Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI METODE BAYES DALAM EVALUASI KEPUASAN MAHASISWA TERHADAP LAYANAN UNIVERSITAS

Student satisfaction towards services is a priority for each tertiary institution to improve the quality that is demanded by students. To improve the quality of education, universities must provide services provided to students. Dehasen University (Unified) Bengkulu promised to improve the quality standards of education and continue to improve and improve dissatisfaction in meeting student expectations. This is done to improve the quality standards of education and meet student expectations related to services that are the rights of students. By understanding the level of satisfaction of students, universities can improve performance in carrying out their duties and take responsibility for the quality of education, thus increasing the quality of services that can be improved. This study uses Bayes study, which is a classification method using probability and statistical methods based on the Bayes theorem. The aim is to implement Bayes to support student satisfaction with universities that support the following fields: Student Affairs, Library, Academic and Student Administration Bureau (BAAK), Facilities and infrastructure and finance. The results of this method produce Administrative and Academic Bureau services and financial services obtain Very Good satisfaction levels with a total service value of 12,183 with an excellent threshold value of 12,042 and a total service value of 9,413 with an excellent threshold value of 9,375. And the level of student satisfaction with the level of service is good, because the total value of results is 49,769 while the threshold is very good is 50,001 this means the total results below the threshold value is very good.
A Fast detection network for skin color model

Skin color target detection is a typical single-type target detection. However, the existing multi-targets detection networks take a long training time and have complex structure for skin color target detection. The detection speeds of some network can be further improved. This paper designs a simple structure for rapid skin color model detection by combining RPN network and knowledge of skin color ellipse model. According to the pretreatment of VGG-16, the test is executed in Kitchen-l in behavior data set Watch-n-patch and Asian face data set CASIA-FaceV5. The frame rate reaches 10.2 and 11.3, and the average precision (AP) reaches to 77.8% and 91.2%, respectively. Further, the desired results are obtained under the video and camera data.
Revisiting ImprovedGAN with Metric Learning for Semi-Supervised Learning

Semi-supervised Learning (SSL) is a classical problem where a model needs to solve classification as it is trained on a partially labeled train data. After the introduction of generative adversarial network (GAN) and its success, the model has been modified to be applicable to SSL. ImprovedGAN as a representative model for GAN-based SSL, it showed promising performance on the SSL problem. However, the inner mechanism of this model has been only partially revealed. In this work, we revisit ImprovedGAN with a fresh look on it based on metric learning. In particular, we interpret ImprovedGAN by general pair weighting, a recent framework in metric learning. Based on this interpretation, we derive two theoretical properties of ImprovedGAN: (i) its discriminator learns to make confident predictions over real samples, (ii) the adversarial interaction in ImprovedGAN constrains the discriminator to decrease the angles between the features of real samples and class weight vectors. The two properties suggest that the adversarial interaction induces the class-wise cluster separation of the features as experimentally verified. Motivated by the findings, we propose a variant of ImprovedGAN, called Intensified ImprovedGAN, where its cluster separation characteristic is improved by two proposed techniques: (a) the unsupervised discriminator loss is scaled up and (b) the generated batch size is enlarged. As a result, I2GAN produces better class-wise cluster separation and, hence, generalization. Extensive experiments on the widely known benchmark data sets verify the effectiveness of our proposed method, showing that its performance is better than or comparable to other GAN based SSL models.
FPGA Implementation of Pulse-Coupled Phase Oscillators working as a Reservoir at the Edge of Chaos

In the field of neuroscience, reservoir computing (RC) has been viewed as a model of the neuron computational system. RC is a framework for constructing recurrent neural networks, which is used for modeling the parts of the brain to solve the temporal problem. We construct the network inside the reservoir using the Pulse-Coupled Phase Oscillator (PCPO) with neighbor topology connections on field-programmable-gate- array (FPGA). Winfree model is used for PCPO spiking-based. We investigate the stability of the edge phenomenon using the zero one test (Z1-Test) methodology. We evaluate the proposed model on the time series generation tasks. We have successfully implemented and verified using FPGA that the 3×3 and 10×10 PCPO working as a Reservoir on the network.
Parallel Asynchronous Stochastic Dual Coordinate Descent Algorithms for High Efficiency and Stable Convergence

Parallel asynchronous stochastic dual coordinate descent algorithm (PASSCoDe) is an efficient method to train linear models in multi-core shared-memory systems. PASSCoDe enjoys a good speedup when the number of threads is less than 8 on sparse datasets, i.e., the percentage of nonzero elements in the training data is relatively small. However, due to the memory conflict and delayed parameter access problem in parallel execution, it often diverges or does not converge to the best accuracy as a serial dual coordinate descent algorithm does. In this paper, we propose two algorithms – Adaptive Hybrid algorithm and Lazy-Sync algorithm, to overcome the convergence issues in parallel execution. Both algorithms use the current accuracy to guide the execution and strike a balance between accuracy and efficiency. Experimental results indicate that both algorithms converge to the same high accuracy as a sequential program does on all datasets tested except on an extremely small one. On the other hand, PASSCoDe sometimes converges to a less accurate value or does not converge at all on some datasets. Our methods also outperform PASSCoDe-Fix, an improved version of PASSCoDe, in stable convergence, execution speed, and scalability. For example, the Adaptive Hybrid algorithm runs up to 2.8 times faster than PASSCoDe-Fix, and the Lazy-Sync algorithm runs 11 times faster than PASSCoDe-Fix on dataset covtype with 10 threads.
Detecting Automatically Generated Tweets Using Lexical Analysis and Profile Credibility

Every industry relies heavily on accurate news and information distribution. In the recent decade social media has become one of the main methods of sharing news and other social impacting information online. But there's an uprising threat to all social media platforms, especially for twitter, known as bots. Not all bots are malicious but these automated accounts are largely responsible for platform manipulation, which is the process of misleading, disrupting the experience of other users by engaging in deceptive, aggressive activities. There are many politically motivated groups in Facebook and Twitter who use various levels of manipulation to influence voters and thereby undermining the democratic process. Platform manipulation is not only carried out by malicious automation, but also with spam and inauthentic accounts (fake accounts). This paper presents novel methodology to detect these bots (automated accounts) using existing research as foundation and builds new research solution to the problem. This methodology can be applied to the news domain to find bots involved in spreading false information. This methodology classifies a given tweet into either fake news or not and use the result as a feature and in addition to that user credibility can also be taken into account.
Linear Algebra for Pattern Processing: Projection, Singular Value Decomposition, and Pseudoinverse

Linear algebra is one of the most basic foundations of a wide range of scientific domains, and most textbooks of linear algebra are written by mathematicians. However, this book is specifically intended to students and researchers of pattern information processing, analyzing signals such as images and exploring computer vision and computer graphics applications. The author himself is a researcher of this domain.
Sentiment Analysis Framework for E-Commerce Reviews Using Ensemble Machine Learning Algorithms

Sentiment analysis or opinion mining as a research field is gaining importance. In this study, we are focusing on building a sentiment analyzer for Amazon product reviews in multi-domain category using ensemble machine learning algorithms. We have used five machine learning algorithms random forest, extra trees, bagging, AdaBoost, and stochastic gradient boosting for our experiments with four datasets, books, DVD, kitchenware, and electronics categories. We have compared the models to explore which model gives better performance in analyzing sentiments. The result shows that stochastic gradient boosting with 83% accuracy outperforms the other algorithms including the random forest algorithm which is generally considered as the best.
Anti-Theft ATM Robbery Detection Using Big Surveillance Video Data

Video reconnaissance framework has turned into a basic part in the security and assurance arrangement of modem urban areas, since savvy checking cameras furnished with canny video examination procedures can screen and pre-alert anomalous practices or occasions. Nonetheless, with the extension of the reconnaissance arrange, monstrous observation video information postures colossal difficulties to the examination, stockpiling and recovery in the Big Data time. This paper shows a novel insightful preparing and usage answer for enormous reconnaissance video information in light of the occasion recognition and disturbing messages from front-end shrewd cameras. The technique incorporates three sections: the astute pre-disturbing for strange occasions, keen stockpiling for observation video and fast recovery for confirm recordings, which completely explores the transient spatial affiliation investigation regarding the unusual occasions in various checking locales. Test comes about uncover that our proposed approach can dependably pre-alert security hazard occasions, considerably diminish storage room of recorded video and essentially accelerate the proof video recovery related with particular suspects.
Approximation with Polynomial and Trigonometric Splines of the Third Order and the Interval Estimation

This work is one of a series of papers that is devoted to the further investigation of polynomial and trigonometric splines of the third order approximation. Polynomial basis splines are better known and therefore more commonly used. However, the use of trigonometric basis splines often provides a smaller approximation error. Here we discuss the interval estimation with the polynomial or trigonometric basis splines which are useful for the approximation of functions with one or two variables. For each grid interval we construct the approximation separately. We construct the interval estimation of approximation also separately on the each grid interval. The one-dimensional polynomial or trigonometric basis splines of the third order approximation are constructed when the values of the function are known in each point of interpolation. Numerical examples are represented.
Variational inference and sparsity in high-dimensional deep Gaussian mixture models

Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure for mixture components. There are several recent extensions of mixture of factor analyzers to deep mixtures, where the Gaussian model for the latent factors is replaced by a mixture of factor analyzers. This construction can be iterated to obtain a model with many layers. These deep models are challenging to fit, and we consider Bayesian inference using sparsity priors to further regularize the estimation. A scalable natural gradient variational inference algorithm is developed for fitting the model, and we suggest computationally efficient approaches to the architecture choice using overfitted mixtures where unnecessary components drop out in the estimation. In a number of simulated and two real examples, we demonstrate the versatility of our approach for high-dimensional problems, and demonstrate that the use of sparsity inducing priors can be helpful for obtaining improved clustering results.
Selective Transition Collection in Experience Replay

Experience replay method is often used in off-policy reinforcement learning. As the training progresses, the distribution of the collected transitions becomes more and more concentrated, and this will lead to catastrophic forgetting and a low rate of convergence. In this paper, we present selective transition collection algorithm which is a new design to address the concentrated distribution by selectively collection the transitions. We propose a method to estimate the similarity between transitions, and a probability function to reduce the chance of transitions with high similarity to the experience memory being collected. We test our method on familiar reinforcement learning tasks and the experimental results demonstrate that selective transition collection can not only speed up the learning but also prevent catastrophic forgetting effectively.
Investigation and recovery of materials by Digital forensics

The digital rhetorical method may be a recognized scientific and rhetorical method employed in digital forensics investigations. Forensics investigator Eoghan Casey defines it as variety of steps from the initial incident alert through to reportage of findings. The method is preponderantly employed in pc and mobile rhetorical investigations and consists of 3 steps: acquisition, analysis and reportage. Prior to the Nineteen Seventies crimes involving computers were proscribed mistreatment existing laws. the primary pc crimes were recognized within the 1978 Sunshine State pc Crimes Act, including legislation against the unauthorized modification or deletion of knowledge on an automatic data processing system.
Single-scale Residual Dense Dehazing Network

Abstract Recently, image dehazing algorithm has been widely used in the preprocessing of target tracking and pattern recognition. A large number of end-to-end convolutional networks have achieved good results in dehazing by image translation. In this paper, we use Residual Dense structure, which has been prove effective in high resolution reconstruction, to build feature extract block, and stack these block to form a single-scale dehazing network. In order to further enhance the performance, we convey the feature of shallow layer to deep layer by channel concatenation. The results show that our network has achieved good results in both the synthetic haze removal and the real haze removal.
Application of Learning Approaches in Healthcare

The learning approaches in healthcare would aim at phenotyping the disease based on clinical as well as physiological characteristics as ideally disease is defined and diagnosed by a combination of clinical symptoms and physiologic abnormalities.The medicine today is advanced into new realm with the growth of applications of artificial intelligence and machine learning in healthcare. This is important as we will not be addressing the target population for a specific disease alone; rather predict the likely outcome of the related disease in an unknown population of interest with the knowledge gained. This is of utmost focus especially with rare diseases, the data for which are available in lower volumes. Further, prediction outcomes available at earlier stages are important to prepare points of care to handle disastrous outcomes resulting from the diseases.
AR Interface Use and Icon Design Demonstration Almanac Research - Take HOLA E-Commerce Website as an Example

This study takes HOLA as an example of the e-commerce website, explores the use of AR interface and icon image, the original website interface for the pretest test, and then by the optimized website for the posttest test, after the end of the test, both need to fill out use of the afterword difference SD form questionnaire, collect edited after research and comparative analysis, the results of conclusions and suggestions. The study shows that after the posttest optimization of the web page after cross comparison through the SD table, the data is significantly improved than the original website measurement, the redesigned ICON image in user interaction has more clear and more established image display, the new add shopping cart function also has a clearer way to let users place orders more quickly. Re-establishing the AR interface of the e-commerce website can give researchers a clearer idea of what the user's needs are, but also increase the use of the interface, and, relatively, let the user feel more directly the interface.
Selective Self-attention with BIO Embeddings for Distant Supervision Relation Extraction

Distant supervision method is proposed to label instances automatically, which could operate relation extraction without human annotations. However, the training data generated in this way intrinsically include massive noise. To alleviate this problem, attention mechanism is employed by most prior works that achieves significant improvements but could be still imcompetent for one-sentence bags which means only one sentence within a bag. To this end, in this paper, we propose a novel neural relation extraction method employing BIO embeddings and a selective self-attention with fusion gate mechanism to fix the aformentioned defects in previous attention methods. First, in addition to commonly adopted embedding methods in input layer, we propose to add BIO embeddings to enrich the input representation. Second, a selective self-attention mechanism is proposed to capture context dependency information and combined with PCNN via a Fusion Gate module to enhance the representation of sentences. Experiments on the NYT dataset demonstrate the effectiveness of our proposed methods and our model achieves consistent improvements for relation extraction compared to the state-of-the-art methods.
Affordable Educational Laptop With Physical Computing Using Single Board Computer

Information and Communications Technology can impact learning of student. As per the latest research we found that when educators are digitally literate and trained to use digital devices for teaching can lead to higher order thinking skills, it provide creative and personalized options for students to express their understanding, and leave students better prepared to manage ongoing technical change in the world and the workplace. For this revolution in education India needs cost effective and innovative educational devices. Technology plays important role in STEM education. We designed low cost educational laptop for schools, students and educators. This educational laptop has good connectivity, processing power and ease of use. Feature of physical computing will boost the creativity and programming skills of the young scholars.
Dual Watermarking of Color Images based on SWT and SVD using Invasive Weed Optimization

This paper proposes a robust and high embedding capacity watermarking technique where two watermarks are embedded in a color image. A gray-scale image is embedded as one of the watermarks to identify the ownership of the host image using Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD). A binary watermark with the help of the least significant bit method is embedded to locate the tampers. The embedding process is optimized to have better robustness against various attacks by employing Invasive Weed Optimization (IWO). A novel fitness function is proposed to optimize color image watermarking. This technique is applied to standard images and medical images. The performance of the proposed method is compared based on imperceptibility, robustness and embedding capacity.
Event-Based Time-Stamped Claim Logic

The Event-Based Time-Stamped Claim Logic that we define in this paper allows one to reason about distributed time-stamped claims that can change through time by the occurrence of events. Such a logic is interesting for theoretical reasons, i.e., as a logic per se, but also because it can be applied in a number of different disciplines and application domains (e.g., history, crime forensics or cyber forensics) as it allows one to reason about a huge amount of pieces of evidence collected from different sources over time, where some of the pieces of evidence may be contradictory and some sources considered to be more trustworthy than others. We formalize the language and the semantics of the Event-Based Time-Stamped Claim Logic, provide a sound and complete Hilbert calculus, and consider some concrete examples. We also show that the validity problem for the logic is decidable by providing a tableau-like decision algorithm.
A distributed security tomography framework to assess the exposure of ICT infrastructures to network threats

The scope of cyber security becomes wider and wider with time and cyber threats rapidly change. This strongly impacts the performance of companies whose success heavily depends on the health of the underlying network infrastructure. The ability to have a clear understanding of the security exposure of a network in its entirety hence becomes part of the mission of such companies. In this work we present the design and implementation of a distributed measurement solution to assess the cyber security exposure of an ICT Infrastructure. We provide means to define cyber security indicators through an automated and repeatable measurement process. We prove the efficiency of the presented methodology by testing it on real-world infrastructure facilities, discussing the results obtained in two different scenarios: a comparison of networks with different characteristics and a real-time monitoring of the defined metrics.
Auto-labeling of sensor data using social media messages

Recently, the deployment of various Internet of Things (IoT) sensors has encouraged smart cities to accumulate a large volume of data. When machine learning models utilize such accumulated raw data to predict events and situations, various systems of a smart city, such as traffic accident management systems, can be further developed by utilizing the predicted events and situations. However, although there has been a large volume of various IoT raw data on smart cities, such data do not have labels that are related to events or situations. Data with meaningful labels are required for the training of the models. Because these sensor data do not have meaningful labels, the data cannot be utilized into the models. There are several existing methods for labeling, but they have different drawbacks. In this study, we investigate the feasibility of utilizing social media messages to extract meaningful labels for machine learning to predict events and situations in smart city environments. As a case study, we compared the extracted labels from social media messages with the events and situations found in announced traffic news, and other articles. The results show that it is feasible to utilize social media messages as a source for meaningful labels of events and situations. We also propose an improved clustering algorithm using an outlier detection technique to extract meaningful labels in a more robust way. Furthermore, for other researchers who want to utilize the IoT raw data, we analyze and release the refined sensor data on which there were unknown noise.