Reviewerly/exHarmony huggingface dataset:
Statistical model of forecasting indicators in one area of sustainable development

This study is based on the seventh goal of sustainable goals development and under the title of clean and affordable As a result of the continuing increase in the number of the world's population, the dramatic rise in energy demand, especially in the production of the renewable energy worldwide, was observed .The Arab region has the highest levels of solar radiation. Many Arab countries are still taking slow steps to develop their renewable energy capacity, which has led many Arab countries today to seek to meet this demand through renewable energy solutions and improved energy efficiency.The field of study presented in this research includes a proposal for the statistical model to analyze the prediction in the field of solar energy, which is one of the most important areas of renewable energy, by providing a comparison between the time series model and the simple regression model to predict data which has been obtained in the field of solar energy.
OBJECT DETECTION USED IN AUTONOMOUS VEHICLES(ARTIFICIAL INTELLIGENCE)

Self-governing vehicle or driverless vehicle or Self driving vehicle, in the event that we put in an easier language is a vehicle which can drive with no human controlling it from inside. It will be a progressive change in the realm of innovation. Self-ruling vehicle has numerous outside sensors associated with it. By these outside sensors it saw or sensed the earth and settled on choice in a like manner. We allude these outside sensors as Advance control Systems. Self-sufficient vehicles have different focal points over manual vehicles like less car crash, savvy dynamic just to give some examples. Thus, the inquiry is the reason driverless vehicles still not succeeded at this point. The appropriate response of the inquiry lays on dangers like cybercrime, or disappointment of the product and critically how they are going to overthrow the manual vehicles running on streets in light of the fact that the complete computerization period is as yet a lengthy, difficult experience to go through. Their principle point is to initially conquer these hindrances. We have some ongoing innovations like Anti braking system (ABS), voyage control that can be alluded as beginning time improvement for independent vehicles. In this article we are going to talk about the recognition codes which will utilize apparent conditions given by sensors as a contribution to return will give vehicle data about various sorts of items present on the streets. We solidly accept that a self-sufficient vehicle will be reality soon when they beat the boundaries of it.
Learning Multi-Modal Image Registration without Real Data

We introduce a learning strategy for contrast-invariant image registration without requiring imaging data. While classical registration methods accurately estimate the spatial correspondence between images, they solve a costly optimization problem for every image pair. Learning-based techniques are fast at test time, but can only register images with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our approach leverages a generative model for diverse label maps and images that exposes networks to a wide range of variability during training, forcing them to learn features invariant to image type (contrast). This strategy results in powerful networks trained to generalize to a broad array of real input images. We present extensive experiments, with a focus on 3D neuroimaging, showing that this strategy enables robust registration of arbitrary image contrasts without the need to retrain for new modalities. We demonstrate registration accuracy that most often surpasses the state of the art both within and across modalities, using a single model. Critically, we show that input labels from which we synthesize images need not be of actual anatomy: training on randomly generated geometric shapes also results in competitive registration performance, albeit slightly less accurate, while alleviating the dependency on real data of any kind. Our code is available at: this http URL
구강건강 관련 스마트폰 애플리케이션 분석

Objectives: This study aimed to investigate the current status of oral health applications developed for smartphones because they can be used as a new educational medium to manage and improve oral health. Methods: This study examined 60 basic oral health applications provided by Google Play Store and Apple App Store as of May 2019 and examined delivery contents, delivery methods, application types, and other information. Results: Apple included 65.4% of oral apps in the game category whereas Android included 64.3% in the education category (p>0.05). All Apple''s apps and 71.4% of Android apps were developed overseas (p 0.05). For the delivery method, game + video was 65.4% in Apple, and game and other methods (text, image, augmented reality) was 42.9% in Android (p>0.05). In the case of application type, play type was the most common with 88.5% in Apple, and 46.4% play type and 39.3% other type (text, appreciation, problem-solving types) in Android (p 0.05). The average review score was 4.30 in the education category, 4.34 in the case of brushing and care (delivery contents), 4.37 in the case of using game + video (delivery methods), and 4.57 in the case of Play + other types (application type) (p>0.05). Conclusions: The use of healthcare apps is expected to increase owing to improved lifestyles, an increase in the elderly population, cost-effectiveness, and convenience that is not affected by time and place. Effective use of oral health apps will require the participation of dental professionals in the development process to identify the exact status, expand subjects, and provide appropriate information.
Comparative Study between Hologram Technology and Augmented Reality

The great development witnessed by our current age has led to the emergence of many diverse modern technologies, one of these advanced technologies is Hologram technology and Augmented reality technology, These two technologies are somewhat similar to somewhat, as it can be said that they perform almost the same purpose, and at the same time, Hologram technology differs from augmented reality in several aspects, as the way in which the three-dimensional images are created and the properties of that images itself. This paper aims to compare and study the similarities and differences between Hologram technology and Augmented reality technology. It is a standard comparison as the comparison takes place according to a number of different aspects of both technologies. Comparing the characteristics of the two technologies showed that there is no one of them excels over the other, but according to different systems and situations, it is maybe better and more appropriate to use one of them than using the other one.
Implementasi Penyembunyian Pesan Pada Audio Dengan Metode Bit-Plane Complexity Segmentation (BPCS)

Cryptography is the science used to maintain the confidentiality of messages, by scrambling messages that are illegible. However, the results of randomization can raise suspicions that confidential communications are being carried out. Steganography can be used to overcome these problems. The trick is the message is inserted in the audio file by the Bit-Plane method. then add a message behind the file. To prevent messages from being read, the message is encrypted first with the Bit-Plane method before inserting. Application design results can be used to hide secret messages that have been encrypted with the Bit-Plane method to audio files, so as to avoid suspicion of confidential communications
Governance in peer-to-peer networks is a design problem

Peer-to-peer networks are gaining momentum, and its revolution is accelerating. Examples of peer-to-peer systems are Bitcoin and Corda, which are enabled by blockchain technology. Even the firmest supporters of blockchain, acknowledge the most significant challenge for successful adoption and growth of peer-to-peer networks is governance in a decentralized way. A lack of governance design poses a risk, as the decentralized network will not prevail in the long term which can even lead to a centrally orchestrated system. In this paper, we argue that the finding an appropriate governance structure is a design problem.
Geometric Supervision and Deep Structured Models for Image Segmentation

The task of semantic segmentation aims at understanding an image at a pixel level. Due to its applicability in many areas, such as autonomous vehicles, robotics and medical surgery assistance, semantic segmentation has become an essential task in image analysis. During the last few years a lot of progress have been made for image segmentation algorithms, mainly due to the introduction of deep learning methods, in particular the use of Convolutional Neural Networks (CNNs). CNNs are powerful for modeling complex connections between input and output data but have two drawbacks when it comes to semantic segmentation. Firstly, CNNs lack the ability to directly model dependent output structures, for instance, explicitly enforcing properties such as label smoothness and coherence. This drawback motivates the use of Conditional Random Fields (CRFs), applied as a post-processing step in semantic segmentation. Secondly, training CNNs requires large amounts of annotated data. For segmentation this amounts to dense, pixel-level, annotations that are very time-consuming to acquire. This thesis summarizes the content of five papers addressing the two aforementioned drawbacks of CNNs. The first two papers present methods on how geometric 3D models can be used to improve segmentation models. The 3D models can be created with little human labour and can be used as a supervisory signal to improve the robustness of semantic segmentation and long-term visual localization methods. The last three papers focuses on models combining CNNs and CRFs for semantic segmentation. The models consist of a CNN capable of learning complex image features coupled with a CRF capable of learning dependencies between output variables. Emphasis has been on creating models that are possible to train end-to-end, giving the CNN and the CRF a chance to learn how to interact and exploit complementary information to achieve better performance.
Segurança da informação em uma organização militar

Much has been said about information security, which is currently one ofthe areas with the greatest focus on the entire information technology value chain.It is a fact that information security has evolved to provide a safer andmore transparent environment for both users and computer network administrators,but the majority of these administrators have a specific and technical view with regard to this topic.It is increasingly necessary to have a broader view on information securitymanagement, to plan, predict and act on the impacts caused by potential threats. Forthis, it is necessary to gather a set of good practices in the configuration, administration and safe operation for the network of the Military Organizations.
Efektivitas Blended-Problem Based Learning dengan Lesson Study Terhadap Hasil Belajar

The effectiveness of learning is important to know, as an indicator that learning created has brought useful results for students or educators. The purpose of this study is to describe the effectiveness of Blended-Problem Based Learning models with Lesson Study toward cognitive learning outcomes. The effectiveness of learning toward cognitive learning outcomes was obtained through cognitive tests, interview and observation guidlines, then the results analyzed by a qualitative approach. The research data shows that the average cognitive learning outcomes of students in Class X IPA 7 of SMA Negeri 1 Karanganyar in the 2015/2016 Academic Year increased by 7,27% and students also have a good response to learning. These results indicate that the Blended-Problem Based Learning model with Lesson Study effectively improves cognitive learning outcomes, with students experiencing learning activities from the best efforts of educators in creating learning.
SecNetworkCloudSim: An Extensible Simulation Tool for Secure Distributed Mobile Applications

Fueled by the wide interest for achieving rich-storage services with the lowest possible cost, cloud computing has emerged into a highly desired service paradigm extending well beyond Virtualization technology. The next generation of mobile cloud services is now manipulated more and more sensitive data on VM-based distributed applications. Therefore, the need to secure sensitive data over mobile cloud computing is more evident than ever. However, despite the widespread release of several cloud simulators, controlling user’s access and protecting data exchanges in distributed mobile applications over the cloud is considered a major challenge. This paper introduces a new NetworkCloudSim extension named SecNetworkCloudSim, a secure mobile simulation tool which is deliberately designed to ensure the preservation of confidential access to data hosted on mobile device and distributed cloud’s servers. Through high-level mobile users’ requests, users connect to an underlying proxy which is considered an important layer in this new simulator, where users perform secure authentication access to cloud services, allocate their tasks in secure VM-based policy, manage automatically the data confidentiality among VMs and derive high efficiency and coverage rates. Most importantly, due to the secure nature of proxy, user’s distributed tasks can be executed without alterations on different underlying proxy’s security policies. We implement a scenario of follow-up healthcare distributed application using the new extension.
Reclamation Tax Calculation at Revenue Service, Regional Financial and Asset Management (Dppkad) Pariaman City

Billboard tax calculations conducted by the Revenue, Financial and Asset Management Agency (DPPKA) Kab. Dharmasraya still uses a manual system so that performance is not optimal. The research conducted aims to optimize the calculation of advertisement tax and provide fast service time for ad users and taxpayers. With this research, it is expected to be able to assist the DPPKA in calculating the advertisement tax effectively and efficiently. The research methods used are field research, library research and laboratories. So that a new system analysis is produced by using tools such as Use Case Diagrams, Sequence Diagrams, Class Diagrams, Activity Diagrams and Deployment Diagrams
Towards automated SCADA forensic investigation: challenges, opportunities and promising paradigms

Modern supervisory control and data acquisition (SCADA) networks represent a challenging domain for forensic investigators who have the responsibility to determine the main causes of the catastrophic incidents that could happen in SCADA systems and to provide precise and logical evidences supported with comprehensive technical reports to the legal organisations. They are characterised to be complex, large-scale, and highly distributed systems comprising diversities of proprietary components such as field devices, embedded control systems, computers, communication networks, etc. Providing forensic investigators with fully or partially automated forensic investigation can be an effective solution against the challenging nature of modern SCADA networks. This review paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological paradigms that can be considered as promising in the realisation of such a framework. Finally, this paper proposes a conceptual framework for automated forensic investigation in modern SCADA networks accompanied with a possible realisation architecture based on the multi-agent systems and wireless sensor networks promising technological paradigms.
Cyber-Crimes and -Laws

The number one risk identified by CEOs/senior executives is the menace of cyber-attacks. The danger is not about identifying where and when but how big the loss would be. Legal mechanisms are not sufficient as deterrents. Bringing to book the perpetrators through IT acts is becoming difficult. However, the regulators and companies are collaborating nationally and internationally to curb the menace. Perhaps, one will have to live with it and attempt to minimize the number of incidents as much as possible. A civil society can go thus far and no further.
Exploratory Data Analysis: Connecting X-Ray Diffraction and Lithogeochemical Data

The Vazante Group is a district in east-central Brazil that consists of a carbonate-dominated marine platform sequence of Late Mesoproterozoic age. The district is home to a north-south belt that contains several zinc mines, but the petrogenesis of the ore body is not yet fully understood. Samples from this district were analyzed using both X-Ray Diffraction (XRD) and lithogeochemical assay techniques by Dr. Neil Fernandes in 2016. An analysis was conducted in order to explore the statistical correlations between the XRD and lithogeochemical test results. The purpose of the analysis was to determine whether the raw (uninterpreted) XRD data alone could be used to identify the samples enriched in zinc and other elements indicative of economic mineralization. The results showed very subtle trends that were not significant enough to make conclusions about the possibility of using XRD data without accompanying lithogeochemical data. The higher-than-average correlation of the intensity of pyrite peaks in the XRD data with elements associated with mineralization suggests that there are potentially more robust and significant trends that were not fully uncovered by this analysis, as pyrite has already been associated with mineralized zones. The analysis process itself could be valuable in future projects, and future work on this technique is proposed that uses machine learning to cluster the data and detect trends that may not be obvious using conventional techniques.
Self Modification, Instruction Buffer and Nondeterministic ISA

In most situations self modification of code is considered really bad programming style, but in system programming it cannot be avoided altogether. After a page fault on fetch a new page has to be swapped into memory by the operating system and subsequently instructions are fetched from this page by the user program.
DEVELOPMENT OF МUSICAL THINKING IN YOUNGER STUDENTS IN THE PROCESS OF LEARNING IN CHILDREN’S ART SCHOOLS

Introduction. The article highlights the problem of development of musical thinking, conditioned by the needs of practice and priority directions of the state education policy. Purpose of the article: to characterize the complex of teaching methods that ensure the unity of perception of music, thinking and imagination. Methodology and research mеthods: theoretical aspects of cultural-historical psychology about the importance of activity in the development of higher mental functions; the main provisions of musicology, psychology of musical activity on the reflection in the consciousness of the musical image; intonation and polyartistic approaches in pedagogy of music education. Меthods of research: study and analysis of literature, generalization of pedagogical experience, comparison, generalization. Results: musical thinking from the standpoint of musicological and pedagogical approaches is considered; identified the complex of teaching methods to ensure the unity of music perception, thinking and imagination: in the preporation stage (the methods of activation of informative interest, conversation, dialogue); in the effective and practical stage (the methods of empathy, reflection, conversation, dialogue, observation of music); in the final stage (analysis, reflection on music, plastic modeling); in the reflexive stage (comparison, analogy, method of imagination and creation of the artistic context). Scientific novelty: the method of teaching the discipline “Listening to music”, providing a unity of perception, thinking and imagination, for primary school students of children’s art schools developed. The method includes the steps of: 1) preparation stage, motivating students on the perception of music; 2) effective and practical stage, associated with the process of “impression-experience” of music; 3) the final stage, which consists in understanding the means of musical expression and General author idea; 4) reflexive stage, revealing the personal significance (meaning) of the work. Practical significance: the complex of methods providing unity of perception, thinking and imagination is revealed; the received data can be used in art education, in the course of musical psychology for students of secondary and higher educational institutions.
Penerapan Metode Simple Multi Attribut Rating Technique Dalam Merekomendasikan Lulur Terbaik Berdasarkan Minat Konsumen

The purpose of this study is to recommend the best scrubs based on consumer interest with criteria such as skin type, quality, price and product size. This study uses a Decision Support System (SPK) also uses questionnaires as material to find data and share it with 150 respondents. The calculation process that the author made uses the Simple Multi Attribute Rating Technique (SMART) method. The results of this study explain that the type of scrub Shinzui brand with a value of 0.91 becomes the best scrub according to consumer interest followed by Purbasari with a value of 0.90, Herborist with a value of 0.83, Martha Tilaar with a value of 0.71 and then Vienna with a value of 0,59.
A semi-supervised approach for network traffic labeling

As discussed in the previous two chapters, the recent promising studies for network classification have relied on the analysis of the statistics of traffic flows and the use of machine learning (ML) methods. However, due to the high cost of manual labeling, it is hard to obtain sufficient, reliable, and up-to-date labeled data for effective IP traffic classification. This chapter discusses a novel semi-supervised approach, called SemTra, which automatically alleviates the shortage of labeled flows for ML by exploiting the advantages of both supervised and unsupervised models. In particular, SemTra involves the followings: (i) generating multi-view representations of the original data based on dimensionality reduction methods to have strong discrimination ability; (ii) incorporating the generated representations into the ensemble clustering model to provide a combined clustering output with better quality and stability; (iii) adapting the concept of self-training to iteratively utilize the few labeled data along with unlabeled within local and global viewpoints; and (iv) obtaining the final class decision by combining the decisions of mapping strategy of clusters, the local self-training and global self-training approaches. Extensive experiments were carried out to compare the effectiveness of SemTra over representative semi-supervised methods using 16 network traffic datasets. The results clearly show that SemTra is able to yield noticeable improvement in accuracy (as high as 94.96%) and stability (as high as 95.04%) in the labeling process.