Reviewerly/exHarmony huggingface dataset:
Semi-dynamic load balancing: efficient distributed learning in non-dedicated environments

Machine learning (ML) models are increasingly trained in clusters with non-dedicated workers possessing heterogeneous resources. In such scenarios, model training efficiency can be negatively affected by stragglers---workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy called semi-dynamic load balancing to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at iteration boundaries, being non-intrusive to intra-iteration execution. We develop LB-BSP based on such an insight, which is an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities by right-sizing the sample batches at the synchronization barriers. We have custom-designed the batch sizing algorithm respectively for CPU and GPU clusters based on their own characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to 54%.
PENETAPAN KADAR VITAMIN C PADA KULIT PISANG (Musa paradisiaca) DENGAN METODE SPEKTROFOTOMETRI UV-VISIBLE TAHUN 2018

Background: Vitamin C is a vitamin that is water soluble qnd one vitamin that are needed by the body. To meet the intake of vitamin C needed source of vitamin a derived from fruits, one of which is banana. Part of the banana , which is still rarely used is its skin. Banana peel is very beneficial for health however the situation is only considered as waste that has not been utilized well. This study aims to determine the levels of vitamin C on the skin of the banana (Musa paradisiaca) by the method of spectrofotometry uv-visible wavelength 224,5 nm. Method: This research use experimental with purely research stage comprise sample preparation, qualitative test, making the solution of the parent raw, quantitative test, determination of wavelength of maximum vitamin and creation of calibration surve. Result: Based on the research that has been done obtained the result of the qualitative test to prove the presence of high content of vitamin C contained in banana peels, with the highest level obtained in the skin of the plantain by 0,0253 mg/ml and the levels of the lowest on a banana peel the wax by 0,0172 ng/ml. Discussion: Levels of vitamin C on the skin of the banana barangan of 0, 0219 mg/ml, sample a banana skin banten by 0, 0173 mg/ml, sample a banana peel wax by 0, 0172 mg/ml and sample the skin of plantain by 0, 0253 mg/ml.
Mobile Phone and Barcode Scanner

Abstract With the advancement in microprocessor technology, mobile phones are becoming more preferable in our day to day lives as compared to personal computers since most phones are capable of functionalities as sophisticated as our PC’s or laptops. Mobile phones nowadays have processor speeds as high as 1GHz and can have a storage capacity of about 32Gb and they also have long hours of battery life. In addition to these feature, there is the aspect of great portability. When all these are combined into one, the mobile phones can be viewed as a very powerful device that rivals PC’s. Advancements of hardware also had made mobile phones much smaller and more efficient whereas including an increasing of peripherals number. The scope of this paper is basically to make a mobile phone application and how to interface the mobile phone to the scanner. The mobile application is to be developed in the Linux-based Android platform. An algorithm based on the operation principles is also to be developed during the work.
Linked Open Data: State-of-the-Art Mechanisms and Conceptual Framework

Today, one of the state-of-the-art technologies that have shown its importance towards data integration and analysis is the linked open data (LOD) systems or applications. LOD constitute of machine-readable resources or mechanisms that are useful in describing data properties. However, one of the issues with the existing systems or data models is the need for not just representing the derived information (data) in formats that can be easily understood by humans, but also creating systems that are able to process the information that they contain or support. Technically, the main mechanisms for developing the data or information processing systems are the aspects of aggregating or computing the metadata descriptions for the various process elements. This is due to the fact that there has been more than ever an increasing need for a more generalized and standard definition of data (or information) to create systems capable of providing understandable formats for the different data types and sources. To this effect, this chapter proposes a semantic-based linked open data framework (SBLODF) that integrates the different elements (entities) within information systems or models with semantics (metadata descriptions) to produce explicit and implicit information based on users’ search or queries. In essence, this work introduces a machine-readable and machine-understandable system that proves to be useful for encoding knowledge about different process domains, as well as provides the discovered information (knowledge) at a more conceptual level.
Manar: An Arabic Game-based Application Aimed for Teaching Cybersecurity using Image Processing

People use the Internet for various activities, including exchanging money, playing games, and shopping.However, this powerful network came at a cost: the features that provide the Internet with these capabilities are also what make it vulnerable.The need for cybersecurity tools and practices was recognized, especially for children, since they tend to be naive and can be easily tricked.Aimed at children from 6 to 12 years old, Manar is an Arabic smartphone game that seeks to build a generation who are well-informed about cybersecurity issues.It teaches them about notable cybersecurity topics such as social engineering and cryptography, it also has a very appealing theme to attract children to play the game.The theme being "pirates and islands", each level will be represented as an island and a moving pirate ship will navigate between the levels.The application introduces the technology of image processing in a unique way, allowing children to move around and look for objects, which makes the game as interactive as possible to attract children's attention.
Electronic transaction system for user authentication and e-payment application based on RFID smart card

In this digital era, everything various aspects in life needs to be done fast, practical, and reliable. All these demands encourage people to consider adopting technology in life that includes microtransaction. Until this day, most of the microtransactions still carried by using conventional cash transactions. Although some people already use a debit card when as the times progressed, people began to switch to cashless transactions. Another similar system that has been implemented has each different design, but always prioritizes the principle of robustness, scalability, and flexibility. This paper proposes a cashless transaction system for the small-scale organization such as school, college, office, and other organization by designing an RFID terminal device and database server. This system aimed to be more user-friendly and straightforward. The designed system consists of an RFID card as e-money, terminal transaction device, and database server. RFID serves as a unique ID owned by the user to identify themselves to authorize their transactions. The user will be able to make a payment on the terminal device installed in the vendor shop by using the smart card. The device will access the database connected by a local network using wireless communication. The server will receive data from the terminal device that consists of user ID, vendor ID code, transaction type code, and transaction nominal. The results of the connectivity test show that at a distance of 1-6 m TD (Terminal Device) does not experience connection failure and delivery. However, starting at a distance of 7 m, TD indicates a failure of 18% of all experiments. TD can connect when separated from access points at a distance of 10 m but has a low success rate at distances of 9 and 10 m. TD always restarts connections after multiple shipments (5-6 shipments for a distance of 9 m, and 3-4 shipments at a distance of 10 m). In testing the connection delay, access time at a distance of 1-6 meters is quite stable with a range of 3-4s. The time needed for TD to connect can be said to be quite stable between 3-4 seconds. It will increase when separated from access points at a distance of 6 m even though the success rate remains stable up to a distance of 8m. For the next development, it is planned to use a single board computer hardware.
Differential Privacy and Natural Language Processing to Generate Contextually Similar Decoy Messages in Honey Encryption Scheme

Honey Encryption is an approach to encrypt the messages using low min-entropy keys, such as weak passwords, OTPs, PINs, credit card numbers. The ciphertext is produces, when decrypted with any number of incorrect keys, produces plausible-looking but bogus plaintext called "honey messages". But the current techniques used in producing the decoy plaintexts do not model human language entirely. A gibberish, random assortment of words is not enough to fool an attacker; that will not be acceptable and convincing, whether or not the attacker knows some information of the genuine source. In this paper, I focus on the plaintexts which are some non-numeric informative messages. In order to fool the attacker into believing that the decoy message can actually be from a certain source, we need to capture the empirical and contextual properties of the language. That is, there should be no linguistic difference between real and fake message, without revealing the structure of the real message. I employ natural language processing and generalized differential privacy to solve this problem. Mainly I focus on machine learning methods like keyword extraction, context classification, bags-of-words, word embeddings, transformers for text processing to model privacy for text documents. Then I prove the security of this approach with e-differential privacy.
Robust Blind Watermarking Technique Against Geometric Attacks for Fingerprint Image Using DTCWT-DCT

In this research paper, a new blind and robust fingerprint image watermarking scheme based on a combination of dual-tree complex wavelet transform (DTCWT) and discrete cosine transform (DCT) domains is demonstrated. The major concern is to afford a solution in reducing the consequence of geometric attacks. It is due to the fingerprint features that may be impacted by the incorporated watermark, fingerprint rotations, and displacements that result in multiple feature sets. To integrate the bits of the watermark sequence into a differential process, two DCT-transformed sub-vectors are implemented. The initial sub-vectors were obtained by sub-sampling in the host fingerprint image of both real and imaginary parts of the DTCWT wavelet coefficients. The basic difference between the relevant sub-vectors of the watermarked fingerprint image in the extraction stage directly provides the inserted watermark sequence. It is not necessary to extract watermark data from an original fingerprint image. Therefore, the technique suggested is evaluated using 80 fingerprint images from 10 persons, from both CASIA-V5-DB and FVC2002-DB2 fingerprint database. For each person, eight fingerprints are set as the template and the watermark are inserted in each image. A comparison between the obtained results with other geometric robust techniques results is performed afterwards. The comparison results show that the proposed technique has stronger robustness against common image processing processes and geometric attacks such as cropping, resizing, and rotation.
Gravitational Space-Time Quantization for Charged Wormholes and the Diophantine Uncertainty Relation

This research work proceeds from the assumption, which was still considered by Einstein, that the quantization of gravity does not require additional external procedures: quantum phenomena can be a consequence of the properties of the universal gravitational interaction, which maps any physical field upon the space-time geometry.Therefore, an attempt is made in this research work to reduce the quantization of physical fields in GRT to the space-time quantization.Three reasons for quantum phenomena are considered: Partition of space-time into a set of unconnected Novikov's R-and T-domains impenetrable for light paths; the set is generated by the invariance of Einstein's equations with respect to dual mappings; The existence of electric charge quanta of wormholes, which geometrically describe elementary particles in GRT.This gives rise to a discrete spectrum of their physical and geometric parameters governed by Diophantine equations.It is shown that the fundamental constants (electric charge, rest masses of an electron and a proton) are interconnected arithmetically; The existence of the so-called Diophantine catastrophe, when fluctuations in the values of physical constants tending to zero lead to fluctuations in the number of electric charges and the number of nucleons at the wormhole throats, which tend to infinity, so that the product of the increments of these numbers by the increment of physical constants forms a relation equivalent to the uncertainty relation in quantum mechanics.This suggests that space-time cannot but fluctuate, and, moreover, its fluctuations are bounded from below, so that all processes become chaotic, and the observables become averaged over this chaos.
HDTCat: Let’s Make HDT Generation Scale

Data generation in RDF has been increasing over the last years as a means to publish heterogeneous and interconnected data. RDF is usually serialized in verbose text formats, which is problematic for publishing and managing huge datasets. HDT is a binary serialization of RDF that makes use of compact data structures, making it possible to publish and query highly compressed RDF data. This allows to reduce both the volume needed to store it and the speed at which it can be transferred or queried. However, it moves the burden of dealing with huge amounts of data from the consumer to the publisher, who needs to serialize the text data into HDT. This process consumes a lot of resources in terms of time, processing power, and especially memory. In addition, adding data to a file in HDT format is currently not possible, whether this additional data is in plain text or already serialized into HDT. In this paper, we present HDTCat, a tool to merge the contents of two HDT files with low memory footprint. Apart from creating an HDT file with the added data of two or more datasets efficiently, this tool can be used in a divide-and-conquer strategy to generate HDT files from huge datasets with low memory consumption.
Conscious Self-Regulation As A Predictor Of Sociocultural Security Of A Student'S Personalit

Correlation analysis is the basis for the presented study of the structure of the relationship between the components of perceived sociocultural security and self-regulation of university students.Sociocultural safety was assessed according to the following scales of the methodology: ethnicity, accessibility of ethnic resources, subjectivity, lateral aggression, and cultural competence of university employees.Conscious self-regulation is evaluated according to the method by V.I.Morosanova.This technique includes the following assessment scales: planning, modeling, programming, evaluating results, flexibility, independence.Mathematical and statistical data processing is carried out using the correlation coefficient of the ranks of Ch.E.It has been proved that the universal predictors of the perception of the sociocultural safety of the university environment for international students are self-regulation skills, for which knowledge of the characteristics of local culture is least necessary.The subjectivity of students is determined by the flexibility of their regulatory processes, the ability to plan, and for international students a proper orientation in their new educational, cultural, and social environment.The growth of organizational skills, mainly such as modeling and evaluation of results, helps to reduce perceived lateral aggression.For local students, the connection between the availability of ethnic resources and the components of sociocultural security comes down to planning and evaluating the results.Students with low accessibility of ethnic resources are pragmatic; they are focused on assessing current and expected results, while students with high availability of ethnic resources are freer to choose action algorithms and less rigidly attached to the expected results.
Facial Expression Recognition Using Wavelet Transform and Convolutional Neural Network

A facial expression recognition is one of the machine learning applications. It categorizes an image of facial expression into one of the facial expression classes based on the extracted features from an image. Convolutional Neural Network (CNN) is one of the classification methods in which also extracts patterns from an image. In this research, we applied the CNN method to recognize facial expression. The wavelet transform is used before being processed into CNN to improve the accuracy of facial expression recognition. The facial expression images are taken from Karolinska Directed Emotional Faces (KDEF) dataset which contains seven different facial expressions. The preprocessing of the images includes converting the image to grayscale, changing the image resolution to 256 × 256 pixels, and applying data augmentation with horizontal reflection and zoom in. The experimental results of facial expression recognition using CNN with wavelet transform achieve 84.68% accuracy and without wavelet transform achieve 81.6%. The best result is 89.6% accuracy which is obtained with the data split based on the photo session, using wavelet transform, RMSprop optimizer with learning rate 0.001, and without data augmentation.
Specific Features In Career Choices Of Cadet Class Pupils

The paper presents the results of studying specific features in career choices of cadet class pupils.Career choice is a pivotal decision in individual's life, as it largely determines their life and professional trajectory, social status, professional as well as life satisfaction.The problem of studying psychological and educational features of cadet training has become a subject of quite a few studies.In the study, a consideration is given to features of education and training in that type of educational institutions.The data obtained attests a higher level of psychological readiness to choosing a profession on behalf of the cadet class pupils in comparison with those of a comprehensive education class.A feature of cadets' professional choice is its strong connection with the profile of their education According to research data, educational environment in cadet classes is more facilitating to formation of socially mature and careeroriented personality.Undoubtedly, this feature of cadet pupils is reflected in their career choice, as it was revealed in with this research.
Accelerated WGAN update strategy with loss change rate balancing

Optimizing the discriminator in Generative Adversarial Networks (GANs) to completion in the inner training loop is computationally prohibitive, and on finite datasets would result in overfitting. To address this, a common update strategy is to alternate between k optimization steps for the discriminator D and one optimization step for the generator G. This strategy is repeated in various GAN algorithms where k is selected empirically. In this paper, we show that this update strategy is not optimal in terms of accuracy and convergence speed, and propose a new update strategy for Wasserstein GANs (WGAN) and other GANs using the WGAN loss(e.g. WGAN-GP, Deblur GAN, and Super-resolution GAN). The proposed update strategy is based on a loss change ratio comparison of G and D. We demonstrate that the proposed strategy improves both convergence speed and accuracy.
The Development Of Literacy Reading Books of North Sumatera Fairy Tale For V Grade Students at Elementary School

The purpose of this research is to develop a literacy reading books that contain North Sumatra myth to increase passion of reading in applying literacy program in school environment. The population is all of fifth grade students of SD Kalam Kudus-2 Medan in total 109 students, and the sample is class Mikha with 29 students. Sampling technique of the research is purposive sampling, which allow researcher to determine the sample with particular consideration. The research uses the development steps of Borg and Gall which are: (1) introduction survey, (2) product development, (3) product design, (4) design validation, (5) design improvement, and (6) final product. The result of the research indicates that it is really important to develop local myth reading books for teachers and students in implementing the literacy program. These books are validated by material and learning design expert teams. The presentation of validation result by material expert is in average 82, 69% with good criteria, meanwhile by learning design expert is in average 87, 94% with very good criteria. Result that shown of Bahasa Indonesia teachers is in average 98,9% with very good criteria. In addition to small area test average is 91, 66% with very good criteria. Overall,the limited area test result is in average 95% with very good criteria.
Social Risks And Cultural Transformations In The Era Of Fourth Industrial Revolution

In the era of the fourth industrial revolution, the globalization processes pose many social risks due to the widespread use of new networked social technologies, the development of self-learning programs based on artificial intelligence, and the virtualization of the human living space.Cultural transformations caused by postmodernism based on the deconstruction of a person's personality are closely intertwined with the capabilities of the achievements of big data and artificial intelligence.The widespread use of crowdsourcing technologies leads to a decrease in the importance of professional ethics and the moral responsibility of a scientist or engineer for the consequences of using their developments.The risks of merging big data technologies with the activities of the criminal world are increasing.Within the framework of cultural transformations, the destruction of all kinds of identities is observed, not only national-cultural but also gender and human.The worldview database of processes is the concept of "nothing-logia."As an experimental environment, virtual reality is used.The spread effects of the techniques transition to altered states of consciousness using virtual reality are analyzed, as well as the results of out-of-body experiments on the alienation of the body.Among the measures that can reduce the manifestation of these risks are considered strategies for the creation of state targeted programs, state expertise of virtual assistants, and network resources in order to identify and prevent destructive elements in their activities.