Reviewerly/exHarmony huggingface dataset:
A fast blink-control system based on FPGA

People have been pursuing fast and convenient methods to control machines. However, the existing human-computer interaction methods still have many shortcomings. We present here a fast blink-control system based on FPGA. The video image processing technologies were utilized to realize the blink-control algorithm and the Verilog hardware description language (Verilog HDL) was taken to develop functional modules on the FPGA. Positioning human eye and recognizing blink were implemented with integral projection method based on gradient operator according to features of eyes. Then, subsequent operations were performed based on judgement results. The designed fast blink-control system was verified on ALTER FPGA development platform. The results showed that the accuracy to detect eye blink (front face, no inclination) was 98% and the average response time of the system was about 413ms, which reached the real-time detection level. Therefore, it has achieved the goal to provide a new control mode which is fast and effective.
Revitalisasi Pengelolaan Jurnal IJID

IJID journal is one of scientific journal that belongs to Department of Informatics, Master and Undergraduate Programs, Faculty of Science and Technology, UIN Sunan Kalijaga. In this paper, we discuss problems that this journal faces. We describe also activities that we have done to solve those problems. Problems, such as discontinuity in publishing papers, not yet registered with DOAJ, not yet obtained national accreditation (namely Sinta), and not yet internationally recognized by Scopus index, are usual journal management problems that happened on journals. IJID editorial has conducted several treatments to combat those problems. As a result, some editions can be published. Now, IJID steps more further to apply for DOAJ and national accreditation.
Bi-level user authentication for enriching legitimates and eradicating duplicates in cloud infrastructure

Ease of usage of cloud computing leads to an exponential growth in all sectors. Exponential growth always attracts duplicates to consume and deplete resources. Cloud is not exempted from invaders and overwhelming the resource utilisation thereby availability become a threat. Availability issue arises due to multiple requests towards the same victim, a DDoS attack. Hence, the major concern in the cloud is to rightly identify legitimates, and providing the required services all time go by avoiding DDoS attacks. Multiple techniques are available to identify and authenticate the users. This paper not only just tries to authenticate the users but also works on eliminating the invaders in two fold. In the first phase, the user ID is scrambled in four different steps. In the second phase, the users are authenticated depending on the credits. Based on the traffic flow (in the case of network level attack) and on the interval between consequent service requests (in the case of service level attack), users are authenticated upon which services are provisioned accordingly. The simulation results presented here exhibits the strength of the proposed method in detection and prevention of DDoS attack in cloud computing environment.
SOADIWA: A Service-oriented Architecture for Data Interoperability in Web Applications

The realisation of Service-Oriented Architecture (SOA) to communicate data between systems running on different platforms lack an organised framework to capture the essential elements required for successful interoperability between web applications and their services. In this work, a SOA for Data Interoperability in Web Applications (SOADIWA) was designed. The architecture of SOADIWA was based on five layers, namely Web Application Layer (WAL), Quality of Service Assurance Certifier Layer (QoSACL), Web Service Layer (WSL), Visualization Input Layer (VIL) and Visualization Output Layer (VOL). In WAL, the Service Requester (SR) initiates a request for data from the Service Provider (SP) through the QoSACL to provide appropriate website via WSL for rendering of services which must be accepted, processed and returned for a particular need in VIL. The requested data is filtered in VIL for data exploration and analysis in VOL using context-sensitive visualization techniques. The purpose of QoSACL is to check and verify the claims made by the SP about its quality of service. This enabled the SR to choose the service that satisfied its needs. The implementation comprised of Java Script, Microsoft Visual Studio 2017 and NuGet packages; while the experiment was simulated on LoadUI pro application. Standard metrics such as Optimal Performance (OP) and Phased Effort Distribution (PED) were developed to test SOADIWA. These results conformed to basic web service interoperability. The work led to the integration of a host of techniques towards the creation of a novel tool that is useful in web domain using SOA approach.
Verifiable secure computation of linear fractional programming using certificate validation

Outsourcing of scientic computations is attracting increasing attention since it enables the customers with limited computing resource and storage devices to outsource the sophisticated computation workloads into powerful service providers. However, it also comes up with some security and privacy concerns and challenges, such as the input and output privacy of the customers, and cheating behaviors of the cloud. Motivated by these issues, this paper focused on privacy-preserving Linear Fractional Programming (LFP) as a typical and practically relevant case for veriable secure multiparty computation. We will investigate the secure and veriable schema with correctness guarantees, by using normal multiparty techniques to compute the result of a computation and then using veriable techniques only to verify that this result was correct.
Human face detection based on skin color with principal component analysis (PCA) algorithm

Identifying and identifying a person's face is one of the challenging issues in computer applications. The main part is the facial recognition of the whole image. Problems include sensitivity to the exposure conditions of the input image. Here is a way to detect human faces in color images based on the edge and color of the skin of the color image by setting the appropriate thresholds. First, image enhancement is performed, especially if the image is obtained from an infinite lighting condition. Then the skin was digested in the laboratory environment. The edges of the image are combined with the tone of the skin color to separate all areas other than the face. For this purpose, skin color detection methods have been used to detect faces. The Eigenface method is also known as the PCA method. This process has been tested in pictures and samples for various samples and has yielded good results. Due to the increasing instances of identity theft and the occurrence of terrorism in the past years, recognizing and identifying a person can determine the unity of a human being with another person's face. For facial recognition of an online monitoring system or an offline image, the main component to be identified is the skin areas. The skin color has been proven to be a powerful and useful indication for face detection, placement, and follow-up. Face detection is simply a facial image for determining the input given, regardless of its size, position, and background. The current evolution of computer technology has increased in this era.
XGAN: adversarial attacks with GAN

Recent studies have demonstrated that deep neural networks can be attacked by adding small pixel-level perturbations to the input data. In general, such disturbances are indistinguishable to the human eye, but can completely subvert the output of the deep neural network classifier to achieve non-target or target attacks. The current common practice is to superimpose the original image after generating a disturbance for the neural network. In this paper, we applied a method of generating target images directly using GAN to achieve a method of attacking deep neural networks. This method has excellent results on black-box attacks and is also suitable for the preconditions of most neural network attacks. Using this method, we achieved an 82% success rate on the black-box target attack on the cifar10 dataset and the MNIST dataset, while ensuring that the generated image is comparable to the original image.
Pose detection of parallel robot based on improved Hough‐K‐means and SURF algorithms

It is difficult to detect end pose of auto electrocoating conveying parallel robot based on binocular vision because of less features, noise and blurred edges in image. So a pose detection method is presented. Firstly, according to distance and similarity between image points, the bilateral filtering is adopted to reduce noise. Secondly, for extracting straight lines on blurred edges and feature points in lines accurately, a Hough-K-means algorithm using K-means clustering analysis in parameter space after Hough transform is proposed. Thirdly, the discrete Gaussian–Hermite moment is used as feature descriptor to solve the low accuracy problem of principal directions of feature points described by speeded-up robust features (SURF) descriptor. The extracted feature points are selected according to the similarity metric between constructed feature vectors. Finally, the vision model is developed to calculate the three-dimensional pose parameters of parallel robot based on obtained point pairs. The experiment results show that, compared with the SURF algorithm, the average matching time decreases by 21.58%, the average deviations of detected poses in x, z and β decrease by 1.149 mm, 0.646 mm and 1.164° respectively by using the proposed method. The speed and accuracy of pose detection of parallel robot can be improved.
TEKNIK DATA MINING DALAM PENILAIAN PENGAJARAN GURU BERDASARKAN INDEKS KEPUASAN SISWA

Teacher performance evaluation is a process of analysis in order to produce good teaching. The process of evaluating the performance of teachers in Kartika Pematangsiantar Middle School has not been so detailed (detailed). With such detailed assessment, it is feared that there will be subjective judgments (based on personal interests), which can lead to social jealousy for teachers who do not receive achievements. In the assessment of teacher teaching students need data on teacher satisfaction. In collecting data using questionnaires and variables used in this study there were 4 namely discipline, quality of teaching, behavior, and social relations. This study aims to assist the School in improving teacher performance, especially in the way of teaching teachers based on student satisfaction using C4.5 Method.Keywords: Data Mining, Method C4.5, Kartika Middle School, Teacher Teaching Evaluation Based on Student Satisfaction Index
Algorithm Model of K-means for Poor Households Classifying

Poverty is a complex problem experienced by majority of world’s nations especially in the developing countries such as Indonesia. Poverty countermeasure has become the main country development program as one of the indicators of development success is the decrease of poverty rate. Various programs either in national or regional scale have been made as the government’s attempt to reduce the poverty rate. However, there have been a lot of news reports covering the government’s programs which are less precise and even not on the target. Thus, it is essential to have a method which can be implemented to help the planning process of those poverty alleviation programs. This study aims at explaining the formation of K-means algorithm model for classifying poor households; taking a study case in Banyumas Regency, Central Java. The result of this study was K-means algorithm model which has been adapted to the poverty concept from the Statistics Indonesia (BPS) as well as the factors affecting the household poverty. The information obtained from each cluster formed is household characteristics, estimation of household number, and estimation of population experiencing poverty.
Deep 3D Facial Landmark Detection on Position Maps

3D facial landmark detection is a crucial step for many computer vision applications, such as 3D facial expression analysis, 3D face recognition, and 3D reconstruction. Pose variations, expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task. In this paper, we propose a novel 3D Face Landmark Localization Network (3DLLN), which is robust to the above challenges. Different from existing methods, the proposed 3DLLN utilizes the position maps as an intermediate representation, from which 3DLLN detects 3D landmark coordinates. Further, we demonstrate the usage of a deep regression architecture to improve the accuracy and robustness of a large number of landmarks. The proposed scheme is evaluated on two public datasets FRGCv2 and BU_3DFE and achieves superior results to state-of-the-arts.
How optimizing perplexity can affect the dimensionality reduction on word embeddings visualization?

Traditional word embeddings approaches, such as bag-of-words models, tackles the problem of text data representation by linking words in a document to a binary vector, marking their occurrence or not. Additionally, a term frequency-inverse document frequency encoding provides a numerical statistic reflecting how important a particular word is in a document. Nevertheless, the major vulnerability of such models concerns with the loss of contextual meaning, which inhibits them from learning proper pieces of information. A new neural-based embedding approach, known as Word2Vec, tries to mitigate that issue by minimizing the loss of predicting a vector from a particular word considering its surrounding words. Furthermore, as these embedding-based methods produce low-dimensional data, it is impossible to visualize them accurately. With that in mind, dimensionality reduction techniques, such as t-SNE, presents a method to generate bi-dimensional data, allowing its visualization. One common problem of such reductions concerns with the setting of their hyperparameters, such as the perplexity parameter. Therefore, this paper addresses the problem of selecting a suitable perplexity through a meta-heuristic optimization process. Meta-heuristic-driven techniques, such as Artificial Bee Colony, Bat Algorithm, Genetic Programming, and Particle Swarm Optimization, are employed to find proper values for the perplexity parameter. The results revealed that optimizing t-SNE’s perplexity is suitable for improving data visualization and thus, an exciting field to be fostered.
CNNs in the frequency domain for image super-resolution

This paper develops methods for recovering high-resolution images from low-resolution images by combining ideas inspired by sparse coding, such as compressive sensing techniques, with super-resolution neural networks. Sparse coding leverages the existence of bases in which signals can be sparsely represented, and herein we use such ideas to improve the performance of super-resolution convolutional neural networks (CNN). In particular, we propose an improved model in which CNNs are used for super-resolution in the frequency domain, and we demonstrate that such an approach improves the performance of image super-resolution neural networks. In addition, we indicate that instead of numerous deep layers, a shallower architecture in the frequency domain is sufficient for many types of image super-resolution problems.
Denoising of MRI images using fast NLM

Denoising of image is a very crucial step which should retain fine details but should remove noise. Making the difference between noise and actual edge related data is very difficult. NLM filter helps to make a differentiation between image data and noise data. Its weight function decides the weightage of the neighboring pixel depending upon the similarity with the pixel to process. It helps to retain the edges and avoid it from smoothening. This paper discusses the implementation of NLM filter using hardware platform Spartan 6. After implementaion of this on FPGA, not only denoise the image but preseve edges and there is a tremendous saving in time compared to its matlab implementation. Denoised image performance is calculated using various objective metrics such as MSE, PSNR, SSIM, PFOM etc. FPGA implementation shows clearly the advntages over its matlab implementation.
Decentralized Privacy-Preserving Platform for Clinical Data Sharing and Analysis.

Collection and management of clinical data for administration and analysis is a time-consuming and complex task, especially when multiple data providers been involved. Even if people are willing to take on the burden for it, there is still no mature solution to protect data privacy for distributed data providers. Distributed ledger is an emerging technology that supports decentralized data sharing and management. Based on this, we present a platform which enables distributed and truthful data collection and serves privacy-preserving needs in clinical data management. Our system, built on Hyperledger Fabric, used smart contract to execute data aggregation and provide basic analysis methods. The system used ledger and world status to record data access history and other metadata. This decentralized platform enables data providers to proactively share and protect their data, Thus can simplify clinical data collection procedure and promote efficient collaboration between providers.
Structural and input reduction in a ESN for robotic navigation tasks

This manuscript aims at showing the effects of feature selection and manifold reduction methods in dealing with the wall-following problem in mobile robotics, a well-known nonlinearly separable classification problem in which sensor recordings are associated to controlled motor responses. The capabilities of state manifold reduction in Echo State Networks (ESNs) through Laplacian Eigenmaps (LEs) are described in terms of noise rejection over the trained weights. Furthermore, various machine learning-based and data mining-based methodologies are applied to show the advantages of using the most informative contents drawn from the original sensor readings.
Research on Intelligent Supervision Method and System Developing of Soil Remediation Project Based on 3D Mobile GIS

At present, some problems such as inconvenient manual supervision, lack of real-time online supervision and poor online interaction during the implementation of soil remediation project. In order to solve the problems, the supervision method and system development of soil remediation project are studied based on sensing online and 3D mobile GIS technologies. The remediation environment are monitored with fixed sensors online and the heavy metal content are sampled with the mobile sensors. The status of soil remediation site is supervised in real time with video online. All the real-time sensing data are integrated and stored in the comprehensive database. The information of soil pollution and remediation environment is visualized with maps and charts in 3D geographic scenes in the system. In addition, the pollution degree evaluation and remediation effect analysis functions are implemented in system. It is proved with the project practice that the method and system are convenient for managers to monitor the environment on-site and supervise the status of soil remediation site in real time. The informationization and intelligence level of soil remediation project can be effectively improved.
REALIZATION OF PEDAGOGICAL CONDITIONS OF FORMATION OF RESPONSIBLE ATTITUDE TO THE FUTURE PROFESSION IN STUDENTS OF PEDAGOGICAL UNIVERSITY

In the article, based on the analysis of scientific sources and empirical material, the realization of pedagogical conditions of forming a responsible attitude to the future profession of students of pedagogical universities is characterized. The ways of improving the content of educational classroom and extracurricular activities of students of pedagogical universities as a pedagogical condition for the cognitive nature of the formation of their responsible attitude to the future profession are substantiated: saturation of the content of the educational disciplines with the educational material of the specified direction in order to master the competencies of a professional duty; extracurricular educational activities of students in the form of functioning of students’ club “Pedagogical volunteering”. The purpose of work of the students’ club “Pedagogical Volunteering” is defined: education accepting the value of pedagogical activity by motivating the need and striving to acquire the necessary knowledge, skills, experience of qualified fulfillment of professional requirements and responsibilities; value attitude to the process of fulfilling professional requirements and responsibilities and satisfaction with the pedagogical results obtained; formation of ideals, traditions related to the effectiveness of pedagogical activity. Requirements for the student club leader are outlined. Forms of realization of the specified condition of formation of responsible attitude to the future profession in the students of pedagogical university are determined: lectures, meetings, conversations with professionals in this field.
Students’ Attitude Toward Extensive Reading at English Education Study Program in IAIN Bukittinggi

The research was due to several problems found in the field; the students were reluctant to read because students found some difficult words.Then, the students read one textin a week for the sake of writing weekly extensive reading report so that the researchers assumed that the students had bad reading habit.Then, the research aims to know students' attitude toward extensive reading in the aspects of affective, cognitive and behavior.This research belongs to quantitative research by survey.The population was the second semester English Education Study Program studentsof IAIN Bukittinggi.To choose sample, the researcher used total sampling.The finding reveals that students' attitude toward extensive reading in the aspect of affective is 3.56 which remarks good attitude.In addition, the students' attitude toward extensive reading in the aspect of cognitive is 3.82 which indicates the good attitude as well.The students' attitude toward extensive reading in the aspect of behavior is 3.36 that denotes good attitude.In conclusion, they perceive extensive reading is beneficial and joyful for them.
An Incremental Autoencoder Approach for Data Stream Feature Extraction

Autoencoder is a neural network model which extracts the best representing features of data by performing unsupervised prelearning. Existing autoencoder approaches have been developed for applications that can access all data set at a time. However, the existing autocoders fail to satisfy for data stream applications that does not have access to the entire data set at once and and requires instant response. Therefore, this study presents an online sequential autocoder approach for data stream. The proposed autoencoder approach is based on the Extreme Learning Machines Autoencoder (ELM-AE) and adapts ELM-AE to be incremental learning algorithm. Therefore, Online Sequential-ELM (OS-ELM) algorithm which is incremental learning algorithm for data stream is integrated into ELM-AE in this paper. The proposed approach is tested on KddCup99, Poker, Pendigits, Hyperplane and ElecNormNews data stream sets which are commonly used in literature and achieved results demonstrate proposed approach's success.