Reviewerly/exHarmony huggingface dataset:
AI based face recognition system using FaceNet deep learning architecture

In recent years face recognition system is emerging technology in research areas. Face recognition is used in many applications such as attendance management system, People tracking system and access control system etc. Major challenges faced in face recognition are detection and recognition because it is not easy to detect multiple faces in one frame and difficult to recognize the faces with poor resolution. Therefore, the main objective of this paper is to obtain better accuracy by using the combination of FaceNet and Deep Neural Network (DNN). In this proposed system, FaceNet is used for feature extraction by embedding 128 dimensions per face and DNN is used to classify the given training data with extracted feature of FaceNet. The outcome of the system is practical, reliable and consumes less time.
Pirls-based cognitive assessment instrument: How effective is it for measuring Indonesian language reading literacy in fifth grade?

The Indonesian reading literacy test is a technique for evaluating students’ cognitive reading abilities. However, as of yet, the test questions administered to students are inadequate to assess certain parts of their Indonesian reading literacy. Therefore, the purpose of this study was to develop an adequate and standardized cognitive assessment instrument based on the PIRLS model to measure students' Indonesian reading literacy. The participants in the study were fifth graders from an elementary school, SD Negeri 11, located in Metro City, Lampung Province, Indonesia, with a total of 293 students. This study adopted a research and development design. The results indicated that the cognitive assessment instrument based on the PIRLS paradigm that was developed to measure Indonesian reading literacy in fifth graders delivered adequate and standardized items
Tip Growing Mechanism in Liquid with Alginate Gel Tube Films

This paper introduces a tip growing mechanism in liquid based on a sol-gel transformation of alginate. A gel film generated by cross-linking provides a flow tube and transmits the pressure at the root to the tip like a hydraulic system. In this paper, we describe the principle and verification of the behavior.
GAN-Generated Face Detection with Strong Generalization Ability Based on Quaternions

Models based on generative adversarial network (GAN) can generate very realistic face images, which bring additional challenges to justice, criminal investigation and reputation protection, etc. Therefore, a GAN-generated face detection algorithm having a strong generalization ability is proposed by using quaternions. The algorithm consists of a GAN noise fingerprint extraction module and a classification module. The former module uses Siamese quaternion U-Net to extract fingerprint features. The latter module adopts quaternion ResNet to distinguish the natural face from the GAN-generated face based on the extracted fingerprint features. In addition, the distance based logistic loss and cross entropy loss are used to optimize parameters. Experiments are based on a public natural face dataset CelebA, and some generated face datasets that generated by various GANs trained on the CelebA. Four groups of ablation experiments verify the improvements of the proposed algorithm in four aspects. The experiments, in which training data generated by only one kind of GAN but testing data by multiple GANs, show that the proposed algorithm has stronger generalization ability than the existing algorithms. Robustness experiments show that the proposed algorithm is also robust against JPEG attacks.
Screening Designs for Image Augmentations to Maximize Accuracy of a Convolutional Neural Network Using Limited Data

Small training datasets present a real-world concern for training Convolutional Neural Networks. Typically, such datasets are augmented via image manipulation techniques intended to add variability to the training dataset. Many such data augmentation techniques exist, and to-date standard practice is to perform augmentation in either an ad-hoc fashion or via expert informed opinions. This research presents a first of its kind procedure using a modern statistical experimental design technique to explore the benefits of various image data augmentation techniques. A definitive screening design experiment is used to study 21 image augmentation techniques, along with the regularization technique dropout, across 15 training datasets of varying sizes. Our study finds that the screening design method is a reliable and replicable way to find beneficial image augmentations in datasets with small amounts of data. The intragroup models provide a 3 percent gain in accuracy over the state of practice models. The robustness of the discovered models for each training dataset when applied to other datasets shows a significant increase over the state of practice for 77% of models (174 of 225 comparisons) when compared to the state of practice using dropout of 50% and a significant increase over the state of practice for 48% of models (108 of 225) when compared to the state of practice using dropout of 60%.
Training Dataset and Dictionary Sizes Matter in BERT Models: The Case of Baltic Languages

Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. While studies have shown that monolingual models produce better results than multilingual models, the training datasets must be sufficiently large. We trained a trilingual LitLat BERT-like model for Lithuanian, Latvian, and English, and a monolingual Est-RoBERTa model for Estonian. We evaluate their performance on four downstream tasks: named entity recognition, dependency parsing, part-of-speech tagging, and word analogy. To analyze the importance of focusing on a single language and the importance of a large training set, we compare created models with existing monolingual and multilingual BERT models for Estonian, Latvian, and Lithuanian. The results show that the newly created LitLat BERT and Est-RoBERTa models improve the results of existing models on all tested tasks in most situations.
The Future of Blockchain for Wastewater Treatment in the Textiles Industry

Industries and domestic use a majority of water on a daily basis, causing water scarcity. Water consumption by industry is increasing every day. Recyclable wastewater can minimize water demands from local water bodies, improve availability of water, reduce waste disposal and pollutant loads, cut thermal energy usage, and perhaps reduce overhead expenses. The produced effluent has been recycled in a variety of methods. Because textiles poorly absorb dyes; the textile industry utilizes synthetic dyes that are available in a wide range and produces vast volumes of highly colored effluent. This brightly colored textile wastewater has a considerable negative impact on plant photosynthesis. It also has an influence on aquatic life due to lower penetration of light and oxygen consumption. Blockchain is a breakthrough innovation that started with cryptocurrencies such as bitcoin and has subsequently extended outside banking and finance. Blockchain technology is an Internet database technology used to record information and contracts across a timeline. Distributed ledger, often known as blockchain technology, is distinguished by its decentralization, transparency, and openness, permitting anybody to maintain information in the database. Smart wastewater treatment plant has long been a prominent topic. It is often deployed as part of a data-scheduling platform that includes sophisticated algorithms. The aim of this chapter is to examine the various treatment options for textile wastewater, as well as blockchain technology in the treatment process. It also addressed the application of blockchain in different sectors, recent advances, and future perspective of blockchain in wastewater treatment.
Multi-oriented Text Detection In Natural Images Using Deep Learning

The exigent demands of text detection have led to the development of numerous deep learning models. Scene text is the text that is incorporated into natural scenes. The precise and explicit spotting and reading of scene text are crucial in countless computer vision applications. This paper presents a novel approach for multi-oriented scene text detection by introducing a deep learning model that integrates CRAFT (Character Region Awareness for Text Detection), text detector and transformer OCR, text recognizer. The CRAFT detector detects the text in the scene text character-wise by producing region and affinity scores. The output of this detector is then fed to the transformer OCR which then gives the recognized text. The results are evaluated using ICDAR 2015 dataset. Hence, this paper fuses two robust mechanisms for extracting textual information from an image, which makes it more accurate than the existing methods.
Numeric key programming - Improvement of sensors for micro:bit robot car -

The micro:bit is designed as a microcontroller board for beginners, but it is difficult to use it for visually impaired learners because its programming environment requires a PC. In addition, mobile robots using the micro:bit are equipped with various sensors, but their use is not easy. In this paper, we report on how to improve the accuracy of the sensors in the micro:bit and how to easily use the sensors in the mobile robot micro:Maqueen.
Prostate Cancer Data Analytics Using Hybrid ECNN and ERNN Techniques

Prostate is a subsequent driving reasons for malignant growth passings among male human. Primal location of malignant tumor can actually lessen the pace of deathrate brought about by Prostatic malignant growth. Because of high and multi-resolution of MRIs from prostatic malignant growth require a legitimate demonstrative frameworks and instruments. In the foregone specialists created Computer supported finding (CAD) frameworks that assist the radiologist with distinguishing the irregularities. In this exploration paper, we have utilized novel Deep Learning methods like Hybrid ECNN-ERNN. Convolutional Neural Networks and Recurrent Neural Networks for distinguishing prostate cancer. In addition, various elements extricating techniques are proposed to further develop the location execution. The highlights removing methodologies depend on surface, morphological, scale invariant element change (SIFT), and elliptic Fourier descriptors (EFDs) highlights. The exhibition was assessed in light of single as well as mix of elements utilizing Deep Learning Classification strategies. The Cross approval (Jack-blade k-crease) was performed and execution was assessed in term of get working bend (ROC) and particularity, responsiveness, Positive prescient worth (PPV), negative prescient worth (NPV), misleading positive rate (FPR). In view of single highlights separating methodologies, ECNN procedure gives the most elevated precision of 98.34% with AUC of 0.999. While, utilizing mix of highlights removing procedures, ECNN with surface + morphological, and EFDs + morphological elements give the most noteworthy precision of 99.71% and AUC of 1.00. The proposed model outperformed than the existing system with parameters / metrics as accuracy (82.42%), error rate (0.13), val_loss (0.41), val_accuracy (0.50), size of dataset used in research (1.50 GB), No. of epochs (30), Time-complexity (O(n2)) and execution time (1022 ms).
EFL Learning Via Smart TV in Junior High School

The main objective of English Foreign Language learning via Smart TV in junior high school gives molestone improvement on the student capacity at interactive level of learning with the ongoing advancements in TV have offered ascend to the intelligent shrewd board innovation which can enhance student to learn english through new cutting-edge technology learning situation. The objective of this qualitative case study is to describe the implementation of Smart TV used in English Foreign Language Learning process in the classroom and Boarding School and the outputs of the implementation of Smart TV at SMP Unggulan Bilingual Boarding School (BBS) Zainul Hasan Genggong Probolinggo. The theory supporting utilization of TV as a general media apparatus in training is tangible incitement theory which has its essential reason that compelling learning happens when the senses are stimulated. TV assumes a huge job in preparing student for citizenship, encouraging the feeling of understanding, natural mindfulness and teaching of socio-political and social qualities. The eventual fate of TV in schools will rely upon whether there is adequate security to keep the device, stockpiling of these devices, wellsprings of electric supply, gracefully of internet facilities, streets to flexibly these devices in various schools and planning of teachers as an asset individual. To arrive at such a point, the researcher gathered the information by direct perception, in-depth interviewing and document review. Furthermore, the researcher examined the assembled data by using three huge periods of the data analysis: data reduction, data display, then drawing conclusion and verification.
Formation of students' cognitive activity within lifelong education

Integration and globalization in the structure and various spheres of human activity require modernization of the educational space, which should form active and creative individuals who need a thorough theoretical and practical training. After all, today the ability to think is of particular importance, that is, to isolate and analyze phenomena, processes, to notice the essential in them, to draw appropriate conclusions oneself, to make assessments and to make timely, balanced decisions. The article also discusses the issues of increasing the cognitive activity of students in the context of the implementation of the Federal State Educational Standard. First of all, we are talking about the development of techniques and methods for the formation of skills for independent acquisition of knowledge, without which it is very difficult for a modern person to live in the modern world. A correct understanding of the Federal State Educational Standard and its implementation on the ground will qualitatively improve the training of a modern student - a future specialist.
Content-Based Nonlinear Filter for the Removal of Impulsively Modeled Artifacts from Images and Videos

A content-based nonlinear filter capable of eliminating impulsively modeled artifacts from images and videos is proposed. The algorithm is a nonlinear filter that chooses median of the array or modified trimmed winsorized median of the array as the threshold to estimate the pixel is noisy or not. The median or modified winsorized median will act as a measure of central tendency to estimate the pixel is noisy or not. There are two demarcation levels used to conclude the estimated pixel is noisy or not. The first demarcation is named ad TH1 which holds value between (20 and 35). The second demarcation level is called TH2 which holds value between (0 and 15). The first delimiter says that the processed pixel is noisy or not and the second delimiter says that the median of the array is faulty or not. The proposed algorithm is applied on different defaced images and found to give very good visual results.
A Boosting Algorithm for Training from Only Unlabeled Data

Unlabeled-unlabeled (UU) learning was proposed to cope with the high cost of data annotation and some realistic cases, in which we cannot get labeled data. It allows us to train a classifier with only unlabeled data. State-of-the-art (SOTA) UU methods with good performance based on neural networks (NN) have been proposed; however, there is a lack of studies on boosting algorithms for learning from only unlabeled data, even though boosting algorithms sometimes perform very well with simple base classifiers. We propose a novel boosting algorithm for UU learning: Ada-UU, which compares against neural networks. The proposed method follows the general procedure of AdaBoost while the classification error is estimated with two sets of unlabeled (U) data. We empirically demonstrate that Ada-UU outperforms neural networks on several large-scale benchmark UU datasets and has comparable performance on a small-scale benchmark dataset.
Some Series of Group Actions

This chapter deals with the decomposition of the Jacobians of curves with actions of three series of groups, namely, cyclic groups of order n, dihedral groups of order 2n, and the semidirect product of the group of order 3 by an arbitrary self-product of the Klein group of order 4. For large n we obtain in each case examples of components which are neither Jacobians nor Prym varieties of subcovers. The importance of the last case lies in the fact that it gives examples of curves whose Jacobian is isogenous to a product of an arbitrary number of Jacobians of the same genus.
Statistical Analysis of Distribution Network Fault Information Based on Multi-source Heterogeneous Data Mining

With the development of communication technology and the increasing requirements of power users for power supply reliability, the safe and stable operation of power grid has become particularly important. Power grid fault detection and analysis is the premise of safe operation of intelligent distribution system, and artificial neural network has high robustness, which can classify and train data information. Therefore, this paper designs and analyzes the distribution network fault information statistics based on multi-source heterogeneous data mining. Firstly, this paper introduces the methods of fault data acquisition and storage, and studies the application of multi-source heterogeneous data mining technology. Then, the statistical structure of distribution network fault information is designed, and the false detection rate of the model is tested. Finally, the test results show that the number of faults diagnosed by the distribution network fault information statistical model based on multi-source heterogeneous data mining is small, and the false detection rate is also very low, which can meet the needs of distribution network fault information statistics.
THE APPLICATION OF COOPERATIVE LEARNING MODEL OF TYPE STUDENT TEAM ACHIEVEMENT DIVISIONS (STAD) ON THE MATERIAL OF COMPOSING AN EQUIVALENT FORCE AT CLASS X TECHNIC OF BUILDING CONSTRUCTION SANITATION AND TREATMENT SMK NEGERI 1 PALANGKA RAYA

The research was motivated by the lack of student learning outcomes in the material of composing an equivalent force. Student who less actively involved in the learning process, learning group has not been managed well, the student who is good, medium and low receive equal treatment, not to be rewarded for the best student, good and lowest. The good student very extremely rare to help the weaker students. This research makes use of the quantitative method research and models of type STAD cooperative learning with population student of X technic of building construction sanitation and treatment as many as 28 people. Formulation of the problem in this study is how the application of models of type STAD cooperative learning can improve learning outcomes the material of composing an equivalent force and how the response of student in grade at class X technic of building construction sanitation and treatment SMK Negeri 1 Palangka Raya year 2017/2018. Succesful implementation of type STAD cooperative learning models in the material of composing an equivalent force is to know to an increase and decrease in class X technic of building construction sanitation and treatment of the prior action is pre-test to post-test. In the prior action, the student who can achieve the minimum of criteria competition (KKM) as many as 0 (0%) people with average value are 36.79. At LKS execution in a group, the student who is can achieve the minimum of competition as many as 28(100%) people with the average value is 75,34. At the postest after, students who are complete as many as 15(53.57%) with the average value is 59.88 and completeness level is 53.57%. In a totality value, the student who is complete as many as 15(53.57%) with average value is 65.37 and completeness classical is 53.57%. Although the thoroughness of student increased from the prior action to the finish value or after action, the classical or the overall student learning outcomes have not reached 85% KKM set at 70, on an individual basis there is some student who does not complete. Thus it can be retrieved the conclusion by the application of the model of type STAD cooperative learning can improve learning outcomes in class X technic of building construction sanitation and treatment SMK Negeri 1 Palangka Raya year 2017/2018
A New Method for Joint Recognition and Location of Radar Jamming

Radar plays an increasingly important role in the civil applications as well as the military ones. Nowadays, various kinds of jamming strategies were presented to blind the radar. It is urgent to identify and recognize those jamming signals in the realistic applications. The early works relied on the predefined handcrafted features. They are highly dependent on the expert knowledge. Likewise, it is difficult to locate the jamming signal accurately. To solve this problem, this paper proposes a new method for joint detection and identification of jamming signals. A backbone composed of some convolutional blocks are presented to learn the high-level features. They are used to locate the jamming signals in the timefrequency plane. The identification of the interfering signal can be then recognized simultaneously. Multiple experiments are performed to demonstrate the effectiveness of proposed method.
Pengembangan Aplikasi Optimasi Rute Destinasi Wisata di Banyuwangi Menggunakan Modern Android Development (MAD) Pattern

The closest route optimization problem is a classic problem that is still often found in everyday life, one of them is the Traveling Salesman Problem (TSP). This study aims to find solutions in the form of optimal distances to various tourist destinations in Banyuwangi. In this study, the author uses the Python programming language as the backend and the Kotlin programming language as the frontend of the Android application as part of Modern Android Development. The results of the system evaluation, namely Black Box and User Acceptance Test on the application, show that the system that has been carried out can be concluded that the functional system can work well without experiencing bugs or problems on every available feature and page. In designing the application, the author uses the Modern Android Development pattern.
Correction of online handwriting stroke using machine learning

In this paper, the authors aim to correct strokes for input of handwriting diagrams. In order to achieve this correction, they propose a method which is in real-time using machine learning. Experiments with this method have shown that machine learning is also effective in stabilization of handwriting.
Empower Keywords Generation for Short Texts with Graph-to-Sequence Learning

Keywords are useful in natural language tasks. However, it is a challenge task to extraction keywords from short texts. In which the model may be subject to impaction of topic dependence and poor text organization structure. To resolve this limitation, we propose a keywords generation model ADGCN of short texts based on graph-to-sequence learning. The model to jointly short texts contextual feature and positional feature based adaptation for this task. We learn domain-invariant feature representations by using graph-building feature and node topic feature space, and jointly perform linear generate feature in framework of keywords decoding. Experiment results on real social datasets demonstrate that our proposed model achieves impressive empirical performance on relevance, information and coherence. Besides, the proposed ADGCN also outperforms the state-of-the-arts on public KP20k dataset. The experiments testify that the model can generate the topic keywords of short texts and effectively alleviate the influence of data disturbance.