Reviewerly/exHarmony huggingface dataset:
Peran Orang Tua dalam Menumbuhkan Minat Belajar Siswa pada Pembelajaran Daring di SDN Kamulan 01 Kecamatan Talun Kabupaten Blitar

The purpose of this study was to analyze the role of parents in fostering student interest in online learning at SDN Kamulan 01. The type of research used was qualitative research with a case study approach. The subjects in this study were parents and fifth grade students of SDN Kamulan 01, totaling 25 students. Data collection techniques using questionnaires, interviews, and documentation methods. Based on the results of the study, it was concluded that the role of parents was indispensable in fostering student interest in learning. The role of parents in fostering student interest in learning shows a total score of 1014 with a percentage of 85% and is in the very high category. Students' learning interest in online learning shows a total score of 765 with a percentage of 77% and is included in the high category.
A Novel deep learning framework for improving the quality of services using block chain technology

Electronic Health Record (EHR) holds immensely sensitive information and consisting of ‎crucial data related to the patients.  Storing and organizing such data is highly arduous task.  ‎Researches still going on to improve the Quality of Service (QOS) of such data. Existing ‎study focused only on improving the system throughput, privacy and latency issues.  But they ‎did not tend to scrutinize the scalability and privacy of such data records.  In this paper, we ‎propose a novel deep learning framework to improve the Quality of Service using block chain ‎technology.  Initially the data is classified into high priority and low priority based on its ‎nature by using Recurrent Neural Network (RNN).  Then, the classified high priority data is ‎further allowed to each block of a block chain and the low priority data is stored and ‎maintained as log file. Finally, the results are compared based on the evaluation metrics ‎which demonstrates our proposed novel deep learning framework achieves better accuracy.
Message sharing through optical communication usingcipher encryption method in python

The project currently exists based on optical communication in addition to how all ofus use it to transfer encrypted messages in the form of light in addition to decrypt the same. Inthe encryption part all of us use cipher EXOR encryption method. This object over here currentlyexists mainly used to prevent leakage of data that object over there all of us currently aresending. This object over here method requires a key at both ends in order to encrypt in additionto decrypt the message. Here all of us use led to transmit the encrypted message in addition towith the help of LDR all of us get the message in the receiver end in addition to decrypt it. All ofus discovered that object over there in optical communication the data transmission currently amable to exists done through both wired medium in addition to wireless medium. From our resultsall of us currently am able to say that object over there the rate at which the transmissioncurrently exists done currently exists high. This object over here currently am able to exists usedby the military during war to communicate with each other.
StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition

Preserving the linguistic content of input speech is essential during voice conversion (VC). The star generative adversarial network-based VC method (StarGAN-VC) is a recently developed method that allows non-parallel many-to-many VC. Although this method is powerful, it can fail to preserve the linguistic content of input speech when the number of available training samples is extremely small. To overcome this problem, we propose the use of automatic speech recognition to assist model training, to improve StarGAN-VC, especially in low-resource scenarios. Experimental results show that using our proposed method, StarGAN-VC can retain more linguistic information than vanilla StarGAN-VC.
APLIKASI PENGENALAN SUARA ALAT MUSIK BETAWI MENGGUNAKAN METODE MEL-FREQUENCY CEPSTRAL COEFFISIENT BERBASIS MATLAB

Voice recognition application is Voice Recognition which means the application can recognize sound / capture sound using the MFCC method. The Mel-frequency cepstral coefficient method is one of the methods used for speech recognition that converts the sound signal into several parameters. The author has the initiative to create a speech recognition application with Betawi musical instrument objects where later applications can be run by listening to the sounds of Betawi musical instruments that have been used as WAV files. The author takes audio samples only from 2 Betawi musical instruments where each Betawi musical instrument is given 3 audio samples which will be used as training data in the application so that the application can distinguish each sound frequency in the audio sample that will be used as test data and proceed with the MFCC process so that the application can guess the audio sample of what musical instrument is read by the application using the SVM and KNN algorithm as a calcification of the results of the method process. So the end result is the application can guess what musical instruments have been heard by the application
Comparison of Encrypted Image Transfer Approaches That Provide Secure Video Transfer for UAV Systems

Today, Unmanned Aerial Vehicles (UAVs) become one of the leading technologies that are widely used for different purposes. Even though UAV systems are platform systems containing different hardware for a specific task, almost all UAV systems transmit real-time images to ground control stations (GCS). In this study it is aimed to review the methods included in the literature to ensure safe video transmission between UAV platforms and GCS and to compare their use in UAV systems by testing them on a scenario. Naive, Pure Permutation, Zigzag Permutation and Selective algorithm approaches that are among the widely used video encryption algorithms in the literature is selected as exemplary structures in this study, both because they need relatively low processing power and because they may be suitable approaches to real-time needs on mobile platforms.Within the scope of the study, a total of six performance criteria is determined and the algorithms are evaluated separately according to the performance criteria. As a result of the study, the most suitable solution for safe video transmission between UAV platforms and GCS is presented in a comparative perspective. he Selective Algorithm is selected among 4 methods examined as it is necessary to ensure the security of real-time image transfer in UAV systems as well as the fast communication between the UAV and GCS.
Butterfly Particle Swarm Optimization based Medical Image Watermarking in Frequency Domain

Digitalization of records leads to reduce load of searching and management. But in electronic health records data privacy and validation plays an important role. Watermarking is solution for such issue to get digital information for image. This paper has proposed a model that embedded a binary data into medical image by using butterfly particle swarm optimization genetic algorithm. Role of genetic algorithm is to select a random image block for embedding with an objective of minimum distortion. Embedding was done in low frequency region of image identified by DWT feature. Experiment was performed on real medical image dataset of brain cancer. In order to test proposed model different attacks were perform on embedded image such as noise, histogram, filter, etc. Result shows that proposed model has efficiently extract watermark information as compared to other comparing watermarking techniques.
A Study on Science Experimental Serious Game based on the Elementary and Middle School Curriculum : Focused on the Newton’s Laws of Motion

Smart education, a perspective which combines suitable data and technologies with the learning experience, is an important objective of Korean education. A key consideration of science education content for smart education is to create self-directed and engaging content tailored to the user based on their level and aptitude. Within science education, which relies on experimentation to teach concepts and theory, smart education content should include aspects allowing users to experiment and learn on their own. The present research aims to develop such smart education content, which meets the goals of the elementary and middle school science curricula. Newton's laws of motion was selected as the education topic, as they are the basis for physics education curriculum at the elementary and middle school level. In a traditional science classroom, a single teacher must consider and compensate for the many constraints to the experiment such as time, location, cost, safety from accidents, as well as aim to ensure that the large number of students receive equal learning opportunities. Through teaching Newton's laws of motion through a developed serious game—which satisfies early-level physics curriculum—the present research demonstrates the possibilities of experimental science education content in the era of actual smart education field.
Gel Memory Foam Baby Shoe - Stjarna Sdn. Bhd. / Siti Nurfaeza Nazeman

Stjarna Sdn. Bhd offers a comfortable and safe baby shoes product for the customer by providing the excellent features that will gives high satisfaction to the customers. The business’s customer is being defined as parents that have child aged 10 months and above and belonged to the middle-income level (M40). This product will be marketed nationwide as there are no restriction in advertising this new product by using online approach that have been so convenient these days. Stjarna Sdn Bhd marketing strategy is to focus on marketing and promoting the quality and function of the product. The unique feature that provide value to this new product will be aggressively emphasized to attract the customer to buy the product. The management of Stjarna Sdn Bhd consists of seven top management workers, one advisor and three salespersons. The management team have a great experience in the footwear industry and hold the responsibilities to accomplish the goals of this company.
Quantification of High-dimensional Energy-time Entanglement in a Biphoton Frequency Comb

We quantify high-dimensional energy-time entanglement with a filtered biphoton frequency comb. Franson interference measurements are performed, with the entanglement of formation up to 1.89 ± 0.03 ebits for a 45.32 GHz biphoton frequency comb.
Piecewise Linear Units Improve Deep Neural Networks

The activation function is at the heart of a deep neural networks nonlinearity; the choice of the function has great impact on the success of training. Currently, many practitioners prefer the Rectified Linear Unit (ReLU) due to its simplicity and reliability, despite its few drawbacks. While most previous functions proposed to supplant ReLU have been hand-designed, recent work on learning the function during training has shown promising results. In this paper we propose an adaptive piecewise linear activation function, the Piecewise Linear Unit (PiLU), which can be learned independently for each dimension of the neural network. We demonstrate how PiLU is a generalised rectifier unit and note its similarities with the Adaptive Piecewise Linear Units, namely adaptive and piecewise linear. Across a distribution of 30 experiments, we show that for the same model architecture, hyperparameters, and pre-processing, PiLU significantly outperforms ReLU: reducing classification error by 18.53% on CIFAR-10 and 13.13% on CIFAR-100, for a minor increase in the number of neurons. Further work should be dedicated to exploring generalised piecewise linear units, as well as verifying these results across other challenging domains and larger problems.
MEDIA PEMBELAJARAN KAMUS TIGA BAHASA NEGARA BERBASIS ANDROID

The purpose of this study is to create a dictionary application in three languages that can help users to make it easier to search for English, Korean and Indonesian vocabulary. Dictionary of 3 State Languages is a learning media that can provide convenience for students and the general public. The phenomenon that occurs is that students in the class have problems with foreign languages to complete the tasks given by the teacher in fulfilling the learning contributions needed in the learning process in foreign language subjects. This application is designed using UML, Eclipse and SQLite database. The design of this application is very useful for students, teachers, and even the general public. The method used in designing this application is the Waterfall System Development Life Cycle method. The stages carried out consist of data collection, analysis of problems and requirements, and testing. Data collected from Indonesian dictionaries, Japanese dictionaries, English dictionaries and preparatory software is required to build an application. The next step is to build an application with Eclipse and the last process is to complete the application and implement it to an android device which provides vocabulary translation services into three languages at once.. Keywords: Android, Foreign Language, Dictionary, Application
What Should Robots FeeI Like? Factors that Influence the Perception of Robot-Skin

It’s widely accepted that a robot’s embodiment plays an important role during human-robot interaction (HRI). While many studies have explored the effect of robot appearance, relatively little is known about how the texture and stiffness of the surface material, or what may be referred to as ‘robot-skin’, influences how the robot is perceived. Gaining improved understanding in this area may have direct and actionable consequences on robot design, since at present nearly all commercially available service robots have similar exterior surfaces composed of smooth, stiff materials, usually plastic. This study is framed around systematically investigating the type of textures that may be better suited for these robots. First, experiments were undertaken to classify the textural characteristics of 27 distinct materials which could potentially be used as a robot-skin. A representative subset of these materials was then selected for a second experiment that explored how the stiffness and tactile properties of the material influenced its perceived suitability for use on a service robot. The research found that people strongly preferred surface textures that were soft, rather than stiff. The most suitable material stiffness was found to be context dependent; soft options were preferred in the blind test condition, but for cases where participants were presented with the 3D image of a service robot in an immersive virtual reality environment, medium stiffness materials were preferred. In the final part of the study, we identified a range of textural properties that seem to correlate with high and low suitability for use on service robots. It is hoped that these findings are useful to help inform the design of future HRI systems, and motivate further investigation into the social roles of robot-skin. CCS CONCEPTS•Computer systems organization →External interfaces for robotics; • Human-centered computing →User studies; Virtual reality;Empirical studies in HCI; Systems and tools for interaction design. ACM Reference Format: Conor McGinn and Dylan Dooley. 2020. What Should Robots Feel Like? Factors that Influence the Perception of Robot-Skin. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’ 20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3319502.3374835
On the Robustness of Domain Adaption to Adversarial Attacks

State-of-the-art deep neural networks (DNNs) have been proved to have excellent performance on unsupervised domain adaption (UDA). However, recent work shows that DNNs perform poorly when being attacked by adversarial samples, where these attacks are implemented by simply adding small disturbances to the original images. Although plenty of work has focused on this, as far as we know, there is no systematic research on the robustness of unsupervised domain adaption model. Hence, we discuss the robustness of unsupervised domain adaption against adversarial attacking for the first time. We benchmark various settings of adversarial attack and defense in domain adaption, and propose a cross domain attack method based on pseudo label. Most importantly, we analyze the impact of different datasets, models, attack methods and defense methods. Directly, our work proves the limited robustness of unsupervised domain adaptation model, and we hope our work may facilitate the community to pay more attention to improve the robustness of the model against attacking.
Anomaly Detection Based on Video Prediction and Latent Space Constraints

Anomaly detection has wide applicability in areas such as security, deception detection, and video surveillance. For video, anomaly detection usually refers to the discovery of events outside the expected behavior. Most existing anomaly detection methods are implemented using reconstruction errors, but they cannot ensure that abnormal events have sufficient reconstruction errors. To avoid these problems, this article proposes a new anomaly detection model based on video prediction that adopts dual autoencoder structure, including a discriminator and generator. The use of the same structure in the discriminator and generator can effectively prevent instability in generative adversarial network training. Use optical flow to extract the time information of the video, and use latent space constraints, gradient constraints, and intensity constraints to improve the quality of the generated prediction frames. In addition, group normalization was employed for further optimization of the overall model performance. Excellent experimental results were achieved using the Chinese University of Hong Kong, University of California—San Diego, Shanghai-Tech, and UCF_Crime100 datasets.
Measurement of Entropy and Quantum Coherence Properties of Two Type-I Entangled Photonic Qubits

Abstract Using the type-I SPDC process in BBO nonlinear crystal (NLC), we generate a polarization-entangled state near to the maximally-entangled Bell-state with high-visibility (high-brightness) 98.50 ±1.33 % (87.71 ± 4.45 %) for HV (DA) basis. We calculate the CHSH version of the Bell inequality, as a nonlocal realism test, and find a strong violation from the classical physics or any hidden variable theory (HVT), S= 2.71±0.10. Via measuring the coincidence count (CC) rate in the SPDC process, we obtain the quantum efficiency of single-photon detectors (SPDs) around (25.5 ±3.4)% , which is in good agreement to their manufacturer company. As expected, we verify the linear dependency of the CC rate vs. pump power of input CW-laser, which may yield to find the effective second-order susceptibility crystal. Using the theory of the measurement of qubits, includes a tomographic reconstruction of quantum states due to the linear set of 16 polarization-measurement, together with a maximum-likelihood-technique (MLT), which is based on the numerical optimization, we calculate the physical non-negative definite density matrices, which implies on the non-separability and entanglement of prepared state. By having the maximum likelihood density operator, we calculate precisely the entanglement measures such as Concurrence, entanglement of formation, tangle, logarithmic negativity, and different entanglement entropies such as linear entropy, Von-Neumann entropy, and Renyi 2-entropy. Finally, this high-brightness and low-rate entangled photons source can be used for short-range quantum measurements in the Lab.
Corrections to “Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , in <xref ref-type="table" rid="table4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table 4</xref> , the operator * was used to represent frame sizes, which are 2 * 128 and 4 * 128. The correct operator should be a multiplication operator <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 128$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 128$ </tex-math></inline-formula> .
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning

Sparse rewards and long time horizons remain challenging for reinforcement learning algorithms. Exploration bonuses can help in sparse reward settings by encouraging agents to explore the state space, while hierarchical approaches can assist with long-horizon tasks by decomposing lengthy tasks into shorter subtasks. We propose HAC Explore (HACx), a new method that combines these approaches by integrating the exploration bonus method Random Network Distillation (RND) into the hierarchical approach Hierarchical Actor-Critic (HAC). HACx outperforms either component method on its own, as well as an existing approach to combining hierarchy and exploration, in a set of difficult simulated robotics tasks. HACx is the first RL method to solve a sparse reward, continuous-control task that requires over 1,000 actions.
Design and simulation of skate scooter made of composite material

In modern days, the challenge is to beat the heavy traffic in the metropolitan cities, the need of the hour to find an alternate mode of transport with low cost and highly efficient transport to skip traffic and to save nature with zero carbon emission. The alternate transport for skipping the traffic with zero carbon emission is the way to suggest to travel with Skate Scooter made of Composite Material. Design validation based on various design aspects and simulating the skate scooter to be free from the commonly caused defects by the user handling and make the scooter user friendly. The main aim of the research is to identify the correct material for the base board to sustain in heavy load with minimal amount of wear and tear during the user handling. Studying the various material aspects for the base board to find out which is the best suitable material for the board to carry the maximum load. The design of skate scooter created using SolidWorks. The dynamic analysis of the Skate Scooter is performed using the Abaqus CAE which contains all the three major steps of Simulation pre-processing, analysis and post-processing. The input parameter for the existing model and new design model are same as follows with load of 80 kg and the velocity of 0.01 m/s. The material used in the trail study for the design of the board AISI Steel 1040 and 12% Maple wood. From the analysis results, it is concluded that joining of the board which is moulded with the base board causing failure. Therefore, the new design which provides distributed stress concentration over the screw mounted joint is reduced to half of the existing model.