Reviewerly/exHarmony huggingface dataset:
Desain Dan Implementasi Customer Relationship Management Berbasis Web (Studi Kasus : Toko Baju Ladya)

Internet Users in Indonesia each year is growing, from the data collected by the Association of Internet Service Users Indonesia said that internet Users in Indonesia in 2013 reached 82 million, this is only 30% of the population of Indonesia.Likewise with e-commerce business, the rapid growth of e-commerce market share in Indonesia is already no doubt.With the number of internet Users reaching 82 million people or about 30% of the total population in Indonesia, e-commerce market becomes a gold mine that is very tempting for some people who can see the potential for the future.The purpose of this research is to design the Website of the shop so that it can increase Sales and also can know the profit and loss from the sale.The method for application development is Prototype and for methods to increase Sales is the method of Customer Relationship Management (CRM).The reason for the use of Prototype is in the process of making the system more listening to the User's wish so that the results of research conducted will be as expected.Supporting tools used in this research are, PHP programming language, and MySQL database.As for writing code program using Codeigniter.The result of this research is the ladya store Website.
Machine Learning Model for Revealing the Characteristics of Soil Nutrients and Aboveground Biomass of Northeast Forest, China

Declining soil quality and climate change may affect species diversity and forest biomass productivity in many temperate regions in the future.Our research objective is to reveal the characteristics of soil nutrients and biomass of forests in Northeast China with climate change.The purpose of this study was to determine the soil physical and chemical properties of mature broad-leaved forest in the cold temperate zone of Mt.Changbai, Jilin Province, by measuring pH, NH 4 + , organic matter (%), C/N, available phosphorus, alkali-hydrolysable N, rapidly available K, and Cr etc., analysing species diversity characteristics, and estimating aboveground biomass (AGB) of tree species with machine learning models.The results showed that with the increase of soil depth, the soil physical and chemical parameters have a decreasing trend; with the increase of soil depth, the soil nutrient content decreased; the main tree species were the Acer barbinerve (6937), Carpinus cordata Bl. ( 6682) and Acer mandshuricum Maxim.( 5447) etc.The total difference (SOR) showed a similar trend in the four directions and central point; the reference sample size at central point, north, west, south and east direction was 903, 954, 971, 1005 and 1016, respectively; GRNN model was the relatively best model among these models for modelling the aboveground biomass of the trees.Therefore, the diversity of tree species in north-eastern forests was affected by soil nutrients, climate change also has a significant impact on the aboveground biomass of northeast forests, which provides a theoretical basis for the management of northeast forests about soil physical and chemical properties and species diversity.
From Reinforcement Learning Towards Artificial General Intelligence

The present work surveys research that integrates successfully a number of complementary fields in Artificial Intelligence. Starting from integrations in Reinforcement Learning: Deep Reinforcement Learning and Relational Reinforcement Learning, we then present Neural-Symbolic Learning and Reasoning since it is applied to Deep Reinforcement Learning. Finally, we present integrations in Deep Reinforcement Learning, such as, Relational Deep Reinforcement Learning. We propose that this road is breaking through barriers in Reinforcement Learning and making us closer to Artificial General Intelligence, and we share views about the current challenges to get us further towards this goal.
How To Deal With The Awareness Of Cyber Hazards And Security In (Higher) Education

Cyber Security of information systems and infrastructure has turned out to be one of the most important issues in recent years. Many people, both children and adults, use portable devices such as mobile phones and tablets in their day to day life, to access computer networks that are connected through the Internet. However, as internet use includes the use of many tools that use shared applications, such as navigation, access to information, trends in social networks, news content, entertainment and office applications (e-mail, calendar, etc.), it has also become an arena for hazards such as user identity theft, privacy sabotage, malicious code, cyber bullying and others. Whilst cyber security has been researched in the literature, few studies addressed cyber security awareness and users’ indifference in cyber security behavior. Moreover, the awareness of cyber security threats among various clusters of users (such as IT experts vs. non-experts, older vs. younger users) has not been fully evaluated. The aim of this paper is to enhance cyber awareness of individuals and thereby equip and prepare them for safer and better work and life, by providing theoretical and practical solutions related to cyber security awareness of various clusters of users, such as elementary and secondary school children, students, employees and retired people. Therefore, the objectives of this paper are as follows: 1. Develop recommendations for curricula changes at each level of the evaluated education systems and practice. 2. Develop a cyber awareness education framework (CAEF) using suggested recommendations for curricula changes at all levels of education. We expect to understand which factors are involved in the cyber awareness gap and how to narrow or even eliminate the gap through specific recommendations, i.e. how to improve the cyber awareness of individuals in the curricula at various levels of the education system
Digital Video Watermarking Methods Using DCT and SVD

In this search, two methods were used to include the watermark in the video.The first method was based on DCT (Discrete Cosine Transform), the second method was based on an algorithm SVD (Singular Value Decomposition) for the purpose of converting video to frequency domain.The process of embedding the watermark in both methods was done after the original video was divided into a set of frames, and one frame was divided into a block of 8 x 8 and the DCT on each block when using the first method and the SVD algorithm when using the second method.And then include the Bit Binary for the watermark inside the center of the cluster.Random selection of video frames and rows of watermark images has been adopted in both ways.The performance of the two methods was assessed using the experimental tests PSNR, MSE and NC.The experimental results show that both methods have achieved a good understanding and high resistance against various attacks, adopted Matlab 2013a language.
Boosting of Denoising Effect with Fusion Strategy

Image denoising, a fundamental step in image processing, has been widely studied for several decades. Denoising methods can be classified as internal or external depending on whether they exploit the internal prior or the external noisy-clean image priors to reconstruct a latent image. Typically, these two kinds of methods have their respective merits and demerits. Using a single denoising model to improve existing methods remains a challenge. In this paper, we propose a method for boosting the denoising effect via the image fusion strategy. This study aims to boost the performance of two typical denoising methods, the nonlocally centralized sparse representation (NCSR) and residual learning of deep CNN (DnCNN). These two methods have complementary strengths and can be chosen to represent internal and external denoising methods, respectively. The boosting process is formulated as an adaptive weight-based image fusion problem by preserving the details for the initial denoised images output by the NCSR and the DnCNN. Specifically, we design two kinds of weights to adaptively reflect the influence of the pixel intensity changes and the global gradient of the initial denoised images. A linear combination of these two kinds of weights determines the final weight. The initial denoised images are integrated into the fusion framework to achieve our denoising results. Extensive experiments show that the proposed method significantly outperforms the NCSR and the DnCNN both quantitatively and visually when they are considered as individual methods; similarly, it outperforms several other state-of-the-art denoising methods.
Human-Fall detection using Video surveillance in an Indoor Domain

This paper provides an overview on the concept of detection of human-falling using video surveillance technique. Seeing the current scenario, people are not able to devote time to assist the elderly, children and those under medical treatment; thus, there is a need of surveillance system that can help individuals to be informed in case there is an emergency. Human Fall Detection can be done using a video sequence and then analyzing it to infer if there is a human shape deformation. This technique involves the use of multiple cameras in a room to detect the human fall. It uses Deep Learning and Gaussian Mixture Model to improvise and train. Firstly, the system is trained using CNN model on previous human fall images in that area. Then, the motion is monitored using cameras and then calculations are done by syncing the video recordings to check if there is sudden change in motion. If the change is encountered, then the alarm is initiated to intimate the people concerned.
Report-writing Disposition and Achievement Orientation: An exploratory research on MUET students

This paper aimed to gauge the MUET students’ report-writing dispositions and achievement orientation as well as examine the influence of selected demographic variables on these two variables. The research was conducted on two groups of MUET students (79 students) in a government secondary school in Kuching. The findings indicated that the multi-item writing disposition and achievement orientation were reliable. These two variables were having a weak association. The report-writing dispositions of the surveyed MUET students were generally low, but their achievement orientations were satisfactory. The demographic variables had significant influences on their report-writing disposition, but not their achievement orientations. Keywords: Report-writing disposition; Achievement orientation; MUET English. eISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI1.2316
System Fusion with Deep Ensembles

Deep neural networks (DNNs) are universal estimators that have achieved state-of-the-art performance in a broad spectrum of classification tasks, opening new perspectives for many applications. One of them is addressing ensemble learning. In this paper, we introduce a set of deep learning techniques for ensemble learning with dense, attention, and convolutional neural network layers. Our approach automatically discovers patterns and correlations between the decisions of individual classifiers, therefore, alleviating the difficulty of building such architectures. To assess its robustness, we evaluate our approach on two complex data sets that target different perspectives of predicting the user perception of multimedia data, i.e., interestingness and violence. The proposed approach outperforms the existing state-of-the-art algorithms by a large margin.
Digital Native on Information Technology Acceptance in Higher Education

The purpose of this study is to find convergence between students as digital natives and university managers as digital immigrants in Higher Education (HE) managing information technology to resolve digital divide between information technology users.The research methodology used in this study is quantitative research to measure Technology Acceptance Model (TAM).The results of the TAM analysis are followed by the formulation of an agenda which is considered important for overcoming issues relating to technology acceptance.The results of research on the acceptance of information technology applied by HE shows that student perceptions have a value that exceeds the ideal conditions on the variable usefulness, and intention to use, but do not meet the ideal conditions on the variable ease of use, and actual system use.Testing the research hypothesis shows that partially and simultaneously the ease of use, usefulness, and intention to use variables have a positive and significant effect on actual system use.Based on the problems in the ease to use variable, it is necessary to improve the aspects of human and computer interaction on the information technology used, including the interaction model, features, and information system usage procedures.This improvement based on the structure of relationships between variables will increase the actual system use.Improvement models in information systems can use Agile manifesto, which emphasizes the development of information systems more attention to the perspectives and opinions of users in designing information systems.
Analisis Framing Pemberitaan Banjir Jakarta Januari 2020 Di Harian Kompas.Com Dan Jawapos.Com

On January 2, 2020, the day after the widespread flood disaster in the Jakarta area occurred, the mass media gave a special portion to the Jakarta flood news.This can be identified from the use of Jakarta flood events as headlines.The mass media always framing (reported) events that are reported based on the perspective and interest of the media concerned.This journal will discuss about the framing of news about the January 2020 Jakarta flood event in Kompas.comand Jawapos.com.The purpose of this study was to determine the construction of the January 2020 Jakarta flood reporting in Kompas.comand Jawapos.com.The research method used is Robert N. Entman's framing analysis model, Urs Dahinden's frame base, and framing grouping from Shanto Iyengar.From the research conducted, this journal gets findings and discussions about the different framing used by each media in constructing news about the January 2020 Jakarta flood.Kompas.com is demanding, questioning, and suing the powerlessness of the local government in dealing with the January 2020 Jakarta flood which was built using the basis of moral and ethical frames that are dominant in the reporting.Jawapos.comconstructed news about the January 2020 Jakarta floods with a frame to build a good image of the performance of local governments.This was done by using frame personalization base in highlighting the efforts of local governments in handling 12
Genetic Algorithm Based Solution for Final Exam Scheduling

The automatic generation of schedules has been in focus of researches for decades.Since small changes in the input have exponential impact on the tested state space, methods based on heuristics and artificial intelligence are the most successful.In our paper we analysed the opportunities of automatic generation of the academic final exam scheduling, which is a special subtask of schedule generation.We examined the problem with the genetic algorithm approach.We created a point system, wherewith the goodness of the generated schedules is measurable and comparable.We analysed the effect on achievable results caused by the different changes on our algorithm.We evaluated the algorithms on a real test set of 100 students.Our results show that for this complexity there is an acceptable solution, and with some improvements on our algorithm we will be able to give solutions, which are better and fairer, than the manually compiled schedules.
The role of V. Lihin's scientific publications in international journals

In the 21st century, we live in the stage of human history when any information is as accessible as possible. The modern scientific world is based on an extensive network of journals. Thanks to them, we have the opportunity to get acquainted with the latest research results. In the 21st century, every reputable journal also has an online version, which makes the dissemination of scientific information almost instantaneous. We are so accustomed to the conveniences of the information age that it is difficult for us to imagine the difficulties that scientists faced a little over 150 years ago. In the second half of the nineteenth century, almost most of the sciences known to mankind were formed. The genesis of science launched the process of forming branch scientific communities and demanded stable ways of communication for productive and effective development of the branch. Scientific journals have become an ideal means of disseminating information, and a scientific article has been transformed from an ordinary letter into a modern form and has taken on an ideal form. Given all the above, it is important to consider the experience of V. Lihin in publishing articles in scientific journals abroad. Valerian Mykolaiovych was the first scientist from Odessa to join a new type of scientific communication. He became the first Russian-speaking member of one of the oldest Mathematical Societies in Europe - the French. The young mathematician conquered with his erudition, attention to detail, thorough approach and comprehensive analysis of research issues. During his life, V. Lihin wrote more than 30 scientific papers, a third of which were presented in scientific journals abroad. These were complete author's translations or articles based on part of the study. Not infrequently, on the pages of magazines, you could see scientific discussions, in which our compatriot also took an active part. In Odessa, the fate of Valerian Mykolaiovych changed quite radically several times: he was a professor and dean of the Faculty of Physics and Mathematics of Novorossiysk University; an adviser of the mayor, and later the mayor; became a trustee of the Warsaw Educational District. And in Europe, his authority as a scientist was highly valued. He showed that science has no state borders, that it can develop harmoniously - imitating the results of research from all over the world. V. Lihin broke the tradition of "isolated" science when discoveries in the Russian Empire (and later in the USSR) were made separately from the rest of the world. Scientists, who speak the language of science - speak the language of peace and development. And so, in 2020, the language of medical science has united the world despite its borders, nations and religions.
Hybrid-AES-Blowfish algorithm: key exchange using neural network

Crypto-system hybrid is method using symmetrical for secure file and using symmetrical algorithm to secure key symmetrical algorithm, dependency key public for security file is one of things important for avoid occurrence attack, at this research we proposed Neural Network for composing key public that evaluated with 1000 example plaintext, based on result testing level accuracy realize 99.98%, formation of the key made as input data at AES, Blowfish, and Hybrid algorithm for secure signature file.
Content-aware Upsampling for Single Image Super-resolution

Super-resolution reconstruction has recently become a hotspot in digital image processing. It has been widely used in medical imaging, video surveillance security, high-definition television terminals, and other fields. In this paper, our group propose an image super-resolution method based on content-aware upsampling. In our neural network, the convolution kernel does not share parameters across the entire feature map, but the network generates specific convolution kernels at each pixel according to the content of the feature map. We use residual dense module as the backbone network. The following data shows that our method has more potential on training and testing datasets and the reconstructed image is more clearer than the sub-pixel convolution layer method.
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?

Deep neural networks are typically initialized with random weights, with variances chosen to facilitate signal propagation and stable gradients. It is also believed that diversity of features is an important property of these initializations. We construct a deep convolutional network with identical features by initializing almost all the weights to $0$. The architecture also enables perfect signal propagation and stable gradients, and achieves high accuracy on standard benchmarks. This indicates that random, diverse initializations are \textit{not} necessary for training neural networks. An essential element in training this network is a mechanism of symmetry breaking; we study this phenomenon and find that standard GPU operations, which are non-deterministic, can serve as a sufficient source of symmetry breaking to enable training.
Dscorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing

Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length.Evaluation of the accuracy of predicted DRSs plays a key role in developing semantic parsers and improving their performance.DRSs are typically visualized as nested boxes, in a way that is not straightforward to process automatically.COUNTER, an evaluation algorithm for DRSs, transforms them to clauses and measures clause overlap by searching for variable mappings between two DRSs.Unfortunately, COUNTER is computationally costly (with respect to memory and CPU time) and does not scale with longer texts.We introduce DSCORER, an efficient new metric which converts box-style DRSs to graphs and then measures the overlap of n-grams in the graphs.Experiments show that DSCORER computes accuracy scores that correlate with scores from COUNTER at a fraction of the time.
Stabilizing Adversarial Invariance Induction from Divergence Minimization Perspective

Adversarial invariance induction (AII) is a generic and powerful framework for enforcing an invariance to nuisance attributes into neural network representations. However, its optimization is often unstable and little is known about its practical behavior. This paper presents an analysis of the reasons for the optimization difficulties and provides a better optimization procedure by rethinking AII from a divergence minimization perspective. Interestingly, this perspective indicates a cause of the optimization difficulties: it does not ensure proper divergence minimization, which is a requirement of the invariant representations. We then propose a simple variant of AII, called invariance induction by discriminator matching, which takes into account the divergence minimization interpretation of the invariant representations. Our method consistently achieves near-optimal invariance in toy datasets with various configurations in which the original AII is catastrophically unstable. Extentive experiments on four real-world datasets also support the superior performance of the proposed method, leading to improved user anonymization and domain generalization.