Reviewerly/exHarmony huggingface dataset:
An Object Detection Algorithm Based on Improved Network Structure

As the state-of-the-art in single-stage detector, Yolov3 has been widely used in pedestrian detection, road signal sign detection, face detection and other fields. Although Yolov3 has higher performance compared with other singlephase detection models, its detection speed still has greater room for improvement compared with some lightweight network models. To further improve the detection speed of Yolov3, we have taken the following measures. First, we changed the original feature extraction network to extract feature images by using depth separable convolutions and convolution kernels of different sizes. Then we changed the measurement criteria of the original bounding box. And in order to predict the position of the candidate box more accurately, we used the improved NMS algorithm and upgraded the loss function of Yolov3. Finally, we trained and tested our model using the datasets VOC2007 and COCO2014 under the Tensorflow. Experimental results demonstrate that the presented method can effectively improve the speed of detection while ensuring the accuracy of model detection.
Patient assessment using computer games in rehabilitation

This article focuses on the issue of rehabilitation of patients with motor impairment using computer games. The rehabilitation industry has been experiencing a boom in recent years, coupled with the growing popularity of virtual reality technology, a drop in prices for these technologies and the expansion the entertainment industry in the form of computer games. We are trying to create a computer game system designed for home rehabilitation of patients. The aim of this article is to devise with a subset of motor impairment assessment of patients with the intention of adapting the demands of computer games based on the physical abilities of a particular patient. We are all trying to keep this system at the level available to a regular home user.
Simple smart glasses based on microcontrollers as money detector of nominal and authenticity

Abstract This study aims to make a detector for the nominal value and authenticity of money (rupiah currency). The working principle of this tool is as follows: UV LED function to provide lighting to objects (money). The light will circulate hidden colours in original money so that you can differentiate original or fake money (money whose authenticity is doubtful). The white LED provides lighting for the object, then the light is received by the TCS3200-DB colour sensor to read its Red, Green and Blue (RGB). Furthermore, the RGB value will be processed by Arduino Mega 2560 to be compared with the RGB values in the program. The mini DF-Player functions to play sound according to the sound that has been recorded with the program. Testing the system is done to determine the percentage of success of the system in distinguishing between original money and the authenticity of money is doubtful. Testing of 5 (five) nominal 2016 emission rupiah, namely 2,000-rupiah (60%), 10,000-rupiah (100%), 20,000-rupiah (80%), 50,000-rupiah (100%) and 100,000-rupiah (66%), so the average percentage of success of the tool is 81.2%.
Weibo user attribute analysis method based on multi-feature

Abstract In view of the deficiency of domestic Weibo user attribute analysis, the imperfection of Weibo feature extraction and the problem that the classification accuracy needs to be improved, a Weibo user attribute analysis method based on multi-features is proposed. This paper first uses the word2vec model to build text features from Weibo content, then constructs Weibo user features from Weibo information and user information, and finally sends multi-feature sets into the improved three-tier stacking model to build Weibo user attribute analysis model. The experimental results show that this method has better classification effect than the text-based classification method and the traditional stacking model.
Optimization of Secret Key using cuckoo Search Algorithm for ensuring data integrity in TPA

Optimization plays an important role in many problems that expect the accurate output. Security of the data stored in remote servers purely based on secret key which is used for encryption and decryption purpose. Many secret key generation algorithms such as RSA, AES are available to generate the key. The key generated by such algorithms are need to be optimized to provide more security to your data from unauthorized users as well as from the third party auditors(TPA) who is going to verify our data for integrity purpose. In this paper a method to optimize the secret key by using cuckoo search algorithm (CSA) is proposed.
Reinforcement learning as a service

This paper describes an application developed in Python that implements reinforcement learning as a service and is ready for deployment on the cloud. The application can be used for solving various types of problems that require reinforcement learning. The application supports the interaction of an agent (e.g. a robot) with an environment in which the agent reaches different states and is able to perform actions (e.g. say a joke). In each state, the agent must select the action most appropriate in the situation, such action selection is provided by our application.
Practical Repetition-Aware Grammar Compression

The goal of grammar compression is to construct a small sized context free grammar which uniquely generates the input text data. Among grammar compression methods, RePair is known for its good practical compression performance. MR-RePair was recently proposed as an improvement to RePair for constructing small-sized context free grammar for repetitive text data. However, a compact encoding scheme has not been discussed for MR-RePair. We propose a practical encoding method for MR-RePair and show its effectiveness through comparative experiments. Moreover, we extend MR-RePair to run-length context free grammar and design a novel variant for it called RL-MR-RePair. We experimentally demonstrate that a compression scheme consisting of RL-MR-RePair and the proposed encoding method show good performance on real repetitive datasets.
Semi-Automatic Geometric Normalization of Profile Faces

This paper proposes a correlation point matching approach, i.e. an efficient methodology for applying geometric normalization for profile face images. This method is used to increase accuracy without imposing a significant increase in face matching computational time when using different feature descriptors. In our work, several such descriptors are tested to compare the accuracy with which low level facial features (edges), useful for profile face image geometric normalization, are extracted. Hence, we determined the most efficient normalization approach that does not substantially increase computational time. Experimental results show that the use of eigenvalues produces a higher than average edge point count, while having a lower increase in computational complexity compared to other similar algorithms. Then, the extracted features are matched using the random sample consensus algorithm (RANSAC). Next, the rotational angles between the pairs of features are calculated and averaged to yield the angle of rotation necessary to achieve a proper profile face image normalization representation. After applying our proposed approach to a deep learning-based profile face recognition algorithm, an increase of 7.2% accuracy is achieved when compared to the baseline (non-normalized profile faces). To the best of our knowledge, this is the first time in the open literature that the impact of automated profile face normalization is being investigated to improve deep learning-based profile face matching performance.
Research on Task-oriented Dialogue Based on Modified Transformer

Abstract The traditional end-to-end task-oriented dialogue models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. But in the case of large amounts of data, there are many types of questions. It performs poorly when answering multiple types of questions, memory information cannot effectively record all the sentence information of the context. In view of the above this, this article uses a modified transformer model to overcome the problems mentioned in dialogue tasks. Transformer is a model constructed using attention mechanisms, which completely discards the method of RNN (recurrent neural networks), and its structure includes two sub-parts of Encoder and decoder. It uses residual network, batch normalization, and self-attention mechanism to build the model structure, uses Positional Encoding to capture sentence information, which can speed up model training convergence and capture Longer sentence information. In this paper, we modified the activation function in the transformer and use label smoothing to optimize the training to make the model’s expressive ability better than previous.
Quantitative Variants of Language Equations and their Applications to Description Logics

Unification in description logics (DLs) has been introduced as a novel inference service that can be used to detect redundancies in ontologies, by finding different concepts that may potentially stand for the same intuitive notion. Together with the special case of matching, they were first investigated in detail for the DL $${\mathcal{FL}}_0$$ , where these problems can be reduced to solving certain language equations. In this thesis, we extend this service in two directions. In order to increase the recall of this method for finding redundancies, we introduce and investigate the notion of approximate unification, which basically finds pairs of concepts that “almost” unify, in order to account for potential small modelling errors. The meaning of “almost” is formalized using distance measures between concepts. We show that approximate unification in $${\mathcal{FL}}_0$$ can be reduced to approximately solving language equations, and devise algorithms for solving the latter problem for particular distance measures. Furthermore, we make a first step towards integrating background knowledge, formulated in so-called TBoxes, by investigating the special case of matching in the presence of TBoxes of different forms. We acquire a tight complexity bound for the general case, while we prove that the problem becomes easier in a restricted setting. To achieve these bounds, we take advantage of an equivalence characterization of $${\mathcal{FL}}_0$$ concepts that is based on formal languages. Even though our results on the approximate setting cannot deal with TBoxes yet, we prepare the framework that future research can build on.
Sentiment Orientation Classification Method and Its Application

The traditional text categorization method, in the word segmentation stage, focuses on the vocabulary discrimination ability, ignoring the people's viewpoints and tendencies from the text, resulting in limited performance in terms of classification accuracy. Because the user's expression has strong correlation with his own emotions, using the scores and comment data of Douban movies and TV dramas provided by Datatang as the corpus, the feature word extraction methods such as chi-square test and information gain are adopted. Combining naive Bayes, N-Gram and support vector machine text classification methods, construct an emotional orientation classification model and experiment with the actual movie forum review data. The results show that the improved classification method can achieve higher classification accuracy, which makes it suitable for a large number of Chinese text emotion classification application systems.
Digital Image Processing and Recognition in Industrial and Public Environments

Because the internet broadcasting, most photos or video streams need copyright protection or forgery detection. Nowadays, the metadata tries to embed and preserve copyright data and general information. The ownership metadata should never be removed, for an efficient protection, but practically it could be relatively easy extracted. This research proposes, based on a comparison between metadata and watermarking methods, a watermarking framework that inserts visual and hide information into digital images, instead of metadata protection. The visible watermark inserted into the host image could be removable or not, based on the objective or non-bijective embedding watermark function. A removable watermark allows the far end receiver to eliminate the visible watermark, whether it uses a framework that uses the invers embedding function. In this way, only the controlled receivers could profit by the clean photos/video stream. More of this, the hidden watermark could embed typical information that identify the owner.
Automatic Parameter Tuning in Aluminum Extrusion Based on Genetic Programming

This work applies artificial intelligence in the aluminum extrusion process for automatic setting the ram speed of a machine according to the requirements of the industry. The automatic parameter tuning system computes the ram speed with the equation created by genetic programming (GP). In model evaluation, MAE and MAPE are used to measure a predictive performance of the models. In addition to GP, linear and polynomial regression are used to generate the automatic parameter tuning model for comparing a performance with GP. The experimental results on the test set show that GP performs the best in predictive performance with 0.130 of MAE and 4.22% of MAPE. Finally, the GP model has been developed as a software to calculate the ram speed and display it on a screen. This system will help users who are not proficient in aluminum extrusion or new users to have better control of production.
CONCEPTION OF INTERMEDIAL COMMUNICATION ACTIVITY IN THE SYSTEM OF VOCATIONAL TRAINING OF FUTURE TEACHERS OF ARTISTIC SPECIALTIES

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Indonesian parent’s role of early literacy program

Abstract The research was based on the application of the curriculum condition and learning in the school of early childhood education. Psychic development, appropriate early childhood does not yet have enough readiness to receive learn to read. Students are not yet mature enough to accept the burden of the material symbols of that language. However, from previous research conducted, it found that 98% of the school taught reading for their students. It is done by a variety of reasons — for example, the demands of the parents/guardians of students, school level demands of the next, commercial purposes, and so on. On the other hand, the students were during the golden age to absorb a variety of information that can be relied upon for readiness and maturity of learning was going to do later on. Based on the research results that are relevant to this study, it was found that children who have a parent with optimizing the opportunity receive language stimulation well will grow into adults who are capable in terms of linguistic and have a tendency to more easily understand the readings five times greater than adults who in his childhood did not receive similar stimuli. The research of applying research methods combined concerning the development model Akker consists of four steps of analysis. At this stage of the development of the early models used two students who have in common the maturity to learn from three schools with different economic and social conditions for the trial subject or the respondent. Then proceeded on a limited testing phase is done with 30 students from five different schools. Extensive test phase carried out in the application of the 40 students from ten different schools. Rating (sig) is less than 0.05 (α), then Ho is rejected. This means that there is a difference of values on the Group 1 (Pre-order) and group 2 (Post). If seen from the average, then the average group 2 (post) = 77.9410 is higher than the average Group 1 (pre) = 59.7450. Then the values of Group 2 (position) better than group 1 (pre). As a result, an increase in the number of students from grades pre to post.
Efficient Cross Validation for SVR Based on Center Distance in Kernel Space

Cross validation (CV) is widely used to find the optimal parameters of the support vector regression (SVR) model. Regarding the conventional CV method, the optimal model parameters may be affected when the training set is randomly split into k disjoint folds. In the paper, an efficient CV based on center distance in kernel space is presented. Data splitting is based on the distance between the sample and the center point in the kernel space. Simulation experiments results show that the proposed CV method makes the selection of optimal model parameters more reasonable and improves the generalization ability of SVR model.
Estimating Solar Irradiance at the Earth’s Surface Using Remote Sensing: Case of Algerian Oranie’s Region

In all of the solar energy studies the solar radiation data is very important input, but it is not possible to measure the solar radiation on each zone. In order to get this spatialised data over a large area, the aim of this work is to estimate Global Horizontal Irradiance (GHI) at the earth’s surface using remote sensing data which show a very high spatial and a sufficient temporal resolution compared to ground station data. This paper introduces the HELIOSAT method applied to derive GHI from satellite images and shows the adaptation to apply the method for Landsat8 images. To analyse the results provided by the implemented methodology a validation against measurements has been made.
Melajah pupuh BALI application “anggita” as a medium of learning in multimedia

Abstract With the industrial revolution 4.0, technology is important to be utilized in various fields. Technology is a medium to support education especially at elementary school level. Technology in the cultural sphere is an important thing in the current era, such as the use of software application for preserving Balinese culture. This application teaches how to sing the Balinese poems correctly for beginners. There are three buttons that go into pupuh Ginada, Ginanti, Pucung and are supported by video. As sustainable development, the role of the AngGita application is relevant to be applied in the field of multimedia learning, because it increases the effectiveness of delivering information to students. Since, this application was published on July 6, 2019, it ranked 4.6 within 5 months and has been downloaded more than 100 downloads with the Android version 8.1. Conceptually, this application is in line with governor regulation number 80 of 2018.
The Analysis on Student’ Psychologic Status of Online Learning under Extraction Model from Computer Face Features

Abstract The research aims to analyze students’ psychological mood during online study and enhance the efficiency of learning. In this research, face features are extracted by LBP (local binary pattern) algorithm based on face feature recognition. The facial feature points are tracked by CLM (constrained local model) algorithm, and the face images are normalized by AdaBoost algorithm. In the end, the fatigue level of learners is calculated by P80 method in PERCLOS criterion to verify the accuracy, recognition rate and error rate of the model. The results indicate that through the recognition of the faces in a complex background, the accuracy of the mode is 95%, the recognition rate is 95.53% and the error rate is only 0.53%, the range of aspect ratio in the human eye image is 0.22 ≤ λopen ≤ 0.27, 0.05 ≤ λclose ≤0.1, and learner’s level of excitement can be estimated according to the range of f. Therefore, this algorithm model can extract learner’s facial features very well and show a good result. Detecting the degree of learner’s excitement accurately can provide an important fundamental for the realization of online learning emotion detection system which also has guiding significance for the development and popularization of the networked education.
Evaluating Huge Matrix Multiplication on Real Hadoop Cluster

The matrix multiplication is the dominant of many business applications such as calculate profits based on price and quantity matrices, predict various weather phenomena following the study of Markov Chains, create Similarities table in a recommendation system and so forth. Some real word problems, matrices are very large in size that is very time-consuming operation even though there are execution strategies on regular matrices and sparse matrices. This study focuses on a recently parallel matrix multiplication approach using MapReduce in big data by tuning Hadoop configuration parameters to improve performance. The results show that changing the number of mappers and reducer numbers would reduce execution time. However, it only decreases to a certain value, if the number of mappers and reducers continues, the execution time does not decrease. In addition, increasing the DataNode is not much impact on reducing matrix multiplication time.
First Study for Ramp Secret Sharing Schemes Through Greatest Common Divisor of Polynomials

A ramp secret sharing scheme through greatest common divisor of polynomials is presented. Verification and shelf correcting protocols are also developed. The proposed approach can be implemented in a hybrid way using numerical and symbolical arithmetic. Numerical examples illustrating the proposed sharing schemes are also given.
Data Analytics for Health-Care Risk Predictions based on Ensemble Classifiers and Subjective Projection: PLENARY TALK

Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making in health care. The challenges in big data analytics are the high dimensionality and complexity in data representation. Granular computing and feature selection are among the challenge to deal with big data analytics that is used for Decision making. We will discuss these challenges in this talk and provide new projection on ensemble learning for health care risk prediction. In decision making most approaches are taking into account objective criteria, however the subjective correlation among different ensembles provided as preference utility is necessary to be presented to provide confidence preference additive among it reducing ambiguity and produce better utility preferences measurement for good quality predictions. Most models in Decision support systems are assuming criteria as independent. Different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to data analytics due to preprocessing and normalization processes which are expensive and difficult when data sets are raw and imbalanced. We will highlight these issues though project applied to health-care for elderly, by merging heterogeneous metrics for providing health care predictions for elderly at home. We have utilized ensemble learning as multi-classification techniques on multi-data streams that collected from multi-sensing devices. Subjectivity (i.e., service personalization) would be examined based on correlations between different contextual structures that are reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations. We outline issues on Virtual Doctor Systems, and highlights its innovation in interactions with elderly patients, also discuss these challenges in granular computing and decision support systems research domains. In this talk I will present the current state of art and focus it on health care risk analysis with examples from our experiments.