Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Informasi Pengelolaan Kuesioner Pelatihan Pada PT Brainmatics Cipta Informatika

Questionnaire is a technique of collecting data or information through forms that contain questions that will be filled in by several respondents to get responses or answers that will be analyzed by parties who have specific goals, besides the questionnaire is also one technique that is often used to make an assessment of item or service activities that are running. At present PT Brainmatics Cipta Informatika uses a questionnaire to assess the training that has been done, in practice at this time the questionnaire storage process is still ineffective and untidy, then the person in charge of training is often late or does not even report the results of the questionnaire when the training has been done, even though the results of the reporting are used to make improvements to the quality of the implementation of further training. Then from various divisions it was difficult to get the results of the questionnaire analysis needed by these divisions. Currently a web-based system application is needed that can carry out the process of managing the questionnaire as well as conducting automatic analysis of the questionnaire that has been filled out by the training participants, With this Information System for Managing Training Questionnaire, it is expected that the questionnaire data will remain well documented, and the results of the questionnaire analysis can be easily accessed by the training division, finance division and marketing division so that business processes related to each division can run more effectively and more efficiently
A frequentist’s resolution of the exchange paradox

In this paper, we provide a frequentist’s resolution of the exchange paradox. We propose a probability space with which we can explain both the Bayesian and likelihood-based resolutions. Under the proposed probability space, we find the optimal conditional strategy for the game to maximize the expected gain in money, which equals to that in Bayesian resolution. The probability space also allows us to understand that the paradox is from the additional symmetry assumption which is mistakenly taken. We show that the assumption is not feasible, at least in frequentist view, in the sense that no distribution of the money (inside the envelope) exists to satisfy the assumption. This understanding to the paradox is equivalent to the likelihood-based solution by Pawitan (2001 Pawitan, Y. 2001. In all Likelihood: Statistical modelling and inference using likelihood. Oxford: Clarendon Press. [Google Scholar]) that the failure of our reasoning arises from treating the above likelihood function as the probability function and taking expectation over the likelihood function.
Red-FLC: an Adaptive Fuzzy Logic Controller with Reduced Learning Parameters

In this paper, an adaptive Takagi-Sugeno (TS)-fuzzy controller is developed for nonlinear dynamical systems, where a new structure of the controller with reduced learning parameters is proposed. The proposed controller is named as a reduced learning parameter based fuzzy logic controller (Red-FLC). Being a model-free controller, the classical TS-fuzzy one performs well in slow-process control-based complex applications. However, the controller's structure is associated with several antecedent and consequent parameters, which need to be adapted during control operation. Adaptation of a high number of parameters is computationally expensive, especially in controlling a system where a fast response is expected. From this research gap, in our developed adaptive fuzzy controller, the tuning parameters have reduced significantly since it has no antecedent parameters. The closed-loop stability of the controller has been proved using a new adaptation law. To evaluate the proposed controller's performance, it has been utilized to stabilize an inverted pendulum's simulated plant on a cart by considering an impulse disturbance. The performance of Red-FLC has been compared with a classical TS-fuzzy controller and a Proportional Integral Derivative (PID) controller, where better tracking of the cart's position and better disturbance rejection is observed from the proposed TS-fuzzy controller.
Performance Analysis and Ranking of Data Mining Algorithms Across Multiple Datasets

Data mining is a powerful emerging tool for analysis and prediction. It involves searching large volumes of data to discover interesting relationships that can provide meaningful information for understanding hidden trends and forecasting future incidences. Advanced mathematical machine learning algorithms are used in data mining. These algorithms vary in their accuracy and effectiveness depending on various factors. In this paper, a comprehensive analysis of the performance of several machine learning algorithms was conducted. The algorithms were analyzed using multiple performance metrics across multiple datasets. Two separate rankings were developed after evaluating the performance of twenty-three algorithms across ten nominal-class datasets based on nine performance metrics and seventeen algorithms across seven numeric-class datasets based on five performance metrics. The first ranking was based on the average score of algorithms and the second ranking was based on the top scoring algorithms. The results showed that Logistic Model Trees (LMT) and Random Forest (RF) were the top performing algorithms for nominal-class datasets and M5P and Linear Regression (LIR) were the top performing algorithms for numeric-class datasets.
Extracting Explainable Deep Representation for Machine Tutoring

In this work, we propose a machine tutoring framework through a modified Adversarial AutoEncoder(AAE) for Interactive Machine Learning(IML). In IML, masters can teach machines for valuable knowledge and skills and machines can transfer such knowledge and skills to novices. Generally in IML, we can discuss transferring knowledge, something to be known and skills, something needs to know and to practice to learn it well from masters to machines and from machines to novices. In this work, we focus on one part of IML techniques, transferring knowledge and skills from machines to novices, called machine tutoring and realize it, in a Taiko-drum playing game platform. In Taiko-drum playing, skill learning is considered more important than knowledge learning. In particular, we adopt Internal Measurement Unit (IMU), attached on novice's forearms to collect the necessary data to understand novice's motions. We assume that the IMU data can help us understand how well novices learn on some particular motions from machines and from masters eventually. On the side, we capture game scores in each novice's play for the evaluation purpose. Four songs with different levels from easy to hard are used in the experiments. When a novice plays the game, we extract the explainable values from latent features in a deep learner for the representation of the novice's play. The machine tutoring is operated as we show the visualization that is built given the latent representation to the novice to suggest on how we may perform a task better and better as we play more. We demonstrate the effectiveness of the proposed machine tutoring framework by using three different deep neural network structures stacked on the Autoencoder included in the framework including MLP, CNN, and LSTM. The results show that AAE-regression CNN (AAER-CNN) performs the best on latent information representation when the song level is from easy to normal, and AAER-LSTM also performs the best when the song level is difficult on two tasks namely score prediction subject classification. Afterwards, we illustrate how the representation can help on machine tutoring.
Design documentation of software specifications for a maternal and child health system

Babakan Surabaya Community Health Center is a Community Health Center under the Bandung city government that organizes Maternal and Child Health (MCH) services. The current MCH history is recorded in the Cohort Book, but individual patient records are held in the form of an MCH Handbook. The process of recording and reporting MCH is still done manually, and the possibility of data entry errors, problems with data accuracy, and data storage that cannot be accessed at any time can present complications. To be able to produce a good MCH information system, the system needs to undergo functional and nonfunctional analysis. The system requirements are then poured into a Software Requirement Specification (SRS) document. In this study, researchers also measured SRS documents that were produced to determine the quality of the documents.
Music Flipped Classroom Teaching Mode Under the Background of the Internet

Under the background of the “Internet”, many excellent teaching resources are collected on the network, such as the MOOC and We-Chat Platform. Through the network resources, learners can acquire the relevant knowledge. In the current comprehensive reform of the music teaching activities, we should adhere to the music practice as the basis of our teaching innovation, and on the basis of stimulating students’ interests in participating in the music learning, we should pay attention to stimulating students’ dominance, so as to realize the real reform of the music teaching. Under the guidance of the music flipped classroom teaching mode under the background of the Internet, the music practice teaching mechanism needs to be innovated, and especially on the basis of integrating the characteristics of the professional teaching, based on the student-oriented concept, we can achieve the ideal effect of our music teaching.
Image authentication using novel cryptography &amp; steganography algorithm in green ubiquitous environment

Ubiquitous environment is often exposed to security threats like malicious code attacks, information’s being exposed by the unauthorized users, and denial of processing low information using low powered CPU. A high-level security is needed for a secured transmission. The proposed scheme is framed for a high-level secured transmission of data information in a ubiquitous environment. The original image is subjected to DWT and the low frequency part is encrypted using AES. The high frequency part is watermarked with the secret message and the character digest value is transmitted along with all the keys to the receiver side. At the receiver side the image is decrypted and checked for tamper localization. The image if tampered at the receiver side the recovery efficiency is 100%. The overall efficiency and the PSNR is better than the previous implemented method. The scheme is highly robust such that it ensures 100% secured transmission under reliable conditions in ubiquitous environment.
Algorithm Portfolio for Parameter Tuned Evolutionary Algorithms

Evolutionary algorithms' performance can be enhanced significantly by using suitable parameter configurations when solving optimization problems. Most existing parameter-tuning methods are inefficient, which tune algorithm's parameters using whole benchmark function set and only obtain one parameter configuration. Moreover, the only obtained parameter configuration is likely to fail when solving different problems. In this paper, we propose a framework that applying portfolio for parameter-tuned algorithm (PPTA) to address these challenges. PPTA uses the parameter-tuned algorithm to tune algorithm's parameters on one instance of each problem category, but not to all functions in the benchmark. As a result, it can obtain one parameter configuration for each problem category. Then, PPTA combines several instantiations of the same algorithms with different tuned parameters by portfolio method to decrease the risk of solving unknown problems. In order to analyse the performance of PPTA framework, we embed several test algorithms (i.e. GA, DE and PSO) into PPTA framework constructing algorithm instances. And the PPTA instances are compared with default test algorithms on BBOB2009 and CEC2005 benchmark functions. The experimental results has shown PPTA framework can significantly enhance the basic algorithm's performance and reduce its optimization risk as well as the algorithm's parametertuning time.
Convolution Neural Network Learning for Course Outcome Attainment Improvement

The capabilities of connectionist approaches such as Convolutional Neural Networks (CNNs) are used for data analysis. The trained system is used as a recommendation system. ADAMS (Accreditation Data Analysis and Management System) provides the training data in the form of assessment rubrics of course learning outcomes for University level programs. Once the system is trained with this data, it can improvise solutions for untrained cases and help in recommending remedies for weaknesses in the student attainment rates for educational quality.
Sociocultural theory applied to collaborative learning in writing strategies

Teaching writing is a crucial skill in promoting learners’ English proficiency. Writing needs a sequence of stages, done step by step. These activities were used with English Department students. Although they followed some sequences of writing stages, they had low proficiency in writing paragraphs. In order to acquire productive skills in writing, this study undertook qualitative research on applying sociocultural theory to collaborative learning in writing strategies. The research objective was to describe implementing sociocultural theory to collaborative learning in writing strategies. This research used a qualitative method that describes the application of sociocultural theory to collaborative learning in writing strategies. This study also used observation to reveal the application of sociocultural theory to collaborative learning in writing strategies. The result shows that sociocultural theory has made a great impact on the learning and teaching profession as it is included in the process of teaching and learning writing, and this skill is regarded as social process. Further, the perspective of sociocultural becomes the foundation in providing scaffolding in teaching writing. Likewise, this learning process also involves collaboration in writing lessons, as well as lecturers’ reflections on their teaching and learning activities in the classroom, as much as the teaching and learning writing as social process.
Power-Efficient Reversible Logic Design of S-Box for N-Bit AES

S-Box is the main core of AES. The S-Box provides confusion capability for AES. The S-Box used in standard AES is static throughout the encryption process. But nowadays, cyberattacks are continuously developing and power attack is one among them. The main aim of this work is to provide higher security for several applications over the Internet by enhancing the overall strength of the existing Advanced Encryption Standard algorithm (AES). Paper explores the usage of new logic family called reversible logic gates as a part of quantum technology to mitigate power attacks. As S-Box involves heavy mathematical calculations, it dissipates more power which may lead to power attacks. To improve the security of the conventional AES, reversible logic is used to build S-Box, as reversible gates offer ideally zero internal power dissipation. This work discusses the implementation details of S-Box using reversible logic and does the performance analysis w.r.t hardware requirements and power.
Moving Object Extraction Method Based on Background in Sports Video

In order to conform to the psychological needs of the audience, director usually reproduces the wonderful lens in the form of slow motion, so the slow motion of sports video contains a wealth of semantic information, and advanced semantic content analysis of slow motion is extremely import. Due to the relative motion between the camera and monitoring scene, background also moving while targets moving between two frames. To solve this problem, this paper continued using the method of background compensation for moving object extraction. In order to transform motion background into a static background, a new frame is filled between two frames, and then frame difference method is used to extract moving targets. In the process of background compensation, this paper remove most of the feature points according to the characteristic of long-distance between harris angular which are line in the tennis court, thus the amount of computation is reduced when feature points matching. When the feature points selected for parameter estimation, relevant domain knowledge is used, and the speed of the algorithm is improving.
Optical Cryptosystem Using Chaotic/Hyperchaotic System

The security is a significant topic in transmission and management of pictures, while encryption is an effective method for protecting the secret images. In recent years, the chaotic/hyperchaotic system has been thoroughly developed in image encryption area due to its high randomness. In this chapter, the optical cryptosystems based on chaotic/hyperchaotic system are expressed in detail. We starts with the brief introduction of various chaotic/hyperchaotic systems, then several optical cryptosystems based on the chaotic/hyperchaotic map are expressed in detail. Some experiments are made to verify the security and robustness of these schemes.
Computational Intelligence for Technology and Services Computing Analysis of Sentiment Analysis Techniques

Emergence towardsBharti, Puja valuingVerma, Amit Kant customer reviews andRaj, Ravi theirKrishna, Gopal opinions is the prime propelling factor for any exploring business. Electronic commerce has clinched the world, and the majority preferring to buy products through these websites online. Due to the increase in demand for e-commerce with customer’s preference towards online purchasing of products over physically moving from shop to shop (offline purchasing), there is the huge amount of information being shared to and fro. The e-commerce websites are loaded with immense volume of data and customer reviews thus being generated. This huge volume of data is in its diversity and its structural randomness. The customers face difficulty in precisely finding the review for a particular feature of a product that they intend to buy. Also, there are mixtures of positive and negative reviews thereby increasing the complexity for customers to find a cogent response. So to avoid this confusion and make this review base more transparent and user friendly, a technique to extract feature-based opinion from a diverse pool of reviews and processing it further to segregate it with respect to the aspects of the product and further classifying it into positive and negative reviews using machine learning-based approach. The analysis of the data generated in huge amount holds the prime centred topic, underlying data analytics. This paper proposes the study and analysis of obtaining the best methodologies on sentiment analysis of consumer reviews in context to the features of a product. The system aims at providing a summary that represents the extent to which the consumers who had already bought the particular product were or were not satisfied with the specific feature of the product. Due to this sentiment analysis, there is a feedback environment being generated for helping customers buy the right product and guiding companies to enhance the features of product suiting consumer’s demand.
Development of Tangible Controller for Ikebana Training System

This paper proposes a training system for Ikebana (Japanese flower arrangement) in an Augmented Reality (AR) and a 3D computer graphics space using a tangible controller. We propose a stick device that represents a flower stalk and a trackpad that represents a Kenzan used to support flower stalks in an actual flower arrangement. We show that our training system provides an intuitive and straightforward interface for users to practice Ikebana. The paper also describes the implementation of the system and discusses the potential future uses of these devices.
Weighted utility based recommender for e-procurement in handicraft communities

In this paper, we would like to assess the positive impact of the recommendation process during the professional activities of business actors. We are interested specifically in the improvement of the economic life of the handicraft women from emerging countries. To this end, we introduce a utility based recommender dealing with the procurement opportunities. Actually, we proposed a utility function which takes into account the weighted preferences and expectations of final users. The system is evaluated based on the gain to obtain if the proposed recommendations are adopted in addition to the satisfaction level of the final users.
Video Super-Resolution Using Wave-Shape Network

Video super-resolution (VSR) aims to restore a high-resolution (HR) image from multiple low-resolution (LR) frames. Previous works deal with inputs LR frames by stacking or warping and only use single scale features for reconstruction. Most of them didn't consider fusing multi-scale spatial and inter-frame temporal information, which may result in loss of details. In this paper, a novel architecture named Wave-shape network is proposed, which is designed to treat each frame as a separate source of information and fuse different temporal frames through a multi-scale structure. This fusion strategy enables us to capture more complete structure and context information for HR image quality improvement. We evaluate this model on Vid4 dataset and the results show Wave-shape network not only achieves significant improvement in vision but also obtains much higher PSNR and SSIM than most previous VSR methods.
Two Ways for the Automatic Generation of Application Ontologies by Using BalkaNet

This article presents two methods for the automatic generation of application ontologies from the multilingual BalkaNet WordNets Web ontology language (OWL) representation. Both proposed methods are applied on the BalkaNet WordNets ontology for the Serbian language (SerWN). The first one uses only the SerWN, both for generating class hierarchy and instances of classes, while the other method combines the SerWN with a domain ontology. The first method was used to automatically generate the FoodOntology, whereas the second method to generate the ontology of rhetorical figures tropes. Preliminary evaluation results corroborate the soundness of the approach. Since BN consists of individual WNs for five Balkan languages and Czech, the methodology presented in this article can also be used for all these languages. The first method can also be used for other domains.
A Zoning Search Strategy for Differential Evolution Variants

Computational time and solution precision are two major concerns in the evolutionary computation (EC). To save the computational time, various high-performance computing techniques have been successfully employed to help meta-heuristic algorithms. Moreover, most of previous studies have focused on the performance improvement of meta-heuristic algorithms for improving the solution precision. However, the use of high- performance computing techniques and the improvement of algorithm performance do not mean that meta-heuristic algorithms can locate a promising region in some cases due to their search capability limitation. Therefore, converting improved computation power indirectly into algorithm search capability is a vital task. To achieve the above goal, a zoning search (ZS) strategy is proposed in the current work. In ZS, some variables of an optimization problem are selected randomly, and then they are divided into some segments, i.e., the entire search space of the optimization problem is divided into some subspaces. Finally, a selected meta-heuristic algorithm is used to find a satisfactory solution in each subspace. The effectiveness of ZS is demonstrated on IEEE CEC2014. Results suggest that ZS is a highly competitive approach to solve complex optimization problems.