four state-of-the-art machine learning classifiers.The purpose of employing feature selection is to find a subset of features that maximizes classification accuracy and improves the speed of traditional machine learning classifiers.The effectiveness of the proposed classification method has been evaluated through comprehensive experiments on real-world web service datasets.The results demonstrated that our approach outperforms other state-of-the-art methods.
An Ongoing Treasure Hunt: One Library’s Practical Experiences Documenting Post-Cancellation Perpetual Access

Albertsons Library embarked on a practical effort this past year to document post-cancellation perpetual access for those electronic journal titles that had been or were part of large package purchases. Documenting entitled content was challenging and hampered by limits to accounting records maintained at the library and university; a change in the library’s integrated library system (ILS); limited or incomplete access to third-party subscription agent order and payment records; and changes or the demise of consortia. Decisions were made to work with the known more recent electronic journal content purchased, and to work backwards from there. Procedures for creating standardized perpetual access documentation by title was devised for use in the ILS cataloging module as well as the library’s electronic resource management system. By creating a method for documenting perpetual access, institutions are able to support claims for access purchased by the library, even as content shifts from one publisher to another.