Reviewerly/exHarmony huggingface dataset:
Analisis Produktivitas Pada Industri Tahu Dengan Konsep Green Productivity (Studi Kasus: UKM Tahu Sumber Rejeki)

Sumber Rejeki SME is is one of the small and medium enterprises in making tofu located in Tegalan village, Boyolali Regency. Along with the increase in production, there have been many difficulties surrounding the environment. The problem that is currently occurring by Sumber Rejeki SME is the waste produced during the production process, in addition to the measurement and evaluation of productivity. This study aims to reduce the concentration of wastewater in tofu production and increase the level of productivity in this SME. The research process begins with interviews with SME owners, this interview includes the process of making information, company input and output data, calculating productivity, waste sampling and laboratory tests.The results of this study use the concept of green productivity, the strategy used to increase productivity by paying attention to the environment. Current productivity before input reduction is also added to the output produced in December 2018 and January 2019 which is 101.53% and 105.41%, then after the input reduction has also been made the addition of productivity output has increased to 102.84% and 107.25% . From the results of the productivity that has been done, it can be seen that soda is recommended to be replaced by using filtration and phytoremediation in tackling pollution from liquid waste. Based on laboratory tests using experimental filtration and phytoremediation, Biological Oxygen Demand (BOD) was obtained 27.3 mg / L, Chemical Oxygen Demand (COD) 42.8 mg / L, Total Suspended Solid (TSS) 23 mg / L, and pH 7.7. Previously BOD was 624 mg / L, COD was 1046.2 mg / L, TSS was 294 mg / L and pH was 5.6.
Research and Application of Single Production Data Synchronization Method for Cigarette Maker

Aiming at the shortcomings of sampling in the current cigarette industry product design and development, production process quality control and overall quality evaluation, a method for synchronizing the cigarette unit cigarette data in real time is designed. This method can be used in real time. The data of each cigarette is synchronized in real time based on the time stamp and the cigarette sequence. The results show that the method can cover 100% of the mass sensor data of the current cigarette maker, and the storage speed is exactly the same as the production speed of the cigarette maker, and the storage sequence precision is 100%, and the deviation rate is less than 0.5%.
Conditionally Learn to Pay Attention for Sequential Visual Task

Sequential visual task usually requires to pay attention to its current interested object conditional on its previous observations. Different from popular soft attention mechanism, we propose a new attention framework by introducing a novel conditional global feature which represents the weak feature descriptor of the current focused object. Specifically, for a standard CNN (Convolutional Neural Network) pipeline, the convolutional layers with different receptive fields are used to produce the attention maps by measuring how the convolutional features align to the conditional global feature. The conditional global feature can be generated by different recurrent structure according to different visual tasks, such as a simple recurrent neural network for multiple objects recognition, or a moderate complex language model for image captioning and weakly supervised multiple objects segmentation. Experiments show that our proposed conditional attention model achieves the best performance on the SVHN (Street View House Numbers) dataset with / without extra bounding box; for image caption, our attention model generates better scores than the popular soft attention model; and for weakly supervised multiple objects segmentation, by simply describing a sentence for each image, our attention model can segment the salient regions corresponding to the meaningful noun words.
Maggy: open-source asynchronous distributed hyperparameter optimization based on Apache Spark

For the past two years, Hopsworks, an open-source machine learning platform, has used Apache Spark to distribute hyperparameter optimization tasks in machine learning. Hopsworks provides some basic optimizers (grid-search, random-search, differential evolution) to propose combinations of hyperparameters (trials) that are run synchronously in parallel. However, many such trials perform poorly, and waste a lot of hardware accelerator cycles on trials that could be stopped early, freeing up resources for other trials. In this thesis, the work on Maggy is presented, an open-source asynchronous and fault-tolerant hyperparameter optimization framework built on Spark. Maggy transparently schedules and manages hyperparameter trials, enabling state-of-the-art asynchronous optimization algorithms, thereby increasing resource utilization and increasing the number of trials that can be performed in a given period of time up to 30% on a fixed amount of resources. Early stopping is found to perform best when the model is sensitive, in terms of generalization performance, to the hyperparameter configurations.
Modality Consistent Generative Adversarial Network for Cross-Modal Retrieval

Cross-modal retrieval, which aims to perform the retrieval task across different modalities of data, is a hot topic. Since different modalities of data have inconsistent distributions, how to reduce the gap of different modalities is the core of cross-modal retrieval issue. Recently, Generative Adversarial Networks has been used in cross-modal retrieval due to its strong ability to model data distribution. We propose a novel approach named Modality Consistent Generative Adversarial Network for cross-modal retrieval (MCGAN). The network integrates a generator to generate synthetic image features from text features, a discriminator to classify the modality of features, and followed by a modality consistent embedding network that projects the generated image features and real image features into a common space for learning the discriminative representations. Experiments on two datasets prove the performance of MCGAN on cross-modal retrieval, compared with state-of-the-art related works.
Respon guru terhadap pelatihan pembuatan multimedia TAVAGIS berbasis prezi dalam pembelajaran kurikulum 2013

This activity aims to determine the teacher's response to the Prezi-based multimedia TAVAGIS training in 2013 elementary school curriculum learning in Kuningan District. This activity was carried out to overcome the teacher's problem regarding the lack of mastery of IT in 2013 curriculum learning. The method of implementing this activity consisted of several stages, namely: 1) planning which included partner surveys, training licenses, preparation of training modules, preparation of training and coordination, and program socialization, 2) the implementation of PKM activities which include training on making TAVAGIS multimedia based on Prezi as well as assisting the use of multimedia that has been made; 3) evaluation and follow-up. Based on the results and discussion, it can be concluded that the planning activities up to the implementation of the training went well, although after being evaluated there were several obstacles. This obstacle was successfully overcome by the implementation team. The teacher's response to the Prezi-based multimedia making of TAVAGIS 2013 primary school curriculum in Kuningan District increased from 55.91% in the good enough category to increase by 36.29%, to 92.20% in the very good category, so it can be said that the PKM program has achieved very well. This also impacts on improving partners' skills in creating multimedia curriculum learning 2013.
GeaBase: a high-performance distributed graph database for industry-scale applications

Graph analytics have been gaining traction rapidly in the past few years. It has a wide array of application areas in the industry, ranging from e-commerce, social networks and recommendation systems to fraud detection and virtually any problem that requires insights into data connections, not just data itself. In this paper, we present GeaBase, a new distributed graph database that provides the capability to store and analyse graph-structured data in real-time at massive scale. We describe the details of the system and the implementation, including novel update architecture, called update centre (UC), and a new language that is suitable for both graph traversal and analytics. We also compare the performance of GeaBase to a widely used open-source graph database Titan. Experiments show that GeaBase is up to 182x faster than Titan in our testing scenarios. We also achieve 22x higher throughput on social network workloads in the comparison.
Comparison of sequence classification techniques with BERT for named entity recognition

This thesis takes its starting point from the recent advances in Natural Language Processing being developed upon the Transformer model. One of the significant developments recently was the release ...
MITIGATION HANDLING OF SQL INJECTION ATTACKS ON WEBSITES USING OWASP FRAMEWORK

The development of the security system on the application of a website is now more advanced. But a software that has vulnerability will threaten all fields such as information system of health, defense, finance, and education. Information technology security issues will become the threat that made managers of the website (webadmin) alerted. This paper is focused on how to handle various application web attacks, especially attacks that uses SQL Injection, using The Open Web Application Security Project (OWASP), the aim is raise awareness about application security web and how to handle an occurred attack.
Dimentionality reduction based on binary cooperative particle swarm optimization

Even though there are numerous classifiers algorithms that are more complex, k-Nearest Neighbour (k-NN) is regarded as one amongst the most successful approaches to solve real-world issues. The classification process’s effectiveness relies on the training set’s data. However, when k-NN classifier is applied to a real world, various issues could arise; for instance, they are considered to be computationally expensive as the complete training set needs to be stored in the computer for classification of the unseen data. Also, intolerance of k-NN classifier towards irrelevant features can be seen. Conversely, imbalance in the training data could occur wherein considerably larger numbers of data could be seen with some classes versus other classes. Thus, selected training data are employed to improve the effectiveness of k-NN classifier when dealing with large datasets. In this research work, a substitute method is present to enhance data selection by simultaneously clubbing the feature selection as well as instances selection pertaining to k-NN classifier by employing Cooperative Binary Particle Swarm Optimisation (CBPSO). This method can also address the constraint of employing the k-nearest neighbour classifier, particularly when handling high dimensional and imbalance data. A comparison study was performed to demonstrate the performance of our approach by employing 20 real world datasets taken from the UCI Machine Learning Repository. The corresponding table of the classification rate demonstrates the algorithm’s performance. The experimental outcomes exhibit the efficacy of our proposed approach.
Going from Parameter Estimation to Density Estimation

A common approach to model variability in integrated circuits is to select a normal distribution for input variables and express variability as a non-linear function of the input variables. Even for simple non-linear functional forms as quadratic polynomials this causes the variability to no longer be normally distributed. It is thus important to be able to estimate the probability distribution of the output. In this chapter we give a brief introduction to the statistical theory of density estimation, which allows to estimate the density of a probability distribution without assuming a specific form of this density. In order to give a self-contained story accessible for non-statisticians, we first present the basic definitions and results of parameter estimation. Then we go beyond parametric estimation by discussing kernel density estimators in detail. We will indicate links with common notions from mathematical analysis such as convolution, Fourier analysis and approximation theory.
METODE RESITASI (PENUGASAN) DALAM MENINGKATKAN MOTIVASI BELAJAR BIMBINGAN DAN KONSELING DI MAN 2 BANYUWANGI

Problems occurred in the process of teaching and learning classes include students ' lack of interest in material with guidance and counselling, is evident when students are given assignments, students are always asking for the value of the results of that task. Students are also less interested in guidance and counselling to the materials due to subjects guidance and counselling is not included in the assessment of students ' report cards like other subjects. Students also concede rarely read books module that had belonged to him. Also teaching methods that have been applied to teachers BK less attractive and varied, this can be shown when the teacher would do with multimedia learning methods many tools that are not available by the school and can be known that with apply method resitasi can improve the learning motivation of students in this class, because by giving many variations in giving the task of making students do not get tired, especially when the implementation of the tasks carried out were not monotonous in the classes only. From the suggestions and criticisms from students to teachers can praktikan known that the students love it when given a task that many model variations and interesting.
Introduction to the Thematic Issue on Language &amp; Knowledge Engineering

This thematic issue of Computación y Sistemas (CyS) contains a selection of papers presenting advances into the field of Language & Knowledge Engineering and their applications. with topics covering, among others, machine learning methods for classification of unstructured data, information retrieval, ontologies, semisupervised methods, image analysis, sentiment analysis, text mining, language engineering, named entity recognition, knowledge engineering, video analysis, graph based analysis for text analytics, text clustering, opinion mining, recommendation systems and computational linguistics.
A Study on the Impact of the Event Protocol Education and Training Program on Job Performance

This study examines what factors generated from an etiquette-related training program make a significant impact on perceived effectiveness, self-efficacy, and learning transition, which in turn influence business achievement. A self-administered survey was employed using 304 recipients who had completed the training course. The results from structural equation modeling analysis suggested that both instructors’ ability and instructional methodology did not significantly impact perceived effectiveness, self-efficacy, and learning transition, but the training contents did. In summary, it is suggested that the training contents include case studies, hands-on experiences, practicum-oriented activities, and the pursuit of the course objectives. Eventually, the training contents would increase self-efficacy and learning transition, which would improve business achievement.
Your Next Program: Alive and Kicking!

Now that you have a firm grasp of GDB and know what an assembly program looks like, let’s add some complexity. In this chapter, we will show how to obtain the length of a string variable. We will show how to print integer and floating-point values using printf. And we will expand your knowledge of GDB commands.
An optimized method towards formal verification of mixed signals using differential fed neural network over FFNN

&lt;span lang="EN-US"&gt;Today, the semiconductor industries are rapidly usinganalog and mixed signals to achieve cost-effective solutions on a System on Chip (SoC) design. The SoC device is a part of analog, digital and essential mixed-signal models/circuits merged on a semiconductor device, which provides the platform to build modern retail/consumer electronics appliances with smart technology. In order to evaluate the mixed signals, the conventional approaches are not effective with respect to its performance, time and manufacturing cost. Thus, the recent researches were much interested in formal verification technique as it provides the evidence of conscious algorithms in a system. The demand for formal verification in the SoC designs in the context of software and hardware platform is high because of its cost and accuracy. Thus, the paper introduces atechnique of formal verification for mixed signals by using training models of the Differential fed neural network (DFNN) over feedforward neural network (FFNN). The formal verification is performed through equivalence checking by using recently adopted designs as reference designs. The outcomes of the verification techniques suggests that DFNN based technique improves the training accuracy and optimizes the hardware resources like area, power than the FFNN based technique.&lt;/span&gt;
A Feature Selection Based on Network Structure for Credit Card Default Prediction

The problem of credit card default prediction is important in finance and electronic commerce, thus it has been attracting more and more attention. Generally, the existing research work on credit card default prediction directly applies a classification model to the historical data and train a predictor, but rarely deeply explores the data. In this paper, we research the problem of credit card default prediction in an unconventional way. First, we study the records of consumption by credit card from the perspective of network to uncover the relationships between features and the ones between features and label. Second, based on the network structure we propose a new feature selection algorithm named as NSFSA. Finally, we apply the NSFSA to five machine learning models to train predictors over the real dataset of consumption records by credit card, and also compare with four existing feature selection algorithms. Experimental results show that the proposed NSFSA performs excellently, which demonstrates the potentials of our way to research the credit card default problem.
An Efficient Method to Detect Outliers in High Dimensional Data

In this era, detection of outliers or anomalies from high dimensional data is really a great challenge. Normal data is distinguished from data containing anomalies using Outlier detection techniques which classifies new data as normal or abnormal. Different Outlier Detection algorithms are proposed by many researchers for high dimensional data and each algorithm has its own benefits and limitations. In the literature the researchers proposed different algorithms. For this work few algorithms such as Dice-Coefficient Index (DCI), Mapreduce Function and Linear Discriminant Analysis Algorithm (LDA) are considered. Mapreduce function is used to overcome the problem of large datasets. LDA is basically used in the reduction of the data dimensionality. In the present work a novel Hybrid Outlier Detection Algorithm (HbODA) is proposed for efficiently detection of outliers in high dimensional data. The important parameters efficiency, accuracy, computation cost, precision, recall etc. are focused for analyzing the performance of the novel hybrid algorithm. Experimental results on real large sets show that the proposed algorithm is better in detecting outliers than other traditional methods.
ELECTROCHEMICAL BEHAVIOR OF N - VINYL PYRIDINE ON A PLATINUM DISK MICROANODE

15Oct 2019 ELECTROCHEMICAL BEHAVIOR OF N - VINYL PYRIDINE ON A PLATINUM DISK MICROANODE Z.G. Haydarova , J.Z. Yaxshiyeva , X.B. Rahmatov , Sh.Sh. Umarov , O.SH. Abdurahmonov and E.T. Turaqulov The Institute of Engineering Economics of Karshi The institute of pedagogics state of Jizzakh The branch of Termiz of Tashkent Technical University the name after of Islam Karimov
COMPENSATION OF NON-SYNCHRONICITY OF RECORDS FOR DOWNHOLE MICROSEISMIC MONITORING SYSTEMS IN CASE OF VTI MEDIA ANIZOTROPY

Downhole microseismic monitoring sometimes includes the installation of several downhole surveillance systems. One of the key hardware requirements is accurate synchronization of the records received. Usually this problem is solved at the hardware level, and in the case of ideal observations, additional correction procedures are not required. In some cases, hardware synchronization is lost, and for the correct use of the observed data, it is necessary to compensate for the shift in synchronization; this compensation is also necessary when synchronization information is lost during data storage and transmission. In this paper, we propose to introduce into inversion an additional parameter responsible for asynchronous recordings and to show that in the case of a simple medium model, this asynchrony can be determined directly from the data. Here we also investigate the dependence of the accuracy of determining synchronization shifts depending on the complexity of the media and the geometry of the observation system.
A Dustbin Category Based Feedback Incremental Learning Strategy for Hierarchical Image Classification

Hierarchical classification (HC) is an effective method to solve the problem of multi-class classification especially when the categories are organized hierarchically. However, HC models perform worse than flat classification (FC) models due to blocking, i.e. errors occur at the higher level will be propagated to the lower level. The first step in solving the blocking problem is to capture the blocked samples. In this paper, we present a novel HC model to capture the blocked samples by adding a virtual dustbin category for each middle-layer classifier, referred to as DBHC model. Furthermore, in order to improve the classification accuracy and accelerate the convergence process, we propose a feedback incremental learning (FIL) strategy to take into account the weights of samples, which can adjust the composition of training samples according to the test results of the previous training steps. Experiments on fashion image classification shows the superiority of the proposed model compared with several prior methods.
Emissivity Image Simulation for a High Resolution Thermal Infrared Satellite Concept

High resolution thermal infrared satellites can provide important observations for applications in various fields. For mine detection application, high resolution data can better resolve mineralogic boundaries. In this paper, an emissivity image simulation method is proposed for a high resolution thermal infrared satellite concept; and the simulated images can be used for mineral recognition and classification algorithm development using such instrument specification. Using Worldview-3 imagery as the simulation data source, the emissivity image has been generated based on a linear mixing model. In addition, accuracy analysis is conducted through comparison between simulated WorldView-3 images and the actual one. Small relative errors in every WorldView-3 band show it is feasible to use our proposed method for image simulation.