Reviewerly/exHarmony huggingface dataset:
Cryptographic Algorithm Performance Measurement For The Secure Data Transmission In Manufacturing Execution System

The high developments of computing technologies have developed powerful and integrated software applications thus evolving the significance of the Manufacturing Execution System. The functional background of Manufacturing Execution system supports from the purchase of raw material, production order release till the delivery of the finished products. This research work discusses the complexity of manufacturing execution system in fulfilling the needs of the customers for secure data transmission using Cryptographic algorithm. Cryptographic algorithms are familiar in usage for data encryption, digital signature, and authentication. Cryptography promises personal protection and non-alteration, etc. It is important to preserve the security of correspondence through open channels and to demonstrate the validity of an incoming message. The art of translating a readable text (plain text) into an unreadable text (ciphertext) that ensures data protection is known as cryptography. In this paper, the various cryptography (DES, AES, Blowfish, RSA, and Hash) techniques are implemented and results were compared. This paper proposes and develops an overall design and approach for robotic security systems for safe communication.Keywords: Cyber Physical Systems, Cyber Physical System Security, Cryptography, Adaptive Security Performance.
Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning

Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate (Fodor et al., 1988; Fodor &McLaughlin, 1990) between connectionist and classicist, wherein the latter continually advocates an algebraic treatment in cognitive architectures. In this work, we follow the classicist's call and propose a hybrid approach to improve systematic generalization in reasoning. Specifically, we showcase a prototype with algebraic representations for the abstract spatial-temporal reasoning task of Raven’s Progressive Matrices (RPM) and present the ALgebra-Aware Neuro-Semi-Symbolic (ALANS2) learner. The ALANS2 learner is motivated by abstract algebra and the representation theory. It consists of a neural visual perception frontend and an algebraic abstract reasoning backend: the frontend summarizes the visual information from object-based representations, while the backend transforms it into an algebraic structure and induces the hidden operator on-the-fly. The induced operator is later executed to predict the answer's representation, and the choice most similar to the prediction is selected as the solution. Extensive experiments show that by incorporating an algebraic treatment, the ALANS2 learner outperforms various pure connectionist models in domains requiring systematic generalization. We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer.
Human Resources Assignment in R&D Departments from Automotive Industry

The objective of this paper is to analyse how the expertise of the employees is used inside the companies in automotive software industry, to present the ideal case of expertise growth and to propose a model that motivates the employees to share their knowledge. The proposed model for increasing the expertise during order execution has a simple structure and is to be implemented straightforwardly. The most performing employees from an area of expertise will become mentors, and those who will be mentored will be the employees who are showing that they can handle their tasks with ease and high quality. In addition, two models can be found in this paper: the model for training evaluation and the development path for every level of expertise in the team.
Οι νέες τεχνολογίες στα πλαίσια της διαπολιτισμικής δευτεροβάθμιας εκπαίδευσης. Διαμόρφωση των πολιτών του κόσμου.

The meticulous study of the usefulness that new technologies play in our nowadays lives in the context of Intercultural Secondary Education has been one of the most interesting issues pursued by those involved in the field of pedagogy. Today, more than ever before, it seems that the contribution that digital educational applications have to the education process as a whole is widely accepted, occupying a constantly increasing trend among educational systems worldwide, in an attempt, of enhancing the efficiency of both native students and immigrants.Therefore, the present thesis examines the extent to which new technologies have penetrated into Intercultural Secondary Education and the role that they play in shaping the citizens of tomorrow, worldwide.In particular, this thesis consists of 2 parts. The 1st part refers mainly to the theoretical framework regarding Intercultural Education and the general use that new technologies have in our everyday lives. Specifically, we develop briefly the role and the goals of Intercultural Education, the characteristics and particularities of multicultural educational methods, as well as the profile that the ideal teacher should have. In addition, we note possible problems that may arise during the educational process, as well as some of the most important multicultural centers worldwide. Moreover, the value and importance that new technologies play in education is acknowledged in general, as well as their contribution to distance education, with specific reference to digital educational games.In the 2nd part of this thesis, the criteria used to determine the effectiveness of digital educational applications in Intercultural Secondary Education are presented, with a brief analysis of those criteria observed in existing digital educational applications having to do with the courses of Language, Culture, Religious, Natural Sciences, Mathematics and Biology – Anatomy. In conclusion, the proper use of digital educational applications under the guidance of knowledgeable teachers / instructors can lead students to receive suitable education regardless of their national, religious and cultural background, as well as to develop intercultural skills appropriate for the modern world – culture.
Application of Matrix in Hill Cipher Algorithm

The aim of the research is applying the 3x 3 matrix in the Hill Cipher of cryptography. Hill cipher is one of cryptography algorithm used of symmetric matrix. It uses an n x n sized as key matrix to convert from plaintext to ciphertext.  The method of research is quantitative method. The hill cipher algorithm has two processes they are encryption and decryption. The matrix used in Hill Cipher algorithm is multiplication between matrix and inverses matrix. Hill cipher used is key matrix ordo 3x3 an modulo theory. The result of the research is Matrix operations used in the hill cipher encryption process such as multiplication, transpose and determination of a matrix's determinants. The more matrix operation is in a hill cipher, the higher the security level of a cipher. The more complicated the application of the matrix in the hill cipher, the significant function of the hill cipher in maintaining data security.
k-NN improvement to data analysis

The problem to classify big data is an actual one the subject. There are multiple ways to classify data but the k Nearest Neighbors (k-NN) has become a popular tool for the data scientist. In this paper we examine several modifications of the k Nearest Neighbors algorithm that achieve better efficiency in terms of accuracy and CPU time when classifying test observations in comparison to the standard k Nearest Neighbors algorithm. To make the modifications faster than standard k-NN we use a special methodology which splits the input dataset into n folds and combine it with input data transformations. Each time we execute the process, one of the folds is saved as a test subset and the rest of the folds are applied for training. The process is executed n times. In the proposed methodology we are looking for the pair of subsets which produces the highest accuracy result.
METHODICAL APPROACHES TO THE ASSESSMENT OF SMART ECONOMICS

Smart economics focuses on key areas to achieve its goals. The chain of building relationships is formed within the interaction of various elements of smart economy. In the concept of smart economics, the functioning of the government as the main regulatory element for the formation of this type of economic system within a single economy occupies an important place. In this aspect, the main parameters of the government within the smart economy are transparency, e-government, Open Data. It should be noted that different subjects of smart economics may have different assessment indicators, but they are all within the basic groups of needs and types of economic activity, including: material income; material deprivation; employment opportunities; education and training; functioning of the health care system; dwelling; access to child care; the right to leave; decent social security; safe environment; environmentally friendly environment; non-discrimination; access to justice. For cities, as separate entities of the smart economy, several groups of evaluation indicators are identified, identified in the study of financial centers and global cities. Scientific papers attempt to assess the smart economy by the parameters of the city, including municipal and business services, smart construction, smart education, smart energy and lighting, smart energy metering, smart water supply and recycling, public safety and others. In general, the green economy occupies a very important place in the concept of smart economy, which at the present stage of development acquires new characteristics, including not only environmental projects, but also the parameters of comfortable living and well-being. We can note that the concept of smart economy is quite closely intertwined with the concept of green economy in some indicators, especially with regard to social inclusion and greening. For example, the Green Economy Progress Index identifies gender equality, education, life expectancy, and well-being, which indicates the need to develop a unified approach or index to the assessment of the smart economy.
Translation of Idioms: How They are Reflected in Movie Subtitling

Idiomatic expressions are expressions with a figurative meaning different from their literal meaning, and therefore, they cannot be understood literally. This study aims to scrutinize some aspects of the translation of idiomatic expressions in a mystery movie entitled ‘Murder on the Orient Express’. Seventy-seven idiomatic expressions were found in the movie. The idioms can be categorized as follows: keywords with idiomatic uses (16) idioms with nouns and adjectives (2), idiomatic pairs (1), idioms with prepositions (7), phrasal verbs idioms (26), verbal idioms (5), idioms from special subjects (1), idioms with key words from special categories (16), and idioms with comparison (3). Phrasal verbs idiom was the most frequently occurred because idioms are mostly organized by verb comprising the combination of verb and preposition or verb and adverb. Further, the rendering of the idioms from English as the Source Language (SL) into Indonesian as the Target Language (TL) employed some strategies; translation by paraphrase was the highly frequent translation strategy. The strategy of using an idiom of similar meaning but dissimilar form was also located in the high-frequency position of the continuum. Furthermore, using an idiom of similar form and meaning and translation by omission were the translation strategies of low frequency. Translation by paraphrase was frequently used because the translator wanted to convey the meaning as clearly as possible while also considering the cultural difference between SL and TL. To sum up, the idiomatic expression dominantly used in the movie was phrasal verbs (34%), while the strategy that was mostly applied in translating them was translation by paraphrase (56%).
Eco-Brick: Bahan Bangunan Kokoh Sebagai Solusi Untuk Mengurangi Sampah Plastik Di Indonesia

Abstract Trash is the most complicated problems in the world including Indonesia. The most dangerous and poisonous trash in the world is non-organic waste; one of them is the plastic waste. Every year, the plastics waste is increase. There are so many actions to reduce a plastic waste, but it was not affected at all because the product of plastics is always increasing drastically. Also, those actions to reduce a plastic waste is not easy to do, so not all people cannot give their contribution to decrease of using plastics waste. The one of the actions to solve this problem is making some eco-brick from one used-plastic bottle. Eco-brick is a building material made from plastic waste. It can be used to make some furniture, a decoration, and build a simple building. Until today, people still doubt about the durability of plastic waste if it use to make sturdy object. This paper presents the quality of plastic after heating it up until 100 o C. The result of this paper hopes that people believe the sturdy of plastic if that it is used as a brick. Keywords: Eco-brick, the durability of plastic, plastic waste, & trash
STUDY ANALYSIS OF DRAINAGE ROAD ADAM MALIK CITY SAMARINDA.

Agus Setiawan, NPM 11.11.1001.7311.092, 2011, Civil Engineering Study Program, Faculty of Engineering, University of 17 August 1945 Samarinda. Flood is natural disaster that occurs due to the inability of channel of a region to accommodate the high rainfall in the region. One solution to overcome the flood is to improve the dimensions of the drainage channel to be able to accommodate the amount of water debit contained in a region. This research took place in Streat Adam Malik, Kec. Sungai Kunjang, Samarinda City. Calculation analysis using Gumbel and Log Person Type III method to calculate drainage channel capacity. Based on the calculation of the existing flood discharge of drainage channel at the research location that is smallest 2,571 m3/dt - biggest 3,450 m3/dt and flood discharge design at Streat Adam Malik Samarinda City that is smallest 3.114 m3/dt - biggest 4,490 m3/dt with kala 10 years until year 2027. With the design of dimensional cross-sectional capacity : an average upper width of 2.00 meters width an average of 2,00 meters, and an average height of 1,50 meters. From the calculation results can be concluded that the drainage channel capacity enough to accommodate all the water discharge that exist in the area.
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation

Adversarial learning strategy has demonstrated remarkable performance in dealing with single-source unsupervised Domain Adaptation (DA) problems, and it has recently been applied to multi-source DA problems. While most of the existing DA methods use multiple domain discriminators, the effect of using multiple discriminators on the quality of latent space representations has been poorly understood. Here we provide theoretical insights into potential pitfalls of using multiple domain discriminators: First, domain-discriminative information is inevitably distributed across multiple discriminators. Second, it is not scalable in terms of computational resources. Third, the variance of stochastic gradients from multiple discriminators may increase, which significantly undermines training stability. To fully address these issues, we situate adversarial DA in the context of information regularization. First, we present a unified information regularization framework for multi-source DA. It provides a theoretical justification for using a single and unified domain discriminator to encourage the synergistic integration of the information gleaned from each domain. Second, this motivates us to implement a novel neural architecture called a Multi-source Information-regularized Adaptation Networks (MIAN). The proposed model significantly reduces the variance of stochastic gradients and increases computational-efficiency. Large-scale simulations on various multi-source DA scenarios demonstrate that MIAN, despite its structural simplicity, reliably outperforms other state-of-the-art methods by a large margin especially for difficult target domains.
An optimal quantum sampling regression algorithm for variational eigensolving in the low qubit number regime

The VQE algorithm has turned out to be quite expensive to run given the way we currently access quantum processors (i.e. over the cloud). In order to alleviate this issue, we introduce Quantum Sampling Regression (QSR), an alternative hybrid quantum-classical algorithm, and analyze some of its use cases based on time complexity in the low qubit number regime. In exchange for some extra classical resources, this novel strategy is proved to be optimal in terms of the number of samples it requires from the quantum processor. We develop a simple analytical model to evaluate when this algorithm is more efficient than VQE, and, from the same theoretical considerations, establish a threshold above which quantum advantage can occur. Finally, we demonstrate the efficacy of our algorithm for a benchmark problem.
More Side Information, Better Pruning: Shared-Label Classification as a Case Study

Pruning of neural networks, also known as compression or sparsification, is the task of converting a given network, which may be too expensive to use (in prediction) on low resource platforms, with another 'lean' network which performs almost as well as the original one, while using considerably fewer resources. By turning the compression ratio knob, the practitioner can trade off the information gain versus the necessary computational resources, where information gain is a measure of reduction of uncertainty in the prediction. In certain cases, however, the practitioner may readily possess some information on the prediction from other sources. The main question we study here is, whether it is possible to take advantage of the additional side information, in order to further reduce the computational resources, in tandem with the pruning process? Motivated by a real-world application, we distill the following elegantly stated problem. We are given a multi-class prediction problem, combined with a (possibly pre-trained) network architecture for solving it on a given instance distribution, and also a method for pruning the network to allow trading off prediction speed with accuracy. We assume the network and the pruning methods are state-of-the-art, and it is not our goal here to improve them. However, instead of being asked to predict a single drawn instance x, we are being asked to predict the label of an n-tuple of instances (x1,…xn), with the additional side information of all tuple instances share the same label. The shared label distribution is identical to the distribution on which the network was trained. One trivial way to do this is by obtaining individual raw predictions for each of the n instances (separately), using our given network, pruned for a desired accuracy, then taking the average to obtain a single more accurate prediction. This is simple to implement but intuitively sub-optimal, because the n independent instantiations of the network do not share any information, and would probably waste resources on overlapping computation. We propose various methods for performing this task, and compare them using extensive experiments on public benchmark data sets for image classification. Our comparison is based on measures of relative information (RI) and n-accuracy, which we define. Interestingly, we empirically find that I) sharing information between the n independently computed hidden representations of x1,..,xn, using an LSTM based gadget, performs best, among all methods we experiment with, ii) for all methods studied, we exhibit a sweet spot phenomenon, which sheds light on the compression-information trade-off and may assist a practitioner to choose the desired compression ratio.
The Icelandic Wordweb 21.02

A single RDF file housing the Icelandic Wordweb in an LT-appropriate format. The Wordweb's features have been encoded with OntoLex and SKOS, with the new version designed in such a way as to replicate all core functionality of the original, and offer an extensive suite of new options for inspection and research.Ein stok RDF skra sem hýsir Islenskt orðanet a maltaeknivaenlegu sniði. Eiginleikar Orðanetsins hafa verið utfaerðir með OntoLex og SKOS, og nýja utgafan honnuð a þann hatt að hun baeði styður alla grunnvirkni upprunalega Orðanetsins, auk þess að bjoða itarlegar og fjolbreyttar nýjar leiðir til að kanna innihald þess og beita þvi til rannsokna.
EVALUATION OF SOCIALIZATION PROGRAMS AND PROMOTIONS OF OPEN UNIVERSITY IN INCREASING PARTICIPATION OF UPBJJ-UT MATARAM NEW STUDENTS

This study aims to determine the effectiveness of socialization and promotion strategies in increasing student participation rates at UT Mataram. The method used in this research is correlational descriptive survey method. The results of this study are: 1. The target target of the socialization and promotion program in the future by utilizing information technology as a promotional media, 2. The number of respondents who know about UT is 101 people or 77.7%, while respondents who do not know about UT are as many as 101 people. 29 people or 22.3% indicates that the dissemination of information about UT in the NTB region is still not optimal. 3. The effectiveness of the social program has an impact on increasing student participation rates. Then sosprom activities in the period 2019.2 can be used as a reference for the activity stage in the next period
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents

Supervised learning models perform best when trained on a lot of data, but annotating training data is very costly in some domains. Active learning aims to chose only the most informative subset of unlabelled samples for annotation, thus saving annotation cost. Several heuristics for choosing this subset have been developed, which use fix policies for this choice. They are easily understandable and applied. However, there is no heuristic performing optimal in all settings. This lead to the development of agents learning the best selection policy from data. They formulate active learning as a Markov decision process and applying reinforcement learning (RL) methods to it. Their advantage is that they are able to use many features and to adapt to the specific task. Our paper proposes a new approach combining these advantages of learning active learning and heuristics: We propose to learn active learning using a parametrised ensemble of agents, where the parameters are learned using Monte Carlo policy search. As this approach can incorporate any active learning agent into its ensemble, it allows to increase the performance of every active learning agent by learning how to combine it with others.