Reviewerly/exHarmony huggingface dataset:
Machine Learning based Secured Data Transmissionfor Banking Application

Security in the Internet of Things emphasizes on securing the Internet-enabled gadgets that link to wireless networks.IoT Safety attempts to safeguard IoT devices and systems against cybercrime and it is considered to be avital security element linked to the Internet of Things.Conversely, banking applications are progressively being supervised for their failure to provide a sufficient degree of customer support and to protect themselves against and respond to cyber-attacks. One of the main factors for this is the vulnerability of fintech systems and networks to malfunctioning. Therefore, wireless networks covering these IoT products are extremely unprotected.IoT is a lightweight system and it is optimal when using lightweight and energy-efficient cryptography for protection. Deep learning is an efficient technique to analyze threats and respond to attacks and security incidents. So this work addresses both security and energy efficiency in IoT using two novel techniques carried out through deep learning. This work contributes to the most innovative way of saving energy in IoT devices through decreasing the use of energy-expensive ‘1’ values in the interface of Dynamic RAM. This can be done by using Base + XOR encoding of data during data transmission. Also, the security of data is incorporated using chaotic XOR encryption (CXE) algorithm which is proved to perform faster and stronger encryption using XOR operation. Using Conditional Generative Adversarial Network (CGAN) based deep learning technique, the Base + XOR encoding technique and CXE are trained well in the banking application. The data generation in CGAN is carried out based on criteria produced using generator model. This work is proved to be consuming less energy, less data transmission time, and provides more security when compared to the existing systems.
Exploring the Privacy Trade-Offs and Industry Impacts of DNS Over HTTPS

The arrival of DNS over HTTPS (DoH), which adds confidentiality to DNS queries, has created a major stir in the Internet community. Policy debate has generally taken the form of debates about its technical functionality, centralization, and potential privacy benefits. But most policy questions are really about the impact of new technical standards on the industrial organization of Internet services. The key to policy responses, in our view, hinges on understanding how the technical functioning of DoH affects the market for DNS and related Internet services. In this paper, we analyze the market for DNS query services and data, and explain how it relates to broader struggles over market power and competition in the Internet-based information services economy. We find an overall market greater than $200 billion, with several identifiable sectors and competitors that users and enterprises interact with to shape the supply and demand for DNS query data. Our work highlights how the seemingly technical and obscure DoH controversy is relevant to broader issues in platform regulation, privacy/data protection law and regulation, cybersecurity and content regulation.
USER EXPERIENCE PEMAIN TERHADAP ASPEK REALISME DALAM GIM DIGITAL 3D BERGENRE ROLE-PLAY

Abstract Player’s User Experience to Realism Aspects in 3D Digital Games in Role-Play Genre. The development of digital games , especially 3D has increased dramatically over the past three decades. Technology and information have a very important role in improving the quality of 3D graphics. 3D digital games provide a different experience when compared to 2D games , which in reality everyday people like seeing a 3D display (perspective). Thus the 3D game seems to display fake reality in the minds of the players. This study will examine the experience of players in the aspect of realism in the role-play genre game from the perspective of First Person or Third Person. The method used is a qualitative descriptive method with an approach based on the player's experience of the game with the 3D role-play genre, especially on aspects of reality as graphics that appear in the game . The results of this study are expected to be able to find out the unique experience gained by players, so that later it will become a reference and consideration in making games and simulations to communicate and preserve local culture to today's generation. Keywords : 3D game , first-third person, role-play, user e xperience Abstrak User Experience Pemain Terhadap Aspek Realisme Dalam Gim Digital 3D Bergenre Role-Play . Perkembangan gim digital, khususnya 3D mengalami peningkatan yang sangat drastis selama lebih dari tiga dekade terakhir. Teknologi dan informasi memiliki peranan yang sangat penting dalam peningkatan kualitas grafik 3D. Gim digital 3D memberikan pengalaman yang berbeda apabila dibandingkan dengan gim 2D, yang secara realitas sehari-hari manusia seperti melihat tampilan 3D (perspektif). Dengan demikian gim 3D seakan-akan menampilkan realitas semu pada pikiran pemainnya. Penelitian ini akan mengkaji mengenai pengalaman pemain dalam aspek realisme pada gim bergenre role-play (bermain peran) dengan sudut pandang First Person atau Third Person . Metode yang digunakan adalah metode deskriptif kualitatif dengan pendekatan berdasarkan pengalaman pemain ( player ) terhadap gim dengan genre role-play 3D khususnya pada aspek realitas sebagai grafis yang muncul dalam gim tersebut. Hasil dari penelitian ini diharapkan dapat mengetahui pengalaman unik yang diperoleh pemain, sehingga nantinya menjadi referensi dan pertimbangan dalam pembuatan gim maupun simulasi untuk mengomunikasikan serta melestarikan kebudayaan lokal kepada generasi masa kini. Kata kunci: gim 3D , first-third person, bermain peran , user experience
시론 : 4차 산업시대의 의료 정보 관리: 우리는 무엇을 준비해야 하는가?

The power of the Internet, mobile devices, and data science including artificial intelligence is making a big difference in our daily lives. The introduction and proliferation of electronic health records, personal health records, and artificial intelligence in the medical field raises expectations that the future of medical practice will change. Both individuals and medical institutions will receive or perform multiple medical services based on much larger and more complete health and medical datasets. Medicine is also expected to increasingly make use of real world evidence (RWE), based on data supplied in the form of real world data (RWD), that will be applied in the clinical setting in real time. Further, the learning healthcare system will complete the data-driven medicine that will be used to deliver future medical care. For these reasons, doctors need to be more data-friendly and respond to these changes.
The Differences in Academic Achievement Between Bidikmisi and Non-Bidikmisi Students of Midwifery Bachelor Degree Students at Faculty of Medicine Universitas Andalas

Indonesian Midwifery Research, that primarily focuses on midwives, midwifery practices and education, is trying to make a tremendous effort to reveal the consequences of industrial development which influences midwifery practice and midwifery educational. The 1st Annual Conference of Midwifery has proven its commitment to promote the spirit of the scientist which provides insight, innovation, and creativity to resolve the problems in the midwifery field which occurred in this industrial development era. Hundreds of scientists, professionals, and students have gathered to discuss, analyse, and express their opinions about the current issues today. We are proud because we can bridge their efforts in a prestigious scientific gathering in Indonesia at The 1st ACM 2019. Aims & Scope This event will bring together midwifery experts, midwifery researcher, midwifery lecturer, and students worldwide. We expect it to be a great opportunity and an inspiring occasion for research development learning, especially to the disseminating of new findings in midwifery and to bridge the networking of midwifery professional, midwifery researcher and midwifery educator. Scopes: Pregnancy Childbirth Postpartum New born care Women's health Reproductive health Techniques of teaching and learning Literacy and skill development
LIBRARY AS AN ASPECT OF SUPPORTING OF LITERATURE LEARNING

The research aims to describe the role of the school library as a major supporter of literary learning in character formation. The object of research is focused on the role of libraries in supporting literary learning. The scope of research focuses the role of libraries in supporting character education. The essence of the library as a component of the education system supports learning with information services. Analyzing required critical thinking realistically apply the conceptual using the historical approach. Primary information sources are considered primary data.Collecting data utilizing library research, reading various literature subjected to the role of libraries, learning literature to form characters. Qualitative descriptive analysis by analyzing various collections of theoretical, research and non-research libraries. Valid analysis uses content analysis, to analyze the meaning of the role of library concepts, literary learning, character building, through the empowerment of library services and the application of literary learning activities. Observation is used to support the construction of theory. All successes in literary education in the formation of character in industrial times 4.0, the essence always depends on the role of the library.
Quantum Legendre-Fenchel transform

We present a quantum algorithm to compute the discrete Legendre-Fenchel transform. Given access to a convex function evaluated at $N$ points, the algorithm outputs a quantum-mechanical representation of its corresponding discrete Legendre-Fenchel transform evaluated at $K$ points in the transformed space. For a fixed regular discretization of the dual space the expected running time scales as $O(\sqrt{\kappa}\,\mathrm{polylog}(N,K))$, where $\kappa$ is the condition number of the function. If the discretization of the dual space is chosen adaptively with $K$ equal to $N$, the running time reduces to $O(\mathrm{polylog}(N))$. We explain how to extend the presented algorithm to the multivariate setting and prove lower bounds for the query complexity, showing that our quantum algorithm is optimal up to polylogarithmic factors. For multivariate functions with $\kappa=1$, the quantum algorithm computes a quantum-mechanical representation of the Legendre-Fenchel transform at $K$ points exponentially faster than any classical algorithm can compute it at a single point.
Automatic incremental recomposition algorithm for QoS-aware internet of things service composition

In the near future, the internet-of-things (IoT) technology will improve dramatically our daily life as a new pervasive computing paradigm. For the IoT computing, various devices and wireless networks are the hardware infrastructure, and service-oriented architecture (SOA) is a valuable software system that allows heterogeneous devices to interoperate each other. Even though IoT researchers have tackled a number of challenges for service composition, the orchestration techniques on IoT are rarely studied yet. Given a set of IoT services and a goal, the QoS-aware IoT service composition problem constructs a composite IoT service with the optimal accumulated QoS value, which satisfies the given goal specification. However, in the IoT environment, frequent changes happen inherently - for instance, temporary machine down, heavy system workload, and network failure. If the solution we have constructed is not valid anymore due to the changes, we should solve a new problem again. In this paper, we propose a novel incremental recomposition algorithm, which does not solve the new composition problem from scratch but explores only the changed space. In the experiment, our incremental recomposition algorithm can deal with the composition problem much faster than the original algorithm to solve from scratch.
Sparse Recovery for Orthogonal Polynomial Transforms.

In this paper we consider the following sparse recovery problem. We have query access to a vector $\vx \in \R^N$ such that $\vhx = \vF \vx$ is $k$-sparse (or nearly $k$-sparse) for some orthogonal transform $\vF$. The goal is to output an approximation (in an $\ell_2$ sense) to $\vhx$ in sublinear time. This problem has been well-studied in the special case that $\vF$ is the Discrete Fourier Transform (DFT), and a long line of work has resulted in sparse Fast Fourier Transforms that run in time $O(k \cdot \mathrm{polylog} N)$. However, for transforms $\vF$ other than the DFT (or closely related transforms like the Discrete Cosine Transform), the question is much less settled. In this paper we give sublinear-time algorithms---running in time $\poly(k \log(N))$---for solving the sparse recovery problem for orthogonal transforms $\vF$ that arise from orthogonal polynomials. More precisely, our algorithm works for any $\vF$ that is an orthogonal polynomial transform derived from Jacobi polynomials. The Jacobi polynomials are a large class of classical orthogonal polynomials (and include Chebyshev and Legendre polynomials as special cases), and show up extensively in applications like numerical analysis and signal processing. One caveat of our work is that we require an assumption on the sparsity structure of the sparse vector, although we note that vectors with random support have this property with high probability. Our approach is to give a very general reduction from the $k$-sparse sparse recovery problem to the $1$-sparse sparse recovery problem that holds for any flat orthogonal polynomial transform; then we solve this one-sparse recovery problem for transforms derived from Jacobi polynomials.
Inferring latent heterogeneity using many feature variables supervised by survival outcome

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high‐risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.
Automated Real Time Detection of Suspicious Appearances Using Deep Learning

Security camera systems especially in public areas such as airports, courthouses or sports facilities etc. are used to find fugitive persons or detect suspicious behaviors manually under the monitoring of an operator. In hallway-like sections in public facilities, repeated appearances of an unknown ordinary person in a short span of time can be defined as suspicious behavior. However, the fact that multiple cameras are monitored by a single operator makes it harder to detect suspicious behaviors especially in crowded fields. Therefore, support decision systems are required to support operator. If individuals are detected on images automatically and their appearances on the camera are recorded on a database by giving them a temporary identity, suspicious behaviors can be reported to an operator as a support decision system. For this reason, two different methods are used together as a hybrid solution in the study; a MTCNN based facial detection is used on the real time security camera images that currently provide face images, and an identification method, created with facial landmarks produced with a deep learning algorithm that was trained with res-net, was used on the obtained person’s face images. It has been presented that suspicious behaviors can be detected by interpreting the temporary identity information that was obtained. The success of the application was experimentally tested, and the causes of success and failures in the results were discussed.
SISTEM PERHITUNGAN PENGGAJIAN PEKARYA BERBASIS WEB DI PT. PETROWELL ENERGI

PT. Petrowell Energi is a company engaged in contracting, trading, repair for industrial equipment and the provision of services for empowering human resources. In providing services, this company processes its data using a computerized but simple payroll system that uses software Ms. Excel, so that in processing data experienced obstacles that cause the process of errors such as incorrect inputting salary data, personal data and salary reports that must be calculated and the process of calculation is repeated from each employee. The purpose of this web-based payroll information system is to overcome the problems that arise in the manual system currently running at PT. Petrowell Energy as well as being a good solution for companies for the creation of a more transparent and accountable system, in its processing it will be more effective and efficient. The method of data collection is done by means of interviews, observations, documentation and analysis of systems used by the company. The results of this study are to produce a computerized payroll information system that is provided with ease in providing payroll data information such as employee data information, and payroll data. In payroll data information systems, the authors use data flow diagrams, DFD, ERD, and MySQL for data processing.
Zernike-polynomial description of the deformation of a known surface profile with a noncircularly symmetric shape

We analyze the errors in a Zernike-polynomial description of the deformation over a noncircularly symmetric portion of the overall input pupil of an optical system. Analysis reveals that Zernike polynomials can be used for such description, provided certain restrictions are observed. In this paper, we describe an example where Zernike polynomials are used to describe and assess the adverse effect of primary-mirror deformation on image quality in a three-mirror off-axis anastigmat.
Sistem Informasi Pengelompokan Pembayaran Denda Tilang Menggunakan Algoritma K-Means Clustering

This study Violation can be defined as a situation where there is a mismatch between the rules and implementation. The regulation is contained in Law Number 22 Year 2009, concerning Road Traffic and Transportation which has been stipulated by the state and is valid legally. The current problem shows, there is no information service system for ticket data management, unable to manage the process of grouping the results of the payment of fines properly, because the process is still manual by checking the data of offenders who have made payments, so it takes longer to process the grouping and data management payment results. The objectives of this study are: Knowing the success of the k-means clustering algorithm in the process of grouping the ticket data to the process of grouping the results of the payment of fines. This research uses the clustering method with the k-means algorithm which is one of the algorithms for the formation of data clusters. This algorithm works by dividing the data into k-clusters by grouping data based on certain classes which are then formulated the results by analyzing the articles that are violated and average. the total amount of the penalty payment. Research shows that information services for ticket data management using the k means clustering algorithm can simplify the process of grouping the payment of fines by more than 70%. This can be proven through the results of the hypothesis test which states that t count is smaller than t table with a value of -2.430 <2.178 so that Ha can be accepted and H0 is rejected.
공감하고 실천하는 민주시민을 기르는 역사수업 - ‘정서적 감정이입(affective empathy)’을 적용한 수업 방안 모색 -

Recently, the newly perceived historical consciousness is related to the students'' proper awareness of the historical facts of the past and their interest in the historical issues that have led to the present, and to foster the power to make the better future history on their own. Also, it is to have a citizen''s consciousness to make a right decision on a given problem and to practice it at the same time, and to cultivate such a democratic citizen''s historical consciousness may be a purpose of history education.involves students'' interest in the historical issues that have led to the present, while fully aware of the historical facts of the past, and furthermore, I will develop the power to make the better future history on their own.Meanwhile, Barton and Levstik, who emphasized democratic citizenship education in that history education should contribute to promoting common good and participatory democracy, insisted that historical empathy is a useful principle of instruction for cultivating citizenship. In particular, criticizing the emphasis on only cognitive inquiry, they suggested that affective factors such as empathy and identification should be actively combined into historical empathy. Barton and Levstik''s views have been developed by researchers at local and abroad into the discussion of the re-conceptualization to properly combine historical empathy with the cognitive inquiry processes and affective elements. It is clear that these discussions are possible logic when integrating concepts based on the studies of scholars and teaching strategies of teachers.In addition to the discussion of re-conceptualization of historical empathy and analysis of class cases using sympathy and emotion, this study specifically designed a teaching plan based on the hypothesis that historical empathy applying affective dimension can greatly contribute to the growth of students as democratic citizens, and tried to find the possibility. At the same time, through the class, this study tried finding that Barton and Levstik''s ‘affective empathy’ called ‘caring’ embodies the driving force that students lead the knowledge and consciousness of history learned in classes to the practice of action as the core of the civic competency.In conclusion, historical empathy should be conceptualized as the principle of instruction that combines both tools of cognitive inquiry and affective elements. Especially, the application of ‘affective empathy’ called ‘caring’ is a factor that exerts more aggressive sympathy among students, and it could be used as a key teaching principle of democratic civic education in that it plays a role in promoting interest in the ‘common good’ such as human rights, peace, and coexistence.