Reviewerly/exHarmony huggingface dataset:
Scalable Saturation of Streaming RDF Triples

In the Big Data era, RDF data are produced in high volumes. While there exist proposals for reasoning over large RDF graphs using big data platforms, there is a dearth of solutions that do so in environments where RDF data are dynamic, and where new instance and schema triples can arrive at any time. In this work, we present the first solution for reasoning over large streams of RDF data using big data platforms. In doing so, we focus on the saturation operation, which seeks to infer implicit RDF triples given RDF Schema or OWL constraints. Indeed, unlike existing solutions which saturate RDF data in bulk, our solution carefully identifies the fragment of the existing (and already saturated) RDF dataset that needs to be considered given the fresh RDF statements delivered by the stream. Thereby, it performs the saturation in an incremental manner. Experimental analysis shows that our solution outperforms existing bulk-based saturation solutions.
Based on point-to-point network extended credit authentication model

Abstract Research shows that P2P technology exist many problems about reliability in the Internet credit industry. It has become particularly important to ensure the reliability of users and improve the success rate of transactions. In this paper, the traditional trust identification model is improved, and the P2P business transaction is integrated into the credit identification model as a dynamic indicator. -On this basis, a block chain is constructed with credit recognition model as intelligent contract. Through the block chain network based on the credit identification model, the credit value of each user will be dynamically evaluated. The block chain network will ensure the credibility of the dynamic indicators of transactions, so as to ensure the reliability of the customer credit value evaluation. Therefore, a quantitative standard that both parties can trust can be established. Without the guarantee of the third party, both sides of P2P transaction can define the trustworthiness of the transaction parties. That guarantees the reliability of the transaction.
Sistem Pakar Diagnosa Kerusakan Dan Perawatan Laptop

Expert System for diagnosing laptop damage and maintenance is an information and communication system that arises to provide solutions to a problem related to the laptop itself. This system is designed using the backward chaining method with the first dept technique as its inference engine, black box as a testing system, Microsoft access 2007 as the database and visual basic 6.0 as the programming language. Data collection was carried out for three months using interview and observation methods which were located at Central Media Computer. This research resulted in an expert system to provide information on symptoms, diagnoses and solutions to laptop damage and in the usability aspect based on testing using the Computer System Usability Questionnare produced a total score of 1538 which was converted on an index scale to a value of 85.4% included in the "Very Eligible" category.
Inter-Algorithm Multiobjective Cooperation for Phylogenetic Reconstruction on Amino Acid Data

Inter-algorithm cooperative approaches are increasingly gaining interest as a way to boost the search capabilities of evolutionary algorithms (EAs). However, the growing complexity of real-world optimization problems demands new cooperative designs that implement performance-driven strategies to improve the solution quality. This article explores multiobjective cooperation to address an important problem in bioinformatics: the reconstruction of phylogenetic histories from amino acid data. The proposed method is built using representative algorithms from the three main multiobjective design trends: 1) nondominated sorting genetic algorithm II; 2) indicator-based evolutionary algorithm; and 3) multiobjective evolutionary algorithm based on decomposition. The cooperation is supervised by an Elite island component that, along with managing migrations, retrieves multitrend performance feedback from each approach to run additional instantiations of the most satisfying algorithm in each stage of the execution. Experimentation on five real-world problem instances shows the benefits of the proposal to handle complex optimization tasks, in comparison to stand-alone algorithms, standard island models, and other state-of-the-art methods.
AN EMBEDDABLE SELF-REFLECTIVE SELF-ADAPTIVE SOFTWARE MODEL FOR RESOURCE MANAGEMENT

With the growing complexity of today's world, there is rise in the design of smart and autonomous technologies.These technologies (or systems) are usually software-intensive and should have ability to self-manage, optimize resources, eliminate or reduce human intervention and still achieve quality and optimal output(s).Thus, technologies of this nature should self-reflect and self-adapt to internal and external factors to achieve its desired goal.Self-reflective, selfadaptive concepts are (or can be) used in a number of different areas including: robotics systems, autonomous computing, space systems, manufacturing, recommender systems, and e-commerce (for transactions monitoring), among others.These technologies basically have software component, which are usually underpinned by model(s).The main goal of this research, therefore, is to develop an embeddable software model (with self-reflective, selfadaptive properties) that shows real-time resource management.In developing our model, we particularly took into cognizance MAPE-K template for identification of self-* properties and performance requirements.We applied a Case Study within a particular domain (the education sector) to demonstrate the usability of this software model in managing human resource (a primary resource).The software model is able to reflect and modify its behavior at run-time in response to internal and external factors.Evaluation results show that the software model successfully self-manage the deployment of human resources; it was able to determine over/under-staffing, and redistribute (and/or identify recruitment needs for) human resource within the domain under consideration.
A Modern themed System for Patients Security of data exposure in semi-convinced Servers in the Cloud

Cloud computing is intermingled distributed storing platform where information is housed in virtualized storage collections which are normally operated by tertiary parties.Patients healthiness record (PHR) is an evolving patient-centered paradigm of healthiness info sharing, problems such as threats of confidentiality leakage, key management scalability, scalable access, and effective consumer revocation, provide the most significant challenges if we introduce a modern patient-centered architecture and a series of info contact control protocols for PHRs maintained in semi-trusted repositories.They use attribute-based encryption (ABE) strategies to encrypt the PHR file of each individual to obtain fine-grained and robust user access protection for PHR's.The project often provisions numerous owner situations and splits the device workers into many protection fields which significantly reduces the difficulty of key managing for holders and handlers.We introduce this program and test upon Drive HQ cloud to validate that our latest framework offers safe data control to outsourced info.
SEMIOTICS ANALYSIS OF RUANGGURU ADVERTISING IN TELEVISION MEDIA

RuangGuru is one of the most successful startups in Indonesia, which is engaged in online tutoring. The application user has reached up to 15 million students. This achievement is the result of the advertisements that often appear on television media, especially during new school academic year or new semester. This study examines advertisements on television media less than 30 seconds to attract customers. This study uses semiotic to analyze television ads for the product. The research method uses an interpretive descriptive approach to analyze the signs used in advertisements. The data resource is four video recording advertisements on television media. The analysis starts with transcription and making video screenshots to sharpen the analysis. The results indicate that to attract television viewers, RuangGuru uses signs that indicate superior features. The features such as animated learning videos, practice questions, discussion, and ease of learning through applications. The celebrity figures in the advertisement represent students and parents who will later become consumers of RuangGuru. The ad also has a sign indicating how easy it is to pay and how cheap it is to subscribe to the Ruang Guru application compared to conventional tutoring. The blue color, which is the dominance of advertisements, shows that the RuangGuru app is trustworthy and can increase students' confidence in facing classroom learning.
Training of literacy-oriented teaching material development in MTs Al Ikhlas Soe, East Nusa Tenggara

In accordance with the paradigm of 21st century education, teachers are required to facilitate students with the learning process which addresses the improvement of students’ higher thinking skill. The community service which was conducted in MTs Al Ikhlas Soe, East Nusa Tenggara was aimed at: 1) culturing as well as developing students’ literacy in school; 2) improving students’ capacity in literacy; 3) managing students’ knowledge and higher thinking skill, as considerable as 4) elevating teachers’ professionalism so that learning process can be more effective and efficient. The methods used were lecture, discussion, brainstorming, simulation, and assignment. The materials delivered were about the arrangement and development of literacy-oriented teaching materials. The product produced from the training was implemented in learning process on VII graders. This was aimed to observe whether there was an improvement gained after the training given. The participants of this community service were 13 teachers and 34 students of MTs Al Ikhlas Soe. The results showed that teachers’ skill in arranging teaching materials has been improved. Furthermore, students’ understanding about the concept or their knowledge related to the materials has been upgraded after experiencing the implementation of literacy-oriented learning.
A Deep Learning Network for Coarse-to-Fine Deformable Medical Image Registration

Deformable image registration was a fundamental task in medical image analysis. Recently published registration methods based on deep learning have shown promising results. However, these algorithms brought limited precision improvement due to the similar learning framework. In order to address this shortcoming, we proposed a novel two-stage framework for deep learning based image registration. The new network computed deformation fields on different scales, similar to methods using auto-context strategy. Thereby, a coarse-scale alignment was obtained by the first half part of our network, which was subsequently improved on finer scale by the second half part. The new model could directly estimate the final deformation field in an end to end way. We demonstrated our method on the task of brain magnetic resonance (MR) image registration and showed that the new model could reach significantly better registration results.
Finite Groups Which Have 20 Elements of Maximal Order

The structure of finite groups is widely used in various fields and has a great influence on various disciplines. The object of this article is to classify these groups <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>G</mml:mi></mml:math> whose number of elements of maximal order of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>G</mml:mi></mml:math> is 20.
Enhancing software development cost control by forecasting the cost of rework: preliminary study

&lt;p&gt;&lt;span&gt;Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product functional complexity and the team competency. Finally, we evaluated the proposed model using a dataset drawn from five actual projects. The results showed a significant enhancement in forecasting accuracy.&lt;/span&gt;&lt;/p&gt;
Analisa Hujan Limpasan Di Sub Das Gongseng Bojonegoro Menggunakan Jaringan Saraf Tiruan

Discharge data or surface runoff data in a watershed need to be known to analyze water availability in the watershed. However, not all watersheds have measurements. Therefore, it needs analysis to transform rainfall data into discharge. This study aims to transform rainfall data into discharge using the Artificial Neural Network (ANN) method. The ANN model uses the Matlab R2014b application program. The research location is in the Gongseng Sub-watershed in Bojonegoro Regency, East Java. The data used are the number of rainy days, rainfall, runoff coefficients, and discharge data (as calibration). The data used are 12 years (2006-2017). Analysis was carried out on three (3) processes, namely calibration, verification and validation. The calculation results of the best calibration process when using 6 years of data (2006-2011) with epoch 2000 that produces an NSE value of 0.69 and an R value of 0.85. As for the verification and validation process when using 1 year of data (2017) with epoch 1000, it produces an NSE value of 0.79 (good) and an R value of 0.92 (a very strong relationship). From these results it was concluded that this method is appropriate to be applied at this research location, and also applied to other locations that have similarity condition with this watershed characteristics.
PSO Swarm intelligence technique to optimized ANN for demand forcasting

With the expansion and development of artificial intelligence algorithms and the use of swarm intelligence and artificial neural networks extensively in solving many complex problems recently.In this research, one of the swarm intelligence algorithms was used, which is represented by the algorithm of the Particle swarm optimization (PSO), and then combined it with one of the algorithms of artificial neural networks, that represented by the algorithm of error back propagation neural network EBPNN, in order to solve the problem of forecasting the demand.And the data that was used in this research was prepared by the general company for prepared clothes and the northern general company for cement represented by the weekly demand data for cement and towels.And the method of the combined PSO with EBPNN obtained the better performance than the standard back propagation neural network algorithm.
Application Possibilities of Decentralization and Blockchain Technology Using Computer Vision and Artificial Intelligence in Defense Management, Military and Police Organizations

Military science has faced new challenges at the end of the 20th century with the emergence of the Internet. Challenges and threats to traditional security got a new interpretation with a new concept of cybersecurity, which led to an organic transformation of military engineering and IT. As the Internet has fundamentally changed the way the world works, new technologies have emerged on the network that can revolutionize the multitude of industries. Such innovation is Distributed Ledger Technology (DLT) and Blockchain Technology supplemented with Artificial Intelligence and machine vision. The potential uses of the block chain represent a multitude of military technical scientific challenges. The technology makes it possible to co-operate freely with cryptographic procedures on distributed networks without state control, but it can also serve military and defense management purposes.
Distributed Association Rules Mining of Varying Data Partition Size Using Nodesets

Association rule mining in the distributed database has become an important area of research, where frequent pattern or itemsets are found in a large distributed data of varied sizes stored at multiple sites.In recent years, new efficient data structures have been proposed for the data mining.A new algorithm named as QDFIN(Quick distributed frequent itemset mining using nodeset) is proposed in this paper which uses the efficient nodeset data structureto store the candidate itemsets locally at each site and zero-first technique to balance the load and pruning to reduce the candidate sets.The algorithm is implemented and the speed performance is compared with PFIN and FDM using FP-Growth algorithms.Results shows that the proposed algorithm not only outperform other algorithms on varying size data partition but also on uniform distributed data on 4, 5 and 6 node setups.
Generating Quantum Random Numbers on a CubeSat (SpooQy-1)

We demonstrate a quantum random number generator based on entangled photon-pair statistics on-board a CubeSat orbiting in Low Earth Orbit.
Importance Of Paralinguistic Features In Communication And Their Theoretical Study

Noverbal communication plays an important role in the communication process, and it is in the process of conversation plays an important role in order to increase the effectiveness of speech. Psychology, cultural studies, and sociology are also important in the study of this type of communication. After all, when approaching an event, people approach it primarily on the basis of their worldview, position in society and the environment in which they live, which indicates that the above sciences are inextricably linked with linguistics.
3D-Playfair Encrypted Message Verification Technology based on MD5

In the world of information development, the transmission of information is much more convenient. However, the transmission process always faces the risk of being attacked, stolen and tampered, which leads to the doubt that the data source is incorrect. For this reason, some scholars proposed to protect important information in the form of passwords. Alok et al. Proposed 3D-Playfair Cipher with Message Integrity using MD5. This paper uses 3D-Playfair encryption for encryption. However, simple 3D-playfair encryption cannot guarantee the integrity of data during transmission, so the author proposes Combined with MD5 to ensure the integrity of the data, but there are doubts about the credibility of the data source, so this paper uses XOR calculation methods to further verify the credibility of the data. When a man-in-the-middle attack is encountered, the attacker intercepts the packet And tampering with the data content can still accurately determine whether the source of the data is the original sender. This method guarantees the integrity of the data while improving the credibility of the data.
Cloud Computing Authentication Attack and Mitigation Survey

Securing the information and customer privacy are very important during the attacks are increased, and the biggest breach happened in the previous year's especially in cloud computing.In cloud computing, we need to use a secure authentication and authorization mechanism to access our data and other services, to ensure the CIA (confidentiality, integrity, and availability).During this survey, we will focus on the authentications attack and how we can have secure access to our environments by reducing the risk of the data breach and mitigate the authentication attacks.
Sustainable QKD transmissions over fiber infrastructures shared with data and real-time transfers

In this contribution we present reasons for sharing the fibres by both Quantum key distribution and telecommunication traffic, we describe possible sources of noise, discuss availability of photodetectors. Based on these facts we propose suitable wavelength band and verify injection of such Quantum key distribution signal into telecommunication fibre with no impact to transmission system performance.
Multiple-Embedded-System Optimization Layout for Electromagnetic Wave Power Density in Complex Environments

Many embedded systems are implemented for healthcare, and smart homes and spaces. These devices are generally designed for elderly care, for monitoring, surveillance, and collection information. As embedded systems are ubiquitous and pervasive in a smart home, office, or space, different layout affects not only reduce the implementation cost but also the power density of electromagnetic waves. This study aimed to develop a multiple-embedded-system optimization layout to consume less electromagnetic wave power density and gain better communication strength. For smart offices, we analyzed the layout topology of n-shaped and n-shaped with door layout categories. On the basis of the location of each embedded system in a communication center via an n-shaped layout, we investigated the electromagnetic wave effect to the local, direct, and semidirect effects. Indirect and subindirect effects were also studied in the n-shaped layout with a door. In addition, we derived a set of formulas from the scope for the diverse effects to help users to quickly identify the scope of each effect. To verify the multiple-embedded-system optimization layout, 16 cooperating embedded systems with four test cases in a smart office were used to evaluate the diverse effects of electromagnetic wave power density and communication strength. Experiment results showed that the optimization layout consumed 3950 × 10−6 W/m2 electromagnetic wave power density.
Privacy-Friendly Monero Transaction Signing on a Hardware Wallet

Keeping cryptocurrency spending keys safe and being able to use them when signing a transaction is a well-known problem, addressed by hardware wallets. Our work focuses on a transaction signing process for privacy-centric cryptocurrency Monero, in the hardware wallets. We designed, implemented, and analyzed a privacy-preserving transaction signing protocol that runs on a hardware wallet and protects the spending keys. Moreover, we also implemented a privacy-preserving multi-party version of the Bulletproof zero-knowledge prover algorithm, which runs on a hardware wallet with constant memory. We present the protocols and evaluate their performance on a real hardware wallet.
UM CAMINHO DO MEIO ENTRE TRADIÇÃO E RADICALISMO EM COGNIÇÃO

While the cognitivist theory of the mind presents the brain as the exclusive locus of cognition, working isolated manipulating its representations, in the descriptions of radical enactivism the roles of brain, body and environment get confused in the total dynamics that characterise the embodied embedded behavior. Putting the phenomenon of learning in focus, our middle way approach finds an equilibrium between the two opposing positions: in an interactive process, where the body and the elements of the environment are in direct contact with each other, the brain encounters opportunities to give form to a behavior that is capable to reach the goal. We analyze and develop this idea with the help of an análogo between the intelligent behavior and the operation of a machine, and between learning and the construction of the machine. The first result we obtain is the identification of fundamental gaps in the cognitivist and enactivist explanations. The most interesting result, however, consists in the observation that the cognivist and enactivist projects are working on the same problem: the construction and operation of cognitive machines. Not only that, but that which missing on each of them seems to be offered by the other — which allows us to portray the two in continuity.