Reviewerly/exHarmony huggingface dataset:
Implementasi Keras Library dan Convolutional Neural Network Pada Konversi Formulir Pendaftaran Siswa

Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless.
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD

We apply a reinforcement learning (RL) based approach to learning optimal synchronization policies used for Parameter Server-based distributed training of machine learning models with Stochastic Gradient Descent (SGD). Utilizing a formal synchronization policy description in the PS-setting, we are able to derive a suitable and compact description of states and actions, allowing us to efficiently use the standard off-the-shelf deep Q-learning algorithm. As a result, we are able to learn synchronization policies which generalize to different cluster environments, different training datasets and small model variations and (most importantly) lead to considerable decreases in training time when compared to standard policies such as bulk synchronous parallel (BSP), asynchronous parallel (ASP), or stale synchronous parallel (SSP). To support our claims we present extensive numerical results obtained from experiments performed in simulated cluster environments. In our experiments training time is reduced by 44 on average and learned policies generalize to multiple unseen circumstances.
More Than Meets The Eye: Toward an Ontology of Proximity

Words cannot describe photographs in the same sense that key words or subject headings can describe verbal documents because words are not native elements of photographs. Words can describe anecdata – reactions and associations that might be functional. Some form of data is coded in some medium, transmitted, received, and decoded. Some forms of coding and circumstances of message making and decoding require little proximity of the recipient to the message maker, while some forms utterly depend on proximity. We explore 10 photographs and interactive data accumulated through interactive exhibition to explore proximity and functional meaning. These examples demonstrate three levels of generality: any image with a particular object or characteristic will do; an image with certain qualities and intriguing connections is wanted; the photograph must show particular qualities or have a backstory that is explicit as to why this is the most useful image. We suggest that a first order taxonomy of proximity comes into play. We expand the idea of a taxonomy of proximity into the more inclusive, environmental notion of an ontology of proximities – referring to types of connections, types of uses, and circumstances of discovering the threads.
Why algorithms are dangerous : what the role of aI should be in cybersecurity

Automation is a popular and very important topic, but with our brains still outperforming Artificial Intelligence (AI) techniques, humans are indispensable in security. Especially with respect to pattern recognition and contextual reasoning, human are superior in keeping false positive rates of automated techniques to a minimum. We therefore still have a job to do and cannot go to the beach.
Does the MS Spell Checker Effectively Correct Non-Native English Writers’ Errors? A Case Study of Saudi University Students

Those learning English as a second or foreign language use spell checkers to correct the mistakes and errors they may have made while typing texts on a computer. However, scholars have debated the effectiveness of such checkers, which were originally designed to fix the spelling mistakes of native speakers. An example of these checkers is the Microsoft (MS) Word program, which constitutes the focus of the current study. This study examined how MS Word treats misspellings made by Saudi learners of English as a foreign language. It specifically addressed three research questions: (1) which L2 spelling errors were successfully fixed by MS Word; (2) which L2 spelling errors were unsuccessfully fixed by MS Word; and (3) how did intermediate L2 learners respond to alternative corrections provided by MS Word. A screen-tracking software, Screencast-O-Matic, was used to monitor the MS Word spell checker’s treatment of misspelled words. It was also used to track learners’ reactions to alternative corrections provided by MS Word in real time. The study analysed 401 errors made by25 female intermediate-level English learners at a Saudi university.
A new collaborative approach to solve the gray-sheep users problem in recommender systems

Recommender systems aim to help users to find items that fit their requirements and preferences. In that field, the collaborative filtering (CF) approach is considered as a widely used one. There are two main approaches for CF: memory-based and model-based. Both of the two approaches are based on the use of users' ratings to predict the top-N recommendation for the active user. Despite its simplicity and efficiency, The CF approach stills suffer from many drawbacks including sparsity, gray sheep and scalability. The aim of this work is to deal with the gray sheep problem, by proposing a novel collaborative filtering approach. This novel approach aims to enhance the accuracy of prediction by turning the users whose preferences disagree with the target user, into new similar neighbors. For instance, if a user X is dissimilar to a user Y then the user ¬ X is similar to the user Y. To evaluate the performance of the proposed approach, we have used two datasets including MovieLens and FilmTrust. The Experimental results show that our approach outperforms many traditional recommendation techniques.
Efficient Block Pruning Based on Kernel and Feature Stablization

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.
A comparison of cohesion features between Korean and NS college students using TAACO program

The purpose of this study is to examine how cohesion features can be used to distinguish between essays written by Korean and NS college students. A total of 800 essays from the ICNALE (International Corpus Network of Asian Learners of English) corpus were analyzed using the newly-developed automated analysis of cohesion program, TAACO (the Tool for the Automatic Analysis of Cohesion). In order to examine whether there are statistically significant differences between NS and Korean students, a multivariate analysis of variance (MANOVA) was conducted. The multivariate test result showed that the combined cohesion indices were significantly affected by L1, indicating that Korean and NS students differed in terms of various cohesion indices. A stepwise discriminant function analysis was also carried out to characterize the nature of cohesion differences in essays between NS and Korean students. The best two predictors for distinguishing between Korean students’ essays and NS students’ essays are the overlap of function words and the use of demonstratives. Overall, NS students produced a more cohesive writing than Korean students in that they employed overlapping words, demonstratives, and lexical subordinators as a way to connect ideas across sentences. Implications of this study for English writing pedagogy are discussed.
Self-adaptive Ant Colony Algorithm Based on Statistical Analysis and Its Application

Path planning is important in robot field and in this field, many researchers have done a lot of work. This paper proposes an improved ant colony algorithm as traditional ones have a shortage of slowly convergence and easily falling into local optimum. On the basis of traditional ones, the dynamic random statistical analysis and extraction of each generation of Ant Colony are performed the optimal, average and worst ant information constitutes an adaptive operator for adaptive updating of local pheromones. Simulation results demonstrate that it is effective in equilibrium increasing convergence rate and getting into the contradiction of local optimal solution.
Integrating with Advance Services

This chapter describes the integration of Watson Assistant with built-in advanced IBM services, such as the Tone Analyzer service and the Watson Discovery service. We also cover integration with other messaging platforms, such as Facebook Messenger and Slack.
A Survey of Data Anonymization Techniques for Privacy-Preserving Mining in Bigdata

Bigdata era is seeing the data burst occurring in a multitude of angles that are better expressed in terms of the 4Vs (Volume, Velocity, Velocity, Veracity).While trying to infer information from data, care should be exercised as not to reveal the identity of the data owner, which breaches the privacy rights.Leakage of information can happen right from the data collection point, at the data storage area, followed by the distribution of data to data users/miners and finally with published results.A cross-matching of all these points with the 4Vs (growing still) of big data, puts a huge challenge on how to extract the maximum possible information, without compromising on the privacy of the data owner.Anonymization of the original data should be done at one or more of the above-mentioned stages before the data are given for the mining process.This work makes a survey of the various anonymization techniques followed to transform the data in such a way that the privacy of the data owner is not compromised.Also, the sample data drawn should resemble and represent the original dataset in the maximum possible number of dimensions.The results of the various methodologies have been analyzed and the observations have been presented.
Design and Implementation of Professional Audio Practice Teaching System Based on AoIP Technology

In the course of professional audio teaching, how to efficiently carry out live audio production training is a problem that puzzles every audio professional teacher. Audio practice teaching system based on AoIP technology can solve the above problem. The system is composed of playback workstation, recording workstation, operation, display, teaching terminal and other parts. On the basis of solving the problems of multicast stream, clock synchronization, material library, etc., it is possible to carry out multi-system cascade teaching activities with audio practice teaching system and AoIP technology as the core, so that students of related majors can make training of live audio production.
Игры со временем

Information and communication technologies significantly change the speed of social interaction and thereby make it possible to consider the phenomenon of subjective time from a new perspective. In the study, we analyzed the problem of time in computer games, its relationship with physical time, as well as the perception of time by a person and the possibility of managing it in a socio-philosophical aspect. The purpose of this work is to consider the refraction of physical and philosophical concepts in modern computer games related to time, and also to identify the mechanisms of time handling in virtual worlds. The main findings of the study include the ability of game developers, identified by the authors, to build alternative concepts of time, not limited by the laws of the physical world. Preserving the main property — the unidirectional arrow of causality (the ability of the past to influence the future), game developers are free to choose how to transform the present / future depending on the change in the past and the presence or absence of a time loop. Hundreds of games devoted to experiments with time that have appeared in recent decades may indicate comprehension in society of various approaches to the ontological nature of this dimension.
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine.

Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning based model and attention mechanism in various tasks in computer vision (CV) and natural language processing (NLP). How to combine the attention mechanism with deep CTR model is a promising direction because it may ensemble the advantages of both sides. Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features. In this paper, we propose a new neural CTR model named Field Attentive Deep Field-aware Factorization Machine (FAT-DeepFFM) by combining the Deep Field-aware Factorization Machine (DeepFFM) with Compose-Excitation network (CENet) field attention mechanism which is proposed by us as an enhanced version of Squeeze-Excitation network (SENet) to highlight the feature importance. We conduct extensive experiments on two real-world datasets and the experiment results show that FAT-DeepFFM achieves the best performance and obtains different improvements over the state-of-the-art methods. We also compare two kinds of attention mechanisms (attention before explicit feature interaction vs. attention after explicit feature interaction) and demonstrate that the former one outperforms the latter one significantly.
$e$-Fuzzy filters of MS-algebras

In this article, we present the notion of  $ e$-fuzzy filters in an MS-Algebra and characterize in terms of equivalent conditions. The concept of $ D $-fuzzy filters is studied and the set of equivalent conditions under which every $ e $-fuzzy filter is an $ D $-fuzzy filter are observed. Moreover we study some properties of the space of all prime $ e $-fuzzy filters of an MS-algebra.
Metaphor and Simile in English Context: Do They Know the Differences?

The present research tries to explore and find out the students understanding on selected metaphor and simile in English context among University students. This descriptive quantitative research involves 38 english depratment students as it samples selected from two different classes in total. This research was conducted on May, 2019. The instruments used to collect the data were test which consisted of 40 items. The result from the test revealed the understanding on these two topics are fair, metaphor (62,63) and simile (60,92). This data seems showing a clear picture that the students need to improve their knowledge on these topics.
Predictive Model of Passengers Trans Metro Bandung Encouraging Smart Transportation

Smart city can be accomplished by merging all components to be smart system. One of significant components is transportation. Everybody moves, everybody need to reach other place safely, efficiency, and comfortably. In order to get smart, we have to analyze the best way to improve capability. One of the best ways is to make predictive model that one may conceive strategies attracting passengers. Using Bayesian dan SVM as comparison will produce best fit model for Trans Metro Bandung passenger data. Better strategy is an emergence needed to produce Trans Metro Bandung better performance because the model predicted there will be no significant growth for Trans Metro Bandung passenger
Temporal 3D Fully Connected Network for Water-Hazard Detection

Water on road is a potential hazard for road vehicles. In complex traffic, it is difficult for drivers to safely share the road with other vehicles and pedestrians while avoiding water puddles and pot holes. Running into these could potentially damage electronic components and corrode metal parts of the car, or even lead to loss of control. Similarly, driver-less cars also need to detect water puddles and plan safe path around them or slow down if it deems to be safe to do so. Such detection needs to be both accurate and fast enough for realtime assistance or planning. We present a new temporal 3D fully convolutional network called T3D-FCN for water detection that exploit temporal information to achieve accuracy comparable to the-state-of-the-art accuracy while requires less computation resources. We also show that by adding temporal information the detection performance significantly improves with a small additional computation as compared to single image detection using a similar network architecture.
The Summary Evaluation Task in the MultiLing - RANLP 2019 Workshop

This report covers the summarization evaluation task, proposed to the summarization community via the MultiLing 2019 Workshop of the RANLP 2019 conference.The task aims to encourage the development of automatic summarization evaluation methods closely aligned with manual, human-authored summary grades and judgements.A multilingual setting is adopted, building upon a corpus of Wikinews articles across 6 languages (English, Arabic, Romanian, Greek, Spanish and Czech).The evaluation utilizes human (golden) and machine-generated (peer) summaries, which have been assigned human evaluation scores from previous MultiLing tasks.Using these resources, the original corpus is augmented with synthetic data, combining summary texts under three different strategies (reorder, merge and replace), each engineered to introduce noise in the summary in a controlled and quantifiable way.We estimate that the utilization of such data can extract and highlight useful attributes of summary quality estimation, aiding the creation of data-driven automatic methods with an increased correlation to human summary evaluations across domains and languages.This paper provides a brief description of the summary evaluation task, the data generation protocol and the resources made available by the MultiLing community, towards improving automatic summarization evaluation.
Classification Model Supervised Using the Heaviside Function

In this paper, the theoretical foundations of a new classification model which is based on the associative approach of Pattern Recognition: Heaviside’s Classifier. As its name suggests its both phases, learning and classification, are based on the Heaviside’s function. The effectivity of the proposed model can be verified by the results of a comparative study where the classifier was tested against other seven pattern recognition models on 20 datasets. Experimental results indicate that the model is competitive in the state of the art. It is noteworthy that with one dataset, our classifier achieved the 100% of performance, validated with 10-fold cross-validation, while in its worst performance it achieved a little above of 50%. The obtained results were validated by the Wilcoxon non parametric test, which provides statistical certainty to the results of the performance comparison between models.
Development of a Unified Digital Information and Communication System for Admission and Enrollment to Higher Educational Institutions

The procedure for a new enrollment in higher educational institutions is quite complicated, having several stages process of interaction of the university with applicants as well as their parents. All the actors in this process pursue different goals, guided by their own interests. Thus, universities try to attract the most talented applicants who are interested in studying in the chosen field of science or specialty as well as provide a good competition. Applicants and their parents are interested in being enrolled in state-funded place of university's major or field of training of their interest. The work of the current system of admission to universities in the Russian Federation is structured in such a way that there is a kind of competition not only between applicants but also between educational organizations themselves. In modern conditions, admission commissions at universities strive to create the most open and convenient information system for applicants' admission, the data in which are updated in online mode. The possibility of having an open and reliable informational communication channel would allow applicants to quickly receive up-to-date information on the competition, which would help them to reduce uncertainty. This article proposes approaches to the development of a unified digital information and communication system for admission and enrollment to higher educational institutions, describes the principles for creating such a system, suggests the structure and functioning algorithms of a unified information and communication system that combines digital resources of the admission commissions of all universities of the Russian Federation. The proposed digital information and communication system will provide applicants with the possibility to monitor and change priorities among all their chosen specialties or areas of training at any university accredited in the Russian Federation up to the moment of their enrollment. A unified digital information and communication environment will optimize the current system of admission to universities, make it more transparent and convenient for both applicants and their parents and for universities.