Reviewerly/exHarmony huggingface dataset:
Contrastive Analysis of Discourse Representation In Indonesia Newspaper (KOMPAS) and English Newspaper Reports (Jakarta Post)

Discourse representation deals with language structures which has crucial role not only for the text, but also for social practices. Newspaper is one of the real implementations of discourse representation because newspaper comprises several components in discourse representation. Newspaper in every country is made based on its language and every newspaper has difference and similarity in terms of discourse representation. Therefore, this study aimed to investigate the contrastive analysis of discourse representation in Indonesia newspaper and English newspaper reports. The subjects of this study were KOMPAS as representative of Indonesia newspaper and Jakarta Post as representative of English newspaper. This present study applied descriptive qualitative design which focused on representing verbs and modes of discourse representation (direct and indirect discourse). The findings of this study were that both newspapers have differences and similarities even though the difference was not quite dominant. The similarity of these newspapers could be seen from the use of reporting verbs and indirect discourse which were more dominant than the other parts in discourse representation. The difference could be seen also from representing verbs and modes of discourse representation. Although, reporting verbs are majority parts in Indonesia newspaper and English newspaper, but several words in Indonesia had the same translation into English. Manner of speaking verbs, mental verbs were still rare found in Indonesia newspaper. In Indonesia newspaper had only direct and indirect discourse, while in English newspaper had direct, indirect, free direct discourse, and indirect discourse in headline. In short, these newspapers have contrastive analysis in terms of discourse representation.
Using scientometrics to estimate publication activity in modern Russia

This article analyzes how state and administrative structures solve the problem of assessment by scientometric indicators specifically of the work of the faculty of Russian universities and specialists of research institutes of the Russian Academy of Sciences: how it is solved by. Past decades attitudinal evolution in Russia toward scientometric indicators is shown, the main drawbacks of using scientometric criteria are identified, and possible solutions to those drawbacks are proposed.
Stop Demam Berdarah Dengue (DBD) – Virtual Reality Application for Learning about Dengue Fever

Demam Berdarah Dengue (DBD) is a disease that has a high mortality rate each year, especially in Indonesia.Improved services to this case are essential to reduce the number of dengue fever victims.However, the lack of knowledge in the community regarding dengue fever makes the efforts of eradicating this disease not optimal.Therefore, it is necessary to provide knowledge of dengue fever in the community.This study aimed to develop an android-based dengue prevention educational game application with Virtual Reality (VR) technology to provide knowledge of dengue fever to the public.This application development used the waterfall method which consisted of analysis, design, implementation, testing, and maintenance stages.Black Box Testing Method and System Usability Scale (SUS) were used as testing methods.SUS testing of 30 respondents obtained an average score of 79.4 (excellent), which means that the application was accepted by respondents.
Vision technique for deflection measurements based on laser positioning

This paper proposes a vision-based target-free deflection measurement method of bridge. This method eliminates the trouble of installing artificial targets on the structure. The proposed method utilizes multi-point laser projection as a reference point, which can eliminate the error caused by vibration at the camera position, and also avoids the difficulty of finding the background building as a reference point. The proposed method controls the measurement accuracy by adjusting the size of the captured video area. Through the method of camera scanning, large-scale multi-point deflection measurement is carried out under the premise of certain precision. The video laser spot reference point (LSRP) before loading is selected by the image segmentation algorithm. The Oriented features from accelerated cegment Test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) feature detection and extraction algorithm is used to detect and extract key features in the region of interest (ROI) near the LSRP. The extracted key feature points calculate the displacement of each frame of the entire video by using the Lucas-Kanade Tracking (KLT) algorithm, which ensures the maximum number of matches of key points between consecutive images. The mismatched outliers are discarded by using the Random Sampling Consistency (RANSAC) algorithm. The accuracy of the proposed method was verified by an indoor and outdoor beam experiment. In the experiment, the results obtained by the proposed method are compared with the vertical displacement values of the corresponding positions acquired by the displacement meter obtained by the high-precision DH3816N dynamic strain test system to verify the validity and accuracy of the proposed method.
LCAHASH-1.1: A New Design of the LCAHASH System for IoT

The present paper represents an extension of LCAHASH system.LCAHASH is a previously developed lightweight hash algorithm.It is based on cellular automata.It was developed as an alternative to existing hash functions to ensure data integrity and to meet the security requirements of the Internet of Things devices.Due to the limited amount of storage and the limited computation capabilities of these devices, the algorithms used by these devices should be as efficient and as robust as possible.In this contribution, we propose an enhanced version of the original LCAHASH algorithm to improve its efficiency and its robustness.A description of the system proposed along with a security analysis and the results of a statistical battery of tests (Dieharder) are included.These results show that the system proposed exhibits good statistical and cryptographic features.
Rich Style Embedding for Intrinsic Plagiarism Detection

Stylometry plays an important role in the intrinsic plagiarism detection, where the goal is to identify potential plagiarism by analyzing a document involving undeclared changes in writing style.The purpose of this paper is to study the interaction between syntactic structures, attention mechanism, and contextualized word embeddings, as well as their effectiveness on plagiarism detection.Accordingly, we propose a new style embedding that combines syntactic trees and the pre-trained Multi-Task Deep Neural Network (MT-DNN).Additionally, we use attention mechanisms to sum the embeddings, thereby experimenting with both a Bidirectional Long Short-Term Memory (BiLSTM) and a Convolutional Neural Network (CNN) maxpooling for sentences encoding.Our model is evaluated on two sub-task; style change detection and style breach detection, and compared with two baseline detectors based on classic stylometric features.
An Empirical Comparison of Machine Learning Algorithms for Classification of Software Requirements

Intelligent software engineering has emerged in recent years to address some difficult problems in requirements engineering.Requirements are crucial for software development.Moreover, the classification of natural language user requirements into functional and non-functional requirements is a fundamental challenge as it defines the fulfillment criteria of the users' expected needs and wants.Therefore the research of this article aims to explore and compare random forest algorithm and gradient boosting algorithm to determine the accuracy of functional requirements and non-functional requirements in the process of requirements classification through the conduct of experiments.Random forest and gradient boosting are ensemble algorithms in machine learning that combines the decisions from several base models to improve the prediction performance.Experimental results show that the gradient boosting algorithm yields improved prediction performance when classifying nonfunctional requirements, in comparison to the random forest algorithm.However, the random forest algorithm is more accurate to classify functional requirements.
Modification of KNN Algorithm

K-Nearest Neighbor (KNN) classification is one of the most fundamental and simple classification methods. It is among the most frequently used classification algorithm in the case when there is little or no prior knowledge about the distribution of the data. In this paper a modification is taken to improve the performance of KNN. The main idea of KNN is to use a set of robust neighbors in the training data. This modified KNN proposed in this paper is better from traditional KNN in both terms: robustness and performance. Inspired from the traditional KNN algorithm, the main idea is to classify an input query according to the most frequent tag in set of neighbor tags with the say of the tag closest to the new tuple being the highest. Proposed Modified KNN can be considered a kind of weighted KNN so that the query label is approximated by weighting the neighbors of the query. The procedure computes the frequencies of the same labeled neighbors to the total number of neighbors with value associated with each label multiplied by a factor which is inversely proportional to the distance between new tuple and neighbours. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the significant improvement in the performance of KNN method.
Developing Lexicon-based Algorithms and Sentiment Lexicon for Sentiment Analysis of Saudi Dialect Tweets

Majority of studies on sentiment analysis field, specifically Arabic lexicon-based approach, are focused on doing preprocessing methods on targeted dataset text or collected textual data from Twitter (Twitter dataset) rather than dealing with lexicon itself.This study proposes a new method, we constraint firstly on building a new sentiment lexicon with reasonable number of words and then doing adequate preprocessing methods on the lexicon's words in addition to the (Twitter dataset).The study presents Saudi Dialect Sentiment lexicon called SaudiSentiPlus contains 7139 words which mostly generated from Saudi tweets and other dictionaries.Moreover, this study also presents two lexicon-based algorithms for Saudi dialect to deal with (prefixes and suffixes) letters in order to increase performance of proposed Saudi dialect lexicon.The experiment which has been conducted in this study to evaluate the performance of SaudiSentiPlus comprises four phases.The precision, recall, accuracy, and F-Score are measured in every phase.We built our testing dataset from twitter by focusing on Saudi dialect hashtags (971 thousands tweets from 162 hashtags).The results, show that accuracy of SaudiSentiPlus with the two lexicon-based algorithms reached to 81%.
Big data, Cloud and 5G networks create smart and intelligent world: A survey

Big Data is a term most commonly used to define data bigger than what we normally deal with. It can be defined as data which is too large and complex for standard computer hardware to deal with and requires much more space and processing power, something that personal computers and even some supercomputers don’t. For this purpose we use Cloud Computing. Cloud Computing is a very powerful technology that is used mostly to solve complex problems and provide the required space for the task at hand. It also helps us by allotting us a separate online space that saves us a lot of money by stopping us from buying expensive hardware to increase memory storage. Ever since the introduction of cloud computing, big data in 5G and its uses have sky rocketed. Tending to enormous information is a testing and time-requesting errand that requires an extensive computational foundation to guarantee effective information handling and examination. Cloud computing is an incredible innovation to perform gigantic scale and complex figuring. It kills the need to keep up costly figuring equipment, committed space, and programming. The 4V’s of big data – volume, velocity, variety and veracity makes the data management and analytics challenging for the traditional data warehouses. Cloud computing is by all accounts an ideal vehicle for facilitating big data tasks at hand in 5G. Nonetheless, dealing with big data in the cloud brings its very own test of accommodating two opposing structure standards. Cloud computing depends on the ideas of solidification and asset pooling, yet big data frameworks, (for example, Hadoop) are based on the shared nothing standard, where every hub is independent. The capacity to store a lot of information in various structures and process everything at vast velocities will result in information that can direct organizations and instruction establishments in growing quick
Parallel Corpus of Croatian-Italian Administrative Texts

Parallel corpora constitute a unique resource for providing assistance to human translators.The selection and preparation of the parallel corpora also conditions the quality of the resulting MT engine.Since Croatian is a national language and Italian is officially recognized as a minority language in seven cities and twelve municipalities of Istria County, a large amount of parallel texts is produced on a daily basis.However, there have been no attempts in using these texts for compiling a parallel corpus.A domain-specific sentencealigned parallel Croatian-Italian corpus of administrative texts would be of high value in creating different language tools and resources.The aim of this paper is, therefore, to explore the value of parallel documents which are publicly available mostly in pdf format and to investigate the use of automatically-built dictionaries in corpus compilation.The effects that a document format and, consequently sentence splitting, and the dictionary input have on the sentence alignment process are manually evaluated.
Dubious Early Medieval Jewels from Matzhausen (Bavaria): New Material and Technological Investigations

A controversial set of elite jewels assumed to be of early medieval period, recovered in 1937–1938 from a burial site in Matzhausen (northeastern Bavaria) was investigated to determine their technological coherency as well as their authenticity. The discovery of these jewels was exploited by German nationalists to argue that the material culture observed in Bohemia and Moravia in the ninth century had Germanic roots. The circumstances of their discovery, however, remain unclear. Research in archives demonstrated the involvement of the SS Ahnenerbe organisation in the recovery of the jewels. Are these jewels authentic or are they forgeries manufactured or modified by a modern craftsman to serve German expansionist nationalism? Three jewels recovered in the grave – a gombik (hollow spherical pendant), an earring with chainlet, and a zoomorphic figurine – were investigated using scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX), X-radiography, and stereomicroscopy. The investigation revealed a very heterogeneous set of objects with respect to technology and state of preservation. It also uncovered repairs and past invasive interventions involving modern metal and solder. Comparison with similar types of jewels from Bohemia and Moravia points to important differences in construction and technology, which confirms the uniqueness of this set of objects and casts suspicion on their authenticity.
Quantum adiabatic algorithms using unitary interpolation*

We present two efficient quantum adiabatic algorithms for Bernstein–Vazirani problem and Simon’s problem. We show that the time complexities of the algorithms for Bernstein–Vazirani problem and Simon’s problem are O (1) and O ( n ), respectively, which are the same complexities as the corresponding algorithms in quantum circuit model. In these two algorithms, the adiabatic Hamiltonians are realized by unitary interpolation instead of standard linear interpolation. Comparing with the adiabatic algorithms using linear interpolation, the energy gaps of our algorithms keep constant. Therefore, the complexities are much easier to analyze using this method.
PENERAPAN ALGORITMA C4.5 PADA KLASIFIKASI POTENSI SISWA DROP OUT

Students are one of the substances that need to be considered in relation to the world of education today. The difficulty of getting students makes the school have to optimize the learning system and infrastructure as well to attract the interest of new students and also make students who have gone to school not drop out or drop out. One of the factors contributing to the large number of students dropping out is because of the lack of policies and actions from the education institutions to keep their students from dropping out. The purpose of this study was to classify potentially dropout students and not have the potential to drop out with the C4.5 algorithm as a reference in making policies and actions to reduce the number of students dropping out. The classification results of the C4.5 algorithm are evaluated and validated with RapidMinerStudio to determine the accuracy of the C4.5 Algorithm in classifying potential dropouts.Keywords: Student dropout, Classification, C4.5 Algorithm
ACADEMIC LIBRARIES IN THE CONTEXT OF TRANSFORMATION AND DIGITALIZATION OF SCIENCE

The paper discloses the key approaches to understanding of current transformations in ideology and technology of resource-building of a today's scientific library developed by workers of the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences (Novosibirsk, Russia).Cognitive value of involving methodological toolset elaborated by various scientific disciplines to dealing with problems of stock science, in particular problems related with indicators of document selection for a library fund, is argued.The authors outline their own opinion on current issues in elaborating of hierarchical system of priorities in forming, using, and preserving of multi-format documents in a today's Russian academic library.
FPGA Implementation of Katan Block Cipher for Security in Wireless Sensor Networks

In this paper we are going to design an efficient and enhanced low weight cipher algorithm for multipurpose applications.A number of new factors like limited computational power, RAM size, ROM size, register width, different operating environment and etc., constrained WSN (Wireless Sensor Networks) to use traditional security measures.These constraints on Wireless sensor networks enabled devices results in the emergence of a new field, Lightweight Cryptography.Recently a number of software and hardware implementation of lightweight ciphers are designed for Wireless sensor networks applications.To attain the declared objective, we choose a single cipher only.Which is specifically the KATAN light -weight block cipher algorithm and it needed to be modeled, implement and optimize on specific XILINX ISE platform.
Improved algorithms in parallel evaluation of large cryptographic S-boxes

Nowadays computational complexity of fast Walsh Hadamard transform and non-linearity for Boolean functions and large cryptographic substitution boxes is a major challenge of modern cryptography research on strengthening encryption schemes against linear and differential attacks. Time and memory complexities of the best existing algorithm for computing fast Walsh Hadamard transform and non-linearity for n×m substitution boxes is O(2n+m). This paper proposes three new innovation in acceleration methods that improve the computation time for paralleled Walsh matrix up to 68 folds and the evaluation time for non-linearity degree up to 1021 folds. These methods include defining and accessing Walsh matrix transpose and combining an important part of evaluation process of non-linearity in the computation algorithm of Walsh matrix. The validity of the proposed algorithms is verified by means of simulation and experiments.The overall analysis of resource consumption of proposed algorithms is compared with previous ones.
Continuous Collateral Privacy Risk Auditing of Evolving Autonomous Driving Software

Autonomous driving systems have a rich and diverse set of sensors and collect a tremendous amount of data during their operations. This has significant implications for individual privacy and induces a new type of potential privacy risks - collateral privacy risks. It is important for the public and the developer community to be aware of the collateral privacy risk posed by current autonomous driving software systems. We performed data privacy analysis for the Apollo project, an open-source autonomous driving software system. We applied source code-based privacy auditing techniques tailored for this particular problem and produced preliminary results, although there were unresolved open issues remaining. As we performed auditing, Apollo was upgraded from version 3.0 to 3.5 with significant under-the-hood technology changes. It was a challenge to perform the analysis as the underlying software evolves and maintain a result that is up-to-date. To address this challenge, we developed and deployed a continuous source code privacy risk analysis tool to assist in the process. In this paper, we discuss our experience and lessons learned from this industrial case study.
Augmented reality to facilitate students’ biology mastering concepts and digital literacy

As one of abstract concepts, nervous system requires proper media which can represent the real condition. The purpose of this study was to observe the effect of augmented reality (AR) on students’ mastering concept and digital literacy in biology. This quasi-experimental research was designed using non-equivalent control group design in which the instruments were test and questionnaire. The data were analyzed using independent samples t-test. The results indicated that AR gave significant positive effect on students’ mastering concept. However, as it has no the same effect to students’ digital literacy, the further observation to be done to ensure the undergirding reason behind the phenomena.
The Application of Differentiated Instruction in Reading and Writing to a Boy with Autism in Early Childhood Education. A Case Study

A number of research findings show that, because of the phenomenon of classroom heterogeneity, in our days, there is a need for the application of differentiated instruction.The latter is a modern teaching approach, which has as its ultimate objective the provision of the best possible education for pupils with special educational needs, including autism.By applying differentiated instruction, the teacher can enhance the learning skills of students as well as contribute to their cognitive and socio-emotional development.The purpose of this research is to investigate the effectiveness of differentiated instruction in the writing and reading skills of a boy with autism in early childhood education.To this end two types of research methodologies were applied, qualitative research (semi-structured interviews) and action research through a series of activities, based on the principles of differentiated instruction.The results of this research showed that the application of differentiated instruction has produced positive results, for the boy who has autism, in both reading and writing.