Reviewerly/exHarmony huggingface dataset:
Learning to synthesize faces using voice clips for Cross-Modal biometric matching

Cross-Modal biometric matching has been a scarcely explored field but carries several important applications and aims to further secure the currently existing security systems. In this paper, a framework for cross-modal biometric matching is presented, where faces of an individual are generated using his/her voice clips and further the synthesized faces are tested using a face classification network. Generative Adversarial Network (GAN) has become a recent trend in deep learning and has been widely used for image synthesis. We explore the advancements of Convolutional Neural Network (CNN) for feature extraction and generative networks for image synthesis. In the experiment, we compare the performance of Variational Autoencoders(VAE), Conditional Generative Adversarial Networks(C-GAN) and Regularized Conditional Generative Adversarial Networks(RC-GAN) and show that RC-GAN that is C-GAN with a regularization factor added to its loss is able to generate faces corresponding to the true identity of the voice clips with the best accuracy of 84.52% while VAE generates a less noise prone image with the highest PSNR of 28.276 decibels but with an accuracy of 72.61%.
PRAXEOLOGICAL COMPONENT OF HIGHER EDUCATION IMPROVEMENT BASED ON THE NETWORK INTERACTION OF UNIVERSITIES WITH NON-EDUCATIONAL ORGANIZATIONS

Changing requirements for Russian universities’ activities and outcomes have actualized the problem of increasing the value of the praxeological component of higher education. The article aims to solve this problem on the basis of a praxeological approach, using documentation and literature analysis, questionnaires, and surveys. The ratio of fundamental and praxeological components in higher education in historical retrospective and in modern universities is shown. The decrease in the level of the praxeological component is justified as an urgent problem of pedagogy and educational practice. The problem can be resolved by using an interdisciplinary approach comprising the knowledge of pedagogy, psychology, sociology, economics, and by improving the regulatory documentation, using the potential of network interaction between educational and non-educational organizations.
Evaluation Method of Operation State in Overweight Rejection based on Unary Linear Regression Model

According to the statistical analysis of different states of overweight cigarette rejection, this paper propose a state evaluation method based on rejection rate of production shift. This paper uses a linear return equation to fit continuous overweight rejection number in time increment, and identify the optimal slope range. By comparing the slope range, it can determine whether the status of overweight rejection of cigarette equipment is abnormal or not, and the relevant factors within the time range of abnormal occurrence are automatically correlated to support the user to carry out anomaly analysis. Through the use of this method, users can get running status of the overweight rejection equipment in time, so they can better guarantee the weight control of product.
The Method for Measuring Center Coordinates of the Out-of-Focus Spot

Miniaturization of the device has become the key of precision measurement system and spot center detection plays a very important role in measuring rotation angle. For the imaging problem of the light source in the image sensor when measuring the rotation center coordinate, a method using the out-of-focus image of the spot to make the measuring device smaller was proposed. Firstly, we preprocess the out-of-focus image. Then the center coordinates of the spot are obtained by using the least square circle fitting method, with the light source rotates, we will obtain a series of center coordinates of the spot. Lastly, the least square circle fitting method is used to fit the center coordinates of the rotating spot to find the center coordinates of rotation axis. Experimental results show that the error between the ideal rotation center and the center obtained by this method can less than 0.7 pixel.
Controlled Vocabularies versus Social Tags: A Brief Literature Review

The present study is a literature review, based on the comparative study between controlled vocabularies and social tags from various perspectives. Critical comments of experts on similarities, co-relationship, uses, trends between controlled vocabularies and social tags have found their manifestation in this literature review. Authors made an effort to portray the overall picture of previous research done on these areas.
Perancangan Backup Link Menggunakan Metode HSRP (Hot Standby Router Protocol) Dalam Penyediaan Layer-3 Redundansi

With the increase of internet user today, if it only has one router, when the router experiencing a network failure, then the internet cannot be used. To overcome this problem, the HSRP (Hot Standby Router Protocol) method by creating one active link and one backup link has been applied in the previous research. In this research will be applying the HSRP, VTP (Vlan Trunking Protocol), InterVLAN, and VLAN (Virtual Local Area Network) as a comparison. The testing parameter in this research will be using packet loss and throughput. From the result of testing that has been done, it shows that by applying the HSRP method, it gives a network availability with the packet loss of only 0,32%, and throughput of 1,159Mb/s (95,58%) from using 100 PC.
Internet of things in designing tangible interfaces for children with special needs

The Internet of Things (IoT) is a technology that allows physical objects to be interconnected through the Internet. The growth in technology means that it now plays an important role in the transformation of environments so that they are better able to benefit children with special needs. Connectivity between different elements is therefore of great interest in developing prototypes oriented to storing information in the cloud or connecting physical objects.
Feature Selection Approach for Improving the Accuracy of Software Bug Prediction

We recently noticed the advancement and growth in the field of artificial intelligence and in its various branches such as Machine Learning (ML) and Deep Learning in various vital fields such as robotics, smart cars, smart cities, health care, software engineering and many other fields. Software bug prediction are one of the most important ML uses in software engineering. In addition, the feature selection is one of ML methods that aim to reduce a feature set that are used for building models. In this paper, we propose to use the Chi-Square feature selection method to calculate features importance, then to build a ML models, first by using top ten important features and second by using top five important features, based on three of well-known ML classifications algorithms, Support Vector Machine, Naïve Bayes and Linear Discriminant Analysis, with adding and exploring more about the effeteness of new metric of code smell intensity, the performance results of our approach against baseline achieved an improvements as average accuracy among nine datasets reaching up to 5.12%, 4.15% and 1% on the NB, SVM and LDA classifiers respectively.
Action Recognition in Videos with Temporal Segments Fusions

Deep Convolutional Neural Networks (CNNs) have achieved great success in object recognition. However, they are difficult to capture the long-range temporal information, which plays an important role for action recognition in videos. To overcome this issue, a two-stream architecture including spatial and temporal segments based CNNs is widely used recently. However, the relationship among the segments is not sufficiently investigated. In this paper, we proposed to combine multiple segments by a fully connected layer in a deep CNN model for the whole action video. Moreover, the four streams (i.e., RGB, RGB differences, optical flow, and warped optical flow) are carefully integrated with a linear combination, and the weights are optimized on the validation datasets. We evaluate the recognition accuracy of the proposed method on two benchmark datasets of UCF101 and HMDB51. The extensive experimental results demonstrate encouraging results of our proposed method. Specifically, the proposed method improves the accuracy of action recognition in videos obviously (e.g., compared with the baseline, the accuracy is improved from 94.20% to 97.30% and from 69.40% to 77.99% on the dataset UCF101 and HMDB51, respectively). Furthermore, the proposed method can obtain the competitive accuracy to the state-of-the-art method of the 3D convolutional operation, but with much fewer parameters.
Aplikasi Smartphone GIS (Geografik Information System) Pencarian Lapangan Futsal Daerah Tangerang Berbasis Android

Geographyc Information System (GIS) is an information system that is applied to store, recall, enter, analyze, process, and produce data in the form of geographic data, used for decision making in support of a system development plan. The problem of finding information to find out the location of various places in an area with the help of smart phones. The use of smart phones in a growing community, then the idea arose to create an application called Find Your Futsal, which is an application about futsal field information based on the Geographyc Information System in the Tangerang area. Futsal has many benefits for the surrounding community. They can use the futsal field to support various futsal activities. The design method of this GIS application research application uses the SDLC (System Development Life Cycle) research method. Started designing the displays in the application, designing the database used in making the application. Next start the application by coding with a predetermined programming language using the Android Studio application. Furthermore, it is compiled and implemented on Android-based mobile devices. The application of the results of this study is a geographic information system application futsal field in the city of Tangerang that is simple and easy to use. As well as GPS and maps as features in it.
Semantic Monocular Depth Estimation Based on Artificial Intelligence

Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.
A Simple Method for 3D Thyroid Reconstruction from 2D Ultrasound Slices

The goal of this paper is to describe a technique for the thyroid 3D model reconstruction from 2D images provided by an ultrasound system using image processing and pattern recognition. We investigate if various image processing techniques can solve the problems of semi-automatic thyroid reconstruction. The techniques involved included methods of approximation like Sum of Squared Differences (SSD) and deconvolution algorithms like point spread function (PSF). This approach has several benefits including 3D models of human organs that can be used in the teaching process, further medical investigations or remote sites evaluations.
Residual Self-Attention for Visual Question Answering

Over these years, many attention mechanism-based neural network models have been put forward in the visual question answering (VQA) by some researchers. However, the semantic gap between the multimodality cannot be bridged simply, and the combination of the questions and the images may be arbitrary, which both hinder the jointly representing learning and make the VQA model easy for overfitting. Therefore, in this paper, a multi-stage attention model has been put forward, that is, for the image, the bottom-up attention and residual self-attention for the image itself and the question-guided double-headed soft (top-down) attention method were used to extract the image features; for the question, the pre-trained GloVe word embeddings were used in this paper to represent the semantics and the GRU was used to encode the questions into fixed-length sentence embedding; finally, the image features were fused with the question embedding through the Hadamard product and then input into the sigmoid multi-classifier. The experiment showed that with the model the overall accuracy of 67.26% in the VQA 2.0 dataset was finally achieved, higher than that of other advanced models.
Visualisasi Gedung Sekolah 3D dengan Konsep Virtual Reality Berbasis Android

Promotion and introduction of school buildings still uses media such as brochures or pamphlets, because it requires software that can introduce and provide information about specific school buildings in SMK 25 Jakarta. This study aims to provide an introduction to the Virtual Reality 25 Jakarta Vocational High School building using the VR Box as a medium of information about the rooms to prospective new students. This application utilizes the method of research and development (R&amp;D) with the ADDIE model, this model serves to carry out periodic assessments in each phase so that the resulting product becomes valid. The validation test results concluded that the system is valid. Testing the level of user satisfaction after using the Virtual Reality application SMKN 25 Jakarta can be concluded that the average user gives a value of 3.4 or enough on the satisfaction of the application menu display, and gives a value of 3.05 on the satisfaction of virtual mode.
Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval

Most Cross-modal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving time-consuming training procedures for large-scale dataset. To tackle these issues, we first formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to large-scale cross-modal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class labels, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modality-specific projection function learning and an efficient closed-form solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the state-of-the-arts.
Personalized Neural Usefulness Network for Rating Prediction

Reviews are crucial to rating prediction on e-commerce websites such as Amazon and Yelp. They reflect user preferences and item properties. Considering not all parts of the reviews are equally important for rating prediction, some works select the more informative words of reviews, others focus on finding the more useful reviews for each user and item. However, on the one hand, the meaning of words may be incomplete, further they may distort the original meaning of sentences, on the other hand, for "the more useful" reviews, they still contain more useful parts and less useful parts for rating prediction. To address these problems, we propose to model reviews in a view of sentence usefulness, since different sentences have varied importance for reviews modeling. In addition, the preferences of a user may vary with different item properties, it leads to the inconstant usefulness of a user sentence for different items, and vice verse. Hence, in this paper, we show a Personalized Neural Usefulness Network (PUN) to capture the varied sentence usefulness of reviews for rating prediction. Expensive experiments on benchmark datasets demonstrate that PUN significantly outperforms the state-of-the-art baseline models. In the meantime, according to personalized sentence usefulness, PUN also attains highly-useful sentences to better understand the varied user preferences and item properties.
Analysis of Challenges facing ICT integration in managing Public Secondary Schools: A Comparative Study of Day and Boarding Secondary Schools in the South Rift Region, Kenya

The uptake of information communication technology (ICT) by secondary schools is beset by a complex of challenges that are not clearly understood and documented. In order to facilitate effective and efficient implementation of digitalization in schools in Kenya, there is need to map out the diversity of challenges that bedevil its adoption. The purpose of this study was to analyze the challenges facing integration of information communication technology (ICT) in the operations of public day and boarding secondary schools from the south rift region of Kenya. The objectives of the study were to; assess the challenges facing ICT integration and compare the levels of ICT integration in boys, girls and co-educational secondary schools from the south rift region of Kenya. The study adopted the descriptive survey research design. The target population for the study comprised of all the 141 teachers from the public secondary schools in the south rift region of Kenya enrolled in the Strengthening of Mathematics and Science in Secondary Education (SMASSE) program. The study employed census sampling technique since the target population was small, easily accessible and manageable. Data was collected by means of a questionnaire. Data analysis was conducted using the descriptive and inferential statistics with the aid of Statistical Package for Social Sciences (SPSS) version 21.0. The study tested the hypothesis that there was no statistically significant difference in challenges facing integration of ICT in boys, girls and co-educational secondary schools from the south rift region of Kenya. To test this hypothesis the One way Analysis of Variance (ANOVA) statistics was computed which yielded p-value = .000 which was less than the alpha value α &gt; 0.05 indicating that the differences in challenges facing ICT integration in boys, girls and co-educational schools were statistically significant. Therefore the null hypothesis was rejected. This led to the conclusion that challenges facing ICT integration from the three categories of schools were significantly different. From the findings of the study, it is recommended that there is need strengthen in service training of teachers in ICT and perform widespread upgrade of ICT software since these were the most serious challenges that faced integration of ICT in schools.
PROJECT-BASED LEARNING APPROACH TO IMPROVE STUDENTS’ WRITING SKILL

This research was held during the internship at SMPK BPK PENABUR Cimahi by taking the material taught in English lesson for 8th grade students at first semester. The objective of this research is to find out the improvement of writing imperative sentence using Project-Based Learning approach. The use of Project Based Learning approach suited with the Kurikulum 2013, which has been used by this school since 2013. The method used Classroom Action Research (CAR), which held on two cycles for seven meetings. Each cycle covered the step of planning, acting, observing and reflecting. The data was collected from test and observation. The data from test showed that treatments are successful in improving writing imperative sentences skill for the students. The improvements covered two points, (1) improvement on their mean score. Mean score of pre-test was 54.967, mean score of post-test1was 64.467 and mean score of post-test2 was 80.033. (2) Improvement of their behaviors. Their activeness individually and participation in the group indicated that the Project Based Learning approach was the right approach to be applied in the classroom teaching. Keywords: CAR, Project-Based Learning Approach, Writing Skill
PERBANDINGAN COBIT 2019 DAN ITIL V4 SEBAGAI PANDUAN TATA KELOLA DAN MANAGEMENT IT

Cobit 2019 and ITIL V4 are tools and guidelines that can be used to form an IT governance and management framework. There are several differences between the two, although they are also common and widely used for IT management and management implementation. Both are also the latest versions of the publications of the organizations that created them. With the help of Cobit 2019 or ITIL V4, organizations can be helped in implementing IT management and management. Not infrequently an organization feels confused in choosing tools or frameworks that will be used as the basis for implementing IT management and management. The literature study conducted aims to discuss the differences between the two as a guide to IT management and management . Keyword : ITIL, COBIT, IT Governance, IT Management DOI : http://doi.org/10.5281/zenodo.3631057
Analysis of Some Linear Dynamic Systems with Bivariate Wavelets

There are many statistical methods related to the forecasting of time series without any input variables such as autoregressive integrated moving average (ARIMA models).In this research, some linear dynamic systems, represented by ARIMA with exogenous input variables (ARIMAX models) were used to forecast crude oil prices (considered as output variable) for OPEC organization with the help of crude oil production (considered as input variable) depending on the data starting from the period of 1973 until 2018.Using traditional ARIMAX method and proposed method (Bivariate Wavelet Filtering) for the time series data in order to select one of them for forecasting through comparing some measures of accuracy, such as MSE, FPE, and AIC.Then, applying crude oil prices for OPEC using the traditional ARIMAX models and ARIMAX models with applying the bivariate wavelet filtering, especially bivariate Haar wavelet.The main conclusions of the research were that the success of bivariate wavelet filtering in forecasting of crude oil prices using proposed model was more appropriate than traditional models, and the forecasting of crude oil prices using proposed method in 2020 will be fairly less than 2019.
Pengamanan Data Teks Menggunakan Algoritma DES Berbasis Android

Android smartphone is certainly no stranger to the public and of course has spread widely to the world. Android smartphone has many applications in it and also has the function of each of these applications. One application that is commonly used is sending SMS, which functions as sending and receiving data quickly. SMS is a text message that at the time of sending has been limited and set its size, the size limit is 160 characters per SMS. But every use and use of SMS is certainly subject to shipping fees that have been determined from the operator. With this, security is one of the most important aspects to maintain the confidentiality and authenticity of the SMS messages so that they avoid being tapped, stolen or irresponsible. So it is very necessary to apply security in this SMS application that uses cryptographic DES (Data Encryption Standard) algorithm so that messages sent to their destinations can be kept confidential and authentic