Reviewerly/exHarmony huggingface dataset:
Modular network for high accuracy object detection

We present a novel modular object detection convolutional neural network that significantly improves the accuracy of object detection. The network consists of two stages in a hierarchical structure. The first stage is a network that detects general classes. The second stage consists of separate networks to refine the classification and localization of each of the general classes objects. Compared to a state of the art object detection networks the classification error in the modular network is improved by approximately 3-5 times, from 12% to 2.5 %-4.5%. This network is easy to implement and has a 0.94 mAP. The network architecture can be a platform to improve the accuracy of widespread state of the art object detection networks and other kinds of deep learning networks. We show that a deep learning network initialized by transfer learning becomes more accurate as the number of classes it later trained to detect becomes smaller.
Introducing a New Hybrid Adaptive Local Optimal Low Rank Approximation Method for Denoising Images

This paper aimed to formulate image noise reduction as an optimization problem and denoise the target image using matrix low rank approximation. Considering the fact that the smaller pieces of an image are more similar (more dependent) in natural images; therefore, it is more logical to use low rank approximation on smaller pieces of the image. In the proposed method, the image corrupted with AWGN (Additive White Gaussian Noise) is locally denoised, and the optimization problem of low rank approximation is solved on all fixed-size patches (Windows with pixels needing to be processed). This method can be implemented in parallelly for practical purposes, because it can simultaneously handle different image patches. This is one of the advantages of this method. In all noise reduction methods, the two factors, namely the amount of the noise removed from the image and the preservation of the edges (vital details), are very important. In the proposed method, all the new ideas including the use of TI image (Training Image) and SVD adaptive basis, iterability of the algorithm and patch labeling have all been proved efficient in producing sharper images, good edge preservation and acceptable speed compared to the state-of-the-art denoising methods.
MENINGKATKAN MOTIVASI BELAJAR PKn MELALUI PEMBELAJARAN COOPERATIF LEARNING TIPE INDEX CARD MATCH SISWA KELAS VI

This research is motivated by the low motivation to learn class VI students of SD Negeri 021 Tarai Bangun Kampar Regency. This study aims to improve the motivation to study in Class VI students of SD Negeri 021 Tarai Bangun Kampar Regency by using Cooperative Learning Type Index Card Match. The subjects of this study were all Class VI students of SD Negeri 021 Tarai Bangun Kampar Regency, totaling 32 people. The form of research is classroom action research. The research instrument consisted of performance instruments and data collection instruments in the form of teacher activity observation sheets and student activities. The results showed, in Cycle I the research score reached a score of 132 in the high criteria, with an average student learning motivation for each indicator of learning motivation of only 68.8%. While the observations of learning motivation in cycle II reached a score of 163 (in very high criteria), with an average student learning motivation for learning motivation indicators of 84.9%. thus it can be concluded that through cooperative learning index card match type can increase students' PKn learning motivation.
A Privacy-Preserving Access Control Scheme with Verifiable and Outsourcing Capabilities in Fog-Cloud Computing

Fog computing is a distribution system architecture which uses edge devices to provide computation, storage, and sharing at the edge of the network as an extension of cloud computing architecture, where the potential network traffic jams can be resolved. Whereas, the untrustworthy edge devices which contribute the computing resources may lead to data security and privacy-preserving issues. To address security issues and achieve fine-grained access control to protect privacy of users, ciphertext-policy attribute-based encryption (CP-ABE) mechanism has been well-explored, where data owners obtain flexible access policy to share data between users. However, the major drawback of CP-ABE system is heavy computational cost due to the complicated cryptographic operations. To tackle this problem, we propose a privacy-preserving access control (PPAC) scheme and the contributions are tri-folded: (1) we introduce outsourcing capability in fog-cloud computing (FCC) environment; (2) the outsource verification mechanism has been considered to guarantee the third party execute the algorithm correctly; (3) we design a partiality hidden method to protect the privacy information embedded in the access structures. The experimental results show that our proposed PPAC is efficient, economical and suitable for mobile devices with limited resources.
Weakly Supervised Learning for Blind Image Quality Assessment

The blind image quality assessment (BIQA) metric based on deep neural network (DNN) achieves the best evaluation accuracy at present, and the depth of neural networks plays a crucial role for deep learning-based BIQA metric. However, training a DNN for quality assessment is known to be hard because of the lack of labeled data, and getting quality labels for a large number of images is very time consuming and costly. Therefore, training a deep BIQA metric directly will lead to over-fitting in all likelihood. In order to solve this problem, we introduced a weakly supervised approach for learning a deep BIQA metric. First, we pre-trained a novel encoder-decoder architecture by using the training data with weak quality annotations. The annotation is the error map between the distorted image and its undistorted version, which can roughly describes the distribution of distortion and can be easily acquired for training. Next, we fine-tuned the pre-trained encoder on the quality labeled data set. Moreover, we used the group convolution to reduce the parameters of the proposed metric and further reduce the risk of over-fitting. These training strategies, which reducing the risk of over-fitting, enable us to build a very deep neural network for BIQA to have a better performance. Experimental results showed that the proposed model had the state-of-art performance for various images with different distortion types.
Understanding the Resource Demand Differences of Deep Neural Network Training

More deep neural networks (DNN) are deployed in the real world, while the heavy computing demand becomes an obstacle. In this paper, we analyze the resource demand differences of DNN training and help understand its performance characteristic. In detail, we study both shared-memory and message-passing behavior in distributed DNN training from layer-level and model-level perspectives. From layer-level perspective, we evaluate and compare basic layers’ resource demand. From model-level perspective, we measure parallel training of representative models then explain the causes of performance differences based on their structures. Experimental results reveal that different models vary in resource demand and even a model can have very different resource demand with different input sizes. Further, we give out some observations and recommendations on performance improvement of on-chip training and parallel training.
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement

Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In many applications, however, raw data usually contains more than one kind of weak supervision at the same time. For example, in user experience enhancement from Didi, one of the largest online ride-sharing platforms, the ride comment data contains severe label noise (due to the subjective factors of passengers) and severe label distribution bias (due to the sampling bias). We call such a problem as "compound weakly supervised learning". In this paper, we propose the CWSL method to address this problem based on Didi ride-sharing comment data. Specifically, an instance reweighting strategy is employed to cope with severe label noise in comment data, where the weights for harmful noisy instances are small. Robust criteria like AUC rather than accuracy and the validation performance are optimized for the correction of biased data label. Alternating optimization and stochastic gradient methods accelerate the optimization on large-scale data. Experiments on Didi ride-sharing comment data clearly validate the effectiveness. We hope this work may shed some light on applying weakly supervised learning to complex real situations.
Edmodo Based e-Learning Development on Science Lesson in 7th Grade

This research aims to produce Edmodo-based e-learning that is feasible and effective to improve science learning outcomes. This research uses a modified Borg &amp; Gall (1983) model. Data collection tools are in the form of questionnaire and test. Data analysis technique used is descriptive analysis. The results showed that the average validation score (1) of the media expert was 4.6 (very valid); (2) peers were 4.5 (very valid); (3) the material expert was 4.5 (very valid); (4) user limited test group is 3.8 (valid); (5) implementation group is 4.1 (valid). To test the effectiveness of e-learning and interactive learning media in improving learning outcomes in Natural Science learning for 7th graders. Based on the pretest results, it obtained an average value of 5.80, then on the posttest results showed an average increase to 75.60. Based on the results of research, it can be concluded that Edmodo-based e-learning developed is feasible and effective to be used in Natural Science learning.
PEER TUTORING IN A PROJECT-BASED COURSE PART 2: THE SECOND YEAR OF IMPLEMENTATION - IMPROVEMENTS AND BENEFITS

In Fall 2017, a peer-tutoring program (PTP) was implemented in a first-year multidisciplinary design course of the mechanical, electrical and electromechanical engineering programs at Université du Québec à Rimouski. Fourth-year students with relevant design experience acted as tutors for teams of first-year students. The intent was to reduce the pressure on our professional staff, while maintaining the quality of supervision. &#x0D; PTP assessment revealed that all stakeholders appreciated the experience. At low cost, it allowed more weekly hours of supervision and freed up time for professional staff to perform tasks requiring their expertise. Although the impact of the PTP on the quality of learning was not directly measured, the student level of satisfaction was very high. However, the teaching team concluded from PTP assessment results that tutors could have more impact on teamwork and team spirit if they were better prepared. Four main areas for improvement of the PTP have been identified. Modifications for improvement of the PTP were developed as follows. Tutor training was modified to include content on teamwork, group dynamics and conflict resolution. To enhance communication between tutors and teaching team, tutors were provided with a new week-by-week guide outlining project planning and giving tutors instructions for structuring team discussions. Tutors participated in a professional co-development session in the fifth week of the project, an activity that allowed tutors to learn from each other and improve their practice. Lastly, the assessment questionnaire was improved to collect more significant data on student learning and teamwork.&#x0D; The new assessment results reveal that the changes made to the PTP have had a very positive influence on teamwork and group dynamics. Results also indicate that the improved PTP has a positive impact on achievement of course objectives. In conclusion, peer tutoring is evidently a very good strategy for supporting first-year students in the development of their design skills.&#x0D;
Robust Hybrid Control Algorithm for Tuning the Altitude and Attitude of Unmanned Aerial Vehicle

In this article, a new and novel robust hybrid control algorithm is designed for tuning the parameters of unmanned aerial vehicle (UAV). The quadrotor type UAV mathematical model is taken to observe the effectiveness of our designed robust hybrid control algorithm. The robust hybrid control algorithm consists of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> based regulation, pole-placement and tracking (RST) controller along with mixed sensitivity function is applied to control the complete model of UAV. The selected rotor craft is under-actuated, nonlinear and multivariable behavior in nature along with six degrees of freedom (DOF). Due to all these aforementioned issues its stabilization is quite difficult as compared to fully actuated systems. For the tuning of nonlinear parameters of the UAV, we designed, robust hybrid control algorithm is used. Moreover, the performance of the designed controller is compared with robust controller. The validity and effectiveness of the designed controllers are simulated in MATLAB and Simulink, in which the designed controller shows better steady state behavior, robustness and converges quickly in specific amount of time as compared to robust controller.
DEVELOPMENT AND INVESTIGATION OF INPUT AMPLIFIER FOR THE OSCILOSCOPE

Digital oscilloscope’s structure has analog signal acquisition circuit, which transforms signal’s amplitude to fit ADC dynamic range. This circuit is commonly called oscilloscope’s vertical or front-end amplifier. Difficulty in designing front-end amplifiers in GHz range largely affects higher frequency range oscilloscope’s price. This work is focused on designing a front-end amplifier using discrete and openly sold components. We propose a design for attenuator, buffer, variable gain circuits. Amplifier’s prototype is designed. Main characteristics of the amplifier were measured. Measured bandwidth is 3 GHz. Amplifier’s gain and attenuation can support vertical scale sensitivity range from 10 mV/div to 1 V/div. Step response distortion is under 10 %. SMD and PTH relay model attenuators were evaluated. In this paper we review oscilloscope’s front-end purpose and structure. We review amplifiers design and provide the results of experimental measurements.
IMPACT OF SENIOR MANAGEMENT SUPPORT, EMPLOYEES' INVOLVEMENT, AND OPEN CORPORATE CULTURE ON THE MOTIVATION OF EMPLOYEES: AN EMPIRICAL STUDY

This study is aimed to investigate the impact of senior management support, employees’ involvement and open corporate culture on the motivation of employees in the education sector of Pakistan. To collect the primary data, a self-administrated questionnaire is used while for secondary data collection research journals and articles are used. The sample size of this study consists of two hundred employees (hundred from public and hundred from private institutes). SPSS is used for measuring the association between variables and to see the impact of senior management support, open corporate culture, and involvement of employees on the motivation of employees. Correlation analysis and regression analysis are used to test the hypothesis of the research model. The motivation of the employees mainly depends upon senior management support and employees' involvement in decision making. Findings of the results support hypothesis 1, 2 &amp; 3 while disproving hypothesis 4. Senior management support and employees’ involvement in decision making have a significant and positive impact on the motivation of employees. And the open corporate culture has no meaningful impact on employees’ motivation.
PER IL CENTESIMO ANNIVERSARIO DELLA TEORIA DELLE RELAZIONI STATISTICHE DI CORRADO GINI

This Note aims at highlighting Giniâ€™s contributions to denition and measurement of the intensity of a statistical relationship, on the occcasion of the centennial anniversary of the publication of his early papers on that topic. The Note stresses the precursory value of those contributions and mentions some of their most signicant developments apropos of metrization of spaces of probability distributions and anaysis of monotone dependence.
Bir Büyükşehir Belediyesi Çağrı Merkezi Verilerinin Kümeleme Analizi ile İncelenmesi

One of the most important issues in statistical data analysis is big data. The process of accessing useful information from big data is called data mining. In clustering analysis, which is one of the data mining methods, via different algorithms, it is desirable to include similar data in the same cluster and divergent data in different clusters. Hierarchical clustering and k-means methods are the most commonly used algorithms in clustering analysis. In this study, call center data of five districts in a metropolitan municipality with a high population density in Turkey were examined. Call center data were clustered according to seven different variables by k-means method. These variables are respective department, application type, application district, education level, gender, age and instant solution. SPSS Clementine and WEKA which are the data mining package programs were used for the analysis and the results were compared and interpreted.
Dynamic Variational Autoencoders for Visual Process Modeling

This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We propose a joint learning framework, combining a vector autoregressive model and Variational Autoencoders. This results in an architecture that allows Variational Autoencoders to simultaneously learn a non-linear observation as well as a linear state model from sequences of frames. We validate our approach on artificial sequences and dynamic textures.
IMAGE RESTORATION BY IMPROVED ITERATIVE SHRINKAGE THRESHOLDING ALGORITHM

The problem of restoration of digital images plays a central role in multitude important applications.A particularly challenging instance of this problem occurs in the case when the degradation phenomenon is modelled by ill-conditional operator.In such situation, the presence of noise makes it impossible to recover a valuable approximation of the image of interest without using some priori information called as simply priors is essential for image restoration, rendering it stable and robust to noise.Particularly, if the original image is known to be a piecewise smooth function, a total variation (TV) based image restoration can be applied.This paper proposes an algorithm for unconstrained optimization problem where the objective function includes a data fidelity term and a nonsmooth regulaizer.Total Variation method is employed to find solution of the problem based on the Improved Iterative Shrinkage Thresholding Algorithm (IISTA).IISTA is performed through a recursive application of two simple procedures linear filtering and soft thresholding.An experimental result shows that proposed algorithm performs well when compared with the existing methods.
Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Procedures

The continuous development and prolonged use of X-ray fluoroscopic imaging in cardiac catheter-based procedures is associated with increasing radiation dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for low-dose X-ray images are needed to improve safety and reliability. Previous X-ray imaging denoising methods mainly rely on domain filtration and iterative reconstruction algorithms and some remaining artifacts still appear in the denoised X-ray images. Inspired by recent achievements of convolutional neural networks (CNNs) on feature representation in the medical image analysis field, this paper introduces an ultra-dense denoising network (UDDN) within the CNN framework for X-ray image denoising in cardiac catheter-based procedures. After patch-based iterative training, the proposed UDDN achieves a competitive performance in both simulated and clinical cases by achieving higher peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) when compared to previous CNN architectures.
PCC: A Leader of Change

From its beginnings, the Program for Cooperative Cataloging (PCC) has been an international leader of change in bibliographic and authority control and maintenance, and metadata creation and distribution. This article highlights some PCC achievements in these areas.
TindArt, an Experiment on User Profiling for Museum Applications

In this paper an Android application called TindArt is presented. It has been developed to investigate a way to profile the user in cultural contexts, through the application of Recommender Systems for museum visits in the future. The purpose of the research also includes the study of the User Experience with TindArt to understand how it could be used in a real museum context. Two pilot studies are also presented.
PENINGKATAN PEMAHAMAN POTENSI DIRI MELALUI LAYANAN INFORMASI BIDANG BIMBINGAN PRIBADI PADA SISWA KELAS XI IPA 2 SMA N 1 CEMPAGA TAHUN 2015/2016

This research aim to to know there is not the make-up of the understanding of potency of x'self of student of class of XI IPA 2 passing service of personal tuition area information in SMA N 1 Cempaga school year 2015 / 2016. Subyek in this research is student of class of XI IPA 2 SMA N 1 Cempaga school year 2015 / 2016 amounting to 35 student. technique of Intake sampel use the saturated technique sampel. Hypothesis raised in this research is there is the make-up of the understanding of potency of x'self of student of class of XI IPA 2 through service of personal tuition area information in SMA N 1 Cempaga [of] School Year 2015 / 2016. Data collecting conducted to use the scale of[is understanding of potency of student x'self, while method used for the examination of hypothesis is test t-test with the technique of paired sample t-test.Hasil analyse obtained [by] value thitung = 10,361 > ttabelpada signifikansi 5% that is 2,0322. This means there is the make-up of the understanding of potency of student x'self of passing service of personal tuition area information.
Low-Complexity LSTM Training and Inference with FloatSD8 Weight Representation

The FloatSD technology has been shown to have excellent performance on low-complexity convolutional neural networks (CNNs) training and inference. In this paper, we applied FloatSD to recurrent neural networks (RNNs), specifically long short-term memory (LSTM). In addition to FloatSD weight representation, we quantized the gradients and activations in model training to 8 bits. Moreover, the arithmetic precision for accumulations and the master copy of weights were reduced from 32 bits to 16 bits. We demonstrated that the proposed training scheme can successfully train several LSTM models from scratch, while fully preserving model accuracy. Finally, to verify the proposed method's advantage in implementation, we designed an LSTM neuron circuit and showed that it achieved significantly reduced die area and power consumption.