Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Using Edmodo as Online Media on Students’ Outcome in Reading Course

The aims of this research are: 1) to define in detail whether edmodo as the online media can enhance the students' outcome in reading course 2) to analyze what are the difficulties of implementing edmodo as the online media in teaching readings course. The classroom action research is as the method of this research design. In this research, it was conducted in two cycles used planning, implementing, observing, and reflecting as the procedures of the collecting data. This research was conducted at Pharmacy Department of the Mathematics and Natural Science Faculty of Garut University (UNIGA). The subject of the research focused on the second semester at class B of Pharmacy Department which consisted of 32 students. The result of the research found that; 1) Edmodo as the online media in enhancing the students' reading outcome in identifying reading indicator regarding to; a) the students' purpose in identifying the text b) the students' understanding in identifying the specific and detailed information in each paragraph. c) the student's finding of the content of the text d) the student's understanding in identifying of the main idea in each paragraph of the of the text e) the students' understanding in getting reference. The students' score in enhancing reading outcome improved from step to step. In this research result finding, the researchers found the students' improvement specifically, such as; the average of students' score in pre-test session was 68.75, the first post-test session was 76.2, and the second post-test session was 84.20. Those results encouraged students to integrate the edmodo as the advanced technology in online media each learning, those created students to be active, creative in improving skill and knowledge; those involved students to be enjoynable activities in each learning including in teaching reading course.
A Block-Floating-Point Arithmetic Based FPGA Accelerator for Convolutional Neural Networks

Convolutional neural networks (CNNs) have been widely used in computer vision applications and achieved great success. However, large-scale CNN models usually consume a lot of computing and memory resources, which makes it difficult for them to be deployed on embedded devices. An efficient block-floating-point (BFP) arithmetic is proposed in this paper. compared with 32-bit floating-point arithmetic, the memory and off-chip bandwidth requirements during convolution are reduced by 50% and 72.37%, respectively. Due to the adoption of BFP arithmetic, the complex multiplication and addition operations of floating-point numbers can be replaced by the corresponding operations of fixed-point numbers, which is more efficient on hardware. A CNN model can be deployed on our accelerator with no more than 0.14% top-1 accuracy loss, and there is no need for retraining and fine-tuning. By employing a series of ping-pong memory access schemes, 2-dimensional propagate partial multiply-accumulate (PPMAC) processors, and an optimized memory system, we implemented a CNN accelerator on Xilinx VC709 evaluation board. The accelerator achieves a performance of 665.54 GOP/s and a power efficiency of 89.7 GOP/s/W under a 300 MHz working frequency, which outperforms previous FPGA based accelerators significantly.
Automatic Segmentation Method of Phalange Regions Based on Residual U-Net and MSGVF Snakes

Bone diseases include rheumatoid arthritis and osteoporosis. Although visual screening using computed radiography (CR) images is an effective method for diagnosing osteoporosis, there are some similar diseases that exhibit low bone mass status. To this end, we aim to develop a computer-aided diagnostic (CAD) system to support the automatic diagnosis of osteoporosis from CR images. In this paper, we use convolutional neural network (CNN) and multiscale gradient vector flow snakes (MSGVF Snakes) algorithms to segment each finger bone regions from the CR image. The proposed method is applied to 15 cases, 92.95 [%] of the true positive rates, 2.21 [%] of the false positive rates, 7.05 [%] of the false negative rates are obtained respectively.
Synthetic images generation for text detection and recognition in the wild

Deep neural networks help solving different images related tasks very efficiently, though their cost is high whereas a lot of data are required for training. While there is a great demand to build neural network models for optical character detection and recognition for different languages, such as, for mobile real-time applications, datasets collecting and labeling are quite expensive. In this paper, we propose the fully automated approach for synthetic images with text generation based on deep learning and projective geometry methods. For evaluation, we trained two neural networks on the dataset generated by our algorithm. Our approach enables to decrease the false negative rate on real images from SVT and SVT-50 datasets in comparison with training on SynthText dataset, giving ~1% of F<sub>1</sub>-measure increasing.
A guided manual method for juvenile age progression using digital images

Predicting the possible age-related changes to a child's face, age progression methods modify the shape, colour and texture of a facial image while retaining the identity of the individual. However, the techniques vary between different practitioners. This study combines different age progression techniques for juvenile subjects, various researches based on longitudinal radiographic data; physical anthropometric measurements of the head and face; and digital image measurements in pixels. Utilising 12 anthropometric measurements of the face, this study documents a new workflow for digital manual age progression. An inter-observer error study (n = 5) included the comparison of two age progressions of the same individual at different ages. The proposed age progression method recorded satisfactory levels of repeatability based on the 12 anthropometric measurements. Seven measurements achieved an error below 8.60%. Facial anthropometric measurements involving the nasion (n) and trichion (tr) showed the most inconsistency (14-34% difference between the practitioners). Overall, the horizontal measurements were more accurate than the vertical measurements. The age progression images were compared using a manual morphological method and machine-based face recognition. The confidence scores generated by the three different facial recognition APIs suggested the performance of any age progression not only varies between practitioners, but also between the Facial recognition systems. The suggested new workflow was able to guide the positioning of the facial features, but the process of age progression remains dependant on artistic interpretation.
User Feedback based Recommendation Engine using Neural Network

With a paradigm shift of focus towards user behavior in today’s E-commerce driven contemporary world there is a need for an efficient and portable learning methodology for machines so as to serve a particular user or a fraternity of users collectively based on knowledge acquired on interests of a pool of users. However, any data driven or data sourced Engine can be contaminated by redundant entries or bogus feedback to jack up or scale down a particular commodity on offer so as to facilitate the promotion of another one in it’s stead. This calls for the implementation of a Neural Network which familiarizes itself with the most popular user choices by virtue of user provided ratings and Machine Learning uses a Support Vector Machine model for filtering time-stamps and IP addresses associated with each user interaction with the system to nullify attempts at manipulating said user feedback or rating by miscreants that could affect recommendation of popular choices explicitly.A Neural Network using 12 Neurons representing various dimensions of a user commodity in form a theme or a colour coded scheme is used on which a rating system is laden for user to provide feedback. This feedback is used to provide unsupervised learning. The monitoring of time-stamps and IP addresses of each user feedback is done by using a supervised learning technique. This makes the model a semi-supervised one in it’s entirety which is the best approach. The Neural Network furthermore adopts a layered learning approach where it uses the ratings provided by the users to learn which fore-ground colour contrasts best with which background colour.In this research a comprehensive study of 10 relevant papers has been made to highlight and discover the scope of research and key challenges to the already existing systems in employment by various entities to serve a similar purpose.
Optical-to-SAR image registration using a combination of CNN descriptors and cross-correlation coefficient

Image registration is a problem of aligning two or more images of the same scene or object. The case when images are taken using different sensors - multimodal image registration - has applications in medical imaging and remote sensing. Unfortunately, many of the existing image registration methods operate under crude assumptions (i.e., the intensities of images are linearly correlated), which makes them inapplicable for the accurate multimodal registration. One approach to this task is to use deep learning to capture the complex intensity dependencies between images of different modalities. However, while deep learning methods produce good results, most of them are trained end-to-end and do not utilize the accumulated body of knowledge about image registration using “classic” information-theoretic and statistical methods. In this paper we consider the specific case of multimodal image registration - of optical and synthetic aperture radar (SAR) images. We use classic feature-based registration pipeline (first, corresponding feature points are found, then RANSAC is used as the transform estimator). Within this method we compare the effectiveness of various feature point detection and correspondence methods - both neural network-based and traditional. We find that Siamese network outperforms (but only slightly) the classic cross-entropy-based method for finding correspondences. Finally, we propose a hybrid method and show that it outperforms both “classic” method and an end-to-end network by a significant margin.
Simultaneous Cross-Sectional Velocity Distribution Measurements Using Laser Doppler Velocimeter Employing 7 × 8 Spatial Encoding

A laser Doppler velocimeter employing 7×8-channel spatially encoded measurement points is proposed for simultaneous cross-sectional velocity distribution measurements. The measurement points arranged in two dimensions are encoded by combining wavelength-division multiplexing and multiple bias-frequency encoding. The functionality of the proposed method was experimentally demonstrated.
Flight Path Planning Surrogate Model Based on Stacking Ensemble Learning

Abstract This paper proposes a method of establishing flight path planning surrogate model based on stacking ensemble learning, which can solve the real-time problem of complex flight mission’s on-line waypoints calculation. Airborne navigation system mainly utilizes several discrete waypoints to guide flight path or flight control. These waypoints are usually calculated by a set of equations based on flight dynamics, flight kinematics and flight mission constraints, and therefore path planning for complex missions cannot guarantee real-time performance. In this paper, flight samples are generated offline by taking flight mission characteristic parameters as input and flight waypoint coordinate series as output. Then two-layer coupling model is constructed based on stacking ensemble learning. A series of base-learners are constructed to learn the quantity of waypoints or each waypoint’s coordinate values respectively. At last, flight path planning surrogate model is built by combining all the base-learners, establishing the direct mapping relationship between input and output. The results show that this surrogate model can effectively calculate the aircraft flight waypoints, and meanwhile maintains ideal accuracy and real-time performance.
Relevance between filtration rate and filter media thickness in downflow filter systems

Abstract One of the simple, easy and inexpensive water treatment technologies is the Sand Filtration technology. Sand Filtration is one of the very small grains of sand as a filter media. This study is an experiment. The objective to be achieved in this study is to analyze the difference in the thickness of the sand filter media to the filtration speed and the amount of filtration discharge, which is the object of research is the raw water of the Departemen engineering Hasanuddin University with treatment using a long reactor 50 cm, width 50 cm and height 150 cm, Down Flow filtering method with sand thickness varies namely 20 cm, 30 cm, 50 cm, besides that it also uses 15 cm gravel and 5 cm fibers. Testing is done by screening, calculating the speed of water through the filter and checking the flow of water. The results were obtained with a thickness of 20 cm sand filtration media resulting in a filtration flow of 0,000199 m 3 /second with flow rate filtration 0,000794 m/second, a thickness of 30 cm sand filtration media resulting in a filtration flow of 0,000220 m 3 /second with rate filtration 0.00879 m/second while 50 cm sand produces filtration flow 0,000242 m 3 /second with rate filtration 0.00969 m/second. It can be concluded by increasing the thickness of the filtration media filtration discharge and filtration speed also increases.
Deep Learning for Visual Content Analysis

It has been reported that quantum generative adversarial networks have a potential exponential advantage over classical generative adversarial networks. However, quantum machine learning is difficult to find real applications in the near future due to the limitation of quantum devices. The structure of quantum generator is optimized to reduce the required parameters and make use of quantum devices to a greater extent. And an image generation scheme is designed based on quantum generative adversarial networks. Two structures of quantum generative adversarial networks are simulated on Bars and Stripes dataset, and the results corroborate that the quantum generator with reduced parameters has no visible performance loss. The original complex multimodal distribution of an image can be converted into a simple unimodal distribution by the remapping method. The MNIST images and the Fashion-MNIST images are successfully generated by the optimized quantum generator with the remapping method, which verified the feasibility of the proposed image generation scheme.
Brain MRI Super-resolution Reconstruction using a Multi-level and Parallel Conv-Deconv Network

High resolution (HR) magnetic resonance images (MRI) provide rich tissue anatomical information that enables accurate diagnostics and pathological analysis. However, the acquisition of HR MRI is limited by hardware restrictions, scanning time, and signal-to-noise ratio (SNR) in clinical applications. Recently, deep learning has shown promising power for improving the spatial resolution of MRI. In this study, we propose a multilevel and parallel Conv-Deconv super-resolution (CDSR) network to reconstruct high-quality HR MRI from low resolution (LR) inputs. Different from current SR methods based on convolutional neural networks (CNNs), we connect parallel 3D convolution and deconvolution filters to capture context information and extract multi-level features. Hierarchical features are adaptively upsampled using each of their following deconvolution layers and then fused together to recover the HR details. In order to alleviate the optimization difficulty, we introduce the interpolated input to the fused output, which performs like a cross-scale residual learning strategy, hence accelerates the convergence. Extensive experimental results on three benchmark datasets show that our proposed method outperforms current reported MRI SR methods and sets a new state-of-the-art performance.
Ant Colony Optimization with Self-Evolving Parameter for Detecting Epistatic Interactions

The epistatic interactions of single nucleotide poly-morphisms (SNPs) are fundamentally important for understanding the genetic causes of complex diseases. Due to the intensive computational burden and the diversity of disease models, existing methods suffer from low detection power, high computational cost, and preferences for some types of disease models. To tackle these drawbacks, an ant colony optimization with self-evolving parameter (SEPACO) is proposed in the paper. In the proposed algorithm, the self-evolving parameter control (SEPC) strategy is used to select the best parameters of the algorithm during the running process. In this way, SEPACO can set different optimal parameters for different disease models, which leads to the enhancement of the detection ability of the algorithm. Furthermore, the probability distribution function and the pheromone evaporation formula are improved to adapt SEPACO to the detection of epistatic interactions. SEPACO is compared with other recent algorithms on a variety of simulated datasets and a real biological dataset. The experimental results show that our algorithm, compared to the other test algorithms, can improve the average detection power from no more than 32% up to 68%. Moreover, our algorithm uses less running time.
Towards real-time recognition of activities in smart homes

Many supervised methods have been proposed to infer the particular activities of the inhabitants from a variety of sensors attached in the home. Current activity recognition systems either assume that the sensor stream has been presegmented or use a sliding window for activity segmentation. This makes real-time activity recognition task difficult due to the presence of temporal gaps between successive sensor activations. In this paper, we propose a method based on a set of hidden Markov models that can simultaneously solve the problem of activity segmentation and recognition on streaming sensor data without relying on any sliding window methods. We demonstrate our algorithm on sensor data obtained from two publicly available smart homes datasets.
VIS Keynote Address: Data Visualization Literacy

In the information age, the ability to read and make data visualizations is as important as the ability to read and write. This talk introduces a theoretical data visualization framework (DVL) meant to empower anyone to systematically render data into insights using temporal, geospatial, topical, and network analyses and visualizations. Exemplarily, the DVL is applied to (1) Map science and technology, see interactive data visualizations from the Places & Spaces: Mapping Science exhibit (http://scimaps.org) and recent PNAS special issue on Modelling and Visualizing Science and Technology Developments (https://www.pnas.org/modeling). (2) Design reference systems and user interfaces within the Human BioMolecular Atlas Program (HuBMAP) (https://commonfund.nih.gov/hubmap) that support the exploration and communication of single-cell data�from the subcellular to the whole body level. (3) Teach Visual Analytics (https://visanalytics.cns.iu.edu) to students around the globe.
The Change of Education with the technology Advancement

With the innumerable changes in the knowledge and technology society resulting from the emergence of the Internet, various communities have emerged and information has been democratized and knowledge better distributed. Technology has advanced so far as to invent the e-book reader. Note the influence and gain with the features that this support offer the user (student), tablet, compared to the printed book. Explain about the influence of this device within the classroom. It showed how coexistence of the two is possible and acceptance and satisfaction by students and readers. In discussing this subject it was observed that both the printed book and the new technologies trends have guaranteed space within the educational environment the tablet and the printed book will walk their paths in parallel without a determined time to their end. The work was not only limited to the printed and digital book but also extended and sought other knowledge gaps, systematization of data and sectoral information. With a brief profile of the book publishing industry and its economic dimensions.
Penerapan Speeded-Up Robust Feature pada Random Forest Untuk Klasifikasi Motif Songket Palembang

Songket is a historical heritage in the city of Palembang. Where Songket has many different types and motifs. Besides having historical value, Palembang's original Songket has high quality and complexity in the manufacturing process. As known Palembang Songket has a lot of motives, one of the ways to recognize Palembang Songket is through its motives, so that research was conducted for the classification of Palembang Songket motifs. The method used to extract features is the Speeded-Up Robust Feature (SURF), while the classification method is Random Forest. The process of forming the SURF feature is divided into two stages, the first stage is Interest Point Detection, which consists of Integral Images, Hessian Matrix Based Interest Points, Scale Space Representation and Interest Point Localization, the second stage of Interest Point Description consists of Orientation Assignment and Descriptor Based on Sum Haar Wavelet Responses. The resulting feature is used for the Random Forest classification. This study used 345 images of Palembang Songket motifs, among others, Bunga Cina, Cantik Manis and Pulir. The images taken are based on 5 colors from each Palembang Songket motif. For the separation of data there are 300 images used as data train and 45 images for testing data. From the tests that have been done the results of the overall overall accuracy are 68.89%, per class accuracy 79.26%, precision 69.27, and recall 68.89%.
Research on Moving Target Detection Method in Variable Background

It is the basis and research key of computer vision recognition to extract the moving target accurately and effectively from the video stream or image sequence. In this paper, a moving object detection method based on improved five-frame difference and edge detection is proposed to fit the motion target detection in the case of camera motion. By using the adaptive similarity threshold and the adaptive similarity preprocessing method based on fingerprint matching, the background overlap of the continuous frame image in the mobile background can meet the requirement of the frame background. The experimental results show that the proposed algorithm can improve the target recognition rate to 89%, and control the false positive rate to about 11%. It can detect the moving target accurately and meet the real-time requirement in the mobile background.
Analysis of Facial Expression Recognition of Visible, Thermal and Fused Imaginary in Indoor and Outdoor Environment

This research study performed on visible images, thermal images and fused images for facial expression recognition. Linear Discriminant Analysis has implemented for feature extraction technique and support vector machine to calculate the result. This work is implemented on a newly designed database of 20 peoples’ facial expression which includes visible images, thermal images, and fused images. The extracted features of visible, thermal and fused images are utilized for classification using support vector machine. This study focuses on 5 types of facial expression. Better results are achieved on smile and anger expression. The comparative analysis of this study is done on visible, thermal and fused facial expression images. The experimental result analysis shows that fused images give better results as compared to visible images. The accuracy of smile expression is better than anger and disgust facial expression. The implementation is carried out on dataset designed in indoor and outdoor environmental setup.