Reviewerly/exHarmony huggingface dataset:
Cloud IaaS Enterprise Architecture Design on Multiplatform Integrated Information System using Cloud Ecosystem Reference Model and TOGAF

Currently Information Technology is increasingly needed by many organizations. Implementation of inappropriate IT in the organization will be a burden, even can hinder the development if not in accordance with the vision and mission of the organization. Weill and Ross also stated, 48 percent of companies that do IT investment cannot increase the expected value. A solid foundation in the execution process of IT implementation plays an important role in determining the success or failure of the company to achieve the desired goals. To build a solid foundation on the execution process of IT implementation, one of the important things to note is Enterprise Architecture (AE). Enterprise architecture provides a long-term view of the processes, systems and technologies in the company, so there is a harmony between business and IT both for now and for the future to achieve the desired goals. Currently the use of cloud computing technology increasingly used in organizations. Organizations typically have a variety of different platform integrated applications that are used to support their business processes. There are many frameworks used to build AE, but there is no AE framework specifically addressing AE design on cloud computing and supports multiplatform and cloud-based IaaS. This paper discusses the use of TOGAF and CERM to educational institution that have many integrated multiplatform applications. AE design that was built is validated by using Enterprise Realization Score Card (EARS) to assess whether it can be implemented well in the organization.
Semi-Supervised Dimensionality Reduction by Linear Compression and Stretching

Dimensionality reduction is a fundamental and important research topic in the field of machine learning. This paper focuses on a dimensionality reduction technique that exploits semi-supervising information in the form of pairwise constraints; specifically, these constraints specify whether two instances belong to the same class or not. We propose two dual linear methods to accomplish dimensionality reduction under that setting. These two methods overcome the difficulty of maximizing between-class difference and minimizing within-class difference at the same time, by transforming the original data into a new space in such a way that the bi-objective problem is (almost) equivalently reduced to a single objective problem. Empirical evaluations on a broad range of public datasets show that the two proposed methods are superior to several existing methods for semi-supervised dimensionality reduction.
Defending Organizational Assets: A Preliminary Framework for Cybersecurity Success and Knowledge Alignment

Cybersecurity governance is a critical issue for organizations engaged in a constant struggle to protect their data, brand, customers, and other assets from malignant actors.The nature of what constitutes successful cybersecurity practices and governance, however, is not yet clear, in part because an appropriate measure for cybersecurity success is not likely to be singular or simple.In this qualitative study, we explore perspectives of cybersecurity success through interviews representing various technical and non-technical roles across a variety of organizations, then provide a preliminary framework for understanding dimensions of cybersecurity success (financial, information integrity, operational, and reputational) as well as their associated knowledge domains and alignments.
Extreme points of the set of quantum states with bounded energy

We show that for any energy observable every extreme point of the set of quantum states with bounded energy is a pure state. This allows us to write every state with bounded energy in terms of a continuous convex combination of pure states of bounded energy. Furthermore, we prove that any quantum state with finite energy can be represented as a continuous convex combination of pure states with the same energy. We discuss examples from quantum information theory.
Reasoning About Future Cyber-Attacks Through Socio-Technical Hacking Information

With the widespread of cyber-attack incidents, cybersecurity has become a major concern for organizations. The waste of time, money and resources while organizations counter irrelevant cyber threats can turn them into the next victim of malicious hackers. In addition, the online hacking community has grown rapidly, making the cyber threat landscape hard to keep track of. In this work, we describe an AI tool that uses a temporal logical framework to learn rules that correlate malicious hacking activity with real-world cyber incidents, aiming to leverage these rules for predicting future cyber-attacks. The framework considers socio-personal and technical indicators of enterprise attacks, analyzing the hackers and their strategies when they are planning cyber offensives online. Our results demonstrate the viability of the proposed approach, which outperforms baseline systems by an average F1 score increase of 138%, 71% and 17% for intervals of 1, 2 and 3 days respectively, providing security teams mechanisms to predict and avoid cyber-attacks.
Revealing lecturer’s paralinguistic attribution: How the visual manner contributes to students’ non-cognitive skills

Classroom-based communication requires an appropriate contribution from lecturer’s perspectives to address students’ non-cognitive skills. This study examines the paralinguistic attribution contributions deriving from lecturer’s visual manner. Of 504 pre-service English teachers, 120 freshmen participated in this study. Data collection used the questionnaire through a random sampling selection from lecturer’s writing instruction. Data analysis used the multiple regression analyses with the significance level (p-value) of .05. The findings exhibited that lecturer’s paralinguistic attributions, namely: articulation (t = 1.073; p = .286), sonority (t = 2.896; p = .005), loudness (t = 3.433; p = .001), facial expression and lips setting (t = 1.097; p = .275), and gesture (t = 2.323; p = .022) contributed a significant influence towards the writing class instruction positively. The effectiveness of the paralinguistic attributions contributed 45.5% from overall findings shown in this study, in which the regression analysis statistically addressed that F = 19.017, R² = .455, and p .05. This study concludes the existence of the paralinguistic attributions accommodates freshmen’s learning maturation in lecturer’s instructional modes.
Mobile IoT adoption as antecedent to Care-Service Efficiency and Improvement: Empirical study in Healthcare-context

Internet of things (IoT) is the buzzword and pioneering breakthrough approach highlighted in today's industry 4.0, where the devices are seamlessly integrated with each other, sharing vital information in real-time sync.With the wearable IoT devices, the diagnostic readings and physical measurements of the patients can be shared with the physician on the go and suitable diagnosis can as well be shared with patient in the real-time on their own mobile devices through IoT applications.This study empirically examines the importance of m-IoT adoption on the information pervasiveness across the network and patient stakeholders and in turn investigating how efficiency of healthcare services and care process improvement got affected.Study outcomes based on 221 completed responses from the healthcare professionals concludes that, m-IoT adoption opens new opportunities and challenges with the process innovation in the healthcare services and increases the efficiency of services.
The Specificity of Critical Thinking of Russian Youth in the Context of Digitalization

The article contains the results of the all-Russian study of the parameters of critical thinking of young people, performed using the methods of a mass online survey of young users of the Russian Federation and expert interviews. The authors established the basic characteristics of the readiness of the younger generation of Russia to critically interpret the problems of the functioning of the network space. We also present the results of a multivariate analysis that establishes the relationship between the specifics of critical thinking of young people and their belonging to different types of professional culture. The key product of the research is systematised recommendations to the authorities on the creation of conditions for raising the level of critical thinking of Russian youth.
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning

We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning. Our focus is the so-called asymptotic network independence property, which is achieved whenever a distributed method executed over a network of n nodes asymptotically converges to the optimal solution at a comparable rate to a centralized method with the same computational power as the entire network. We explain this property through an example involving the training of ML models and sketch a short mathematical analysis for comparing the performance of distributed stochastic gradient descent (DSGD) with centralized stochastic gradient decent (SGD).
Generation and robustness of quantum entanglement in spin graphs

Abstract Entanglement is a crucial resource for quantum information processing, and so protocols to generate high-fidelity entangled states on various hardware platforms are in demand. While spin chains have been extensively studied to generate entanglement, graph structures also have such potential; however, only a few classes of graphs have been explored for this specific task. In this paper, we apply a particular coupling scheme involving two different coupling strengths to a graph of two interconnected $$3\times 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>×</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> square graphs such that it effectively contains three defects. We show how this structure allows generation of a Bell state whose fidelity depends on the chosen coupling ratio. We apply partitioned graph theory in order to reduce the dimension of the graph and show that, using a reduced graph or a reduced chain, we can still simulate the same protocol with identical dynamics. Finally, we investigate how fabrication errors affect the entanglement generation protocol and how the different equivalent structures are affected, finding that for some specific coupling ratios they are extremely robust.
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings

We propose and analyze a novel theoretical and algorithmic framework for structured prediction. While so far the term has referred to discrete output spaces, here we consider more general settings, such as manifolds or spaces of probability measures. We define structured prediction as a problem where the output space lacks a vectorial structure. We identify and study a large class of loss functions that implicitly defines a suitable geometry on the problem. The latter is the key to develop an algorithmic framework amenable to a sharp statistical analysis and yielding efficient computations. When dealing with output spaces with infinite cardinality, a suitable implicit formulation of the estimator is shown to be crucial.
Towards a High-Level Multi-label Classification from Complex Networks

Multi-label learning aims to solve problems in which data items can have multiple class labels assigned simultaneously, e.g., text categorization, image annotation, medical diagnosis, etc. However, as most of multi-label techniques are derived from the single-label ones, existing techniques perform the multi-label classification only based on the physical features of the data (e.g., distance, similarity or distribution), ignoring the semantic meaning of the data, such as the formation pattern. Inspired by recent advances in the use of complex networks for single-label learning, this exploratory work aims to investigate a multi-label solution able to combine existing multi-label classifiers with a high-level classifier based on complex networks measures, aiming to present a new concept of multi-label classification that, besides the physical attributes, also analyzes the topological structure of the data. Experimental results considering both artificial and real-world data sets emphasize respectively the salient features of our technique in comparison to the traditional ones and its potential to improve the predictive performance of those techniques, especially in data sets characterized by higher cardinality and density of labels, which often denote more difficult scenarios to multi-label learning.
Blockchain and Smart Contract in Future Transactions—Case Studies

Blockchain is an innovative technology that has attracted the attention of many industrial sectors with its decentralization and transparency features. It has capabilities that go beyond cryptocurrency and could be harnessed for numerous applications. This chapter aims at shedding lights on use cases that utilize Blockchain and smart contract in real-life applications such as Real Estate contracts, Secure Certificates, and intellectual property protection through digital licensing.
Analisis Big Data untuk Kota Aman

Abstract—Smart solutions are needed by the city government to overcome various city problems. One solution is smart city. To realize smart city, one of the main challenges is the solution to overcome the city's security problems. Currently cities in Indonesia do not yet know the level of security of their cities. The level of city security can be obtained by surveying various cities. But surveys require personnel, time and cost that is not small. In this study the authors propose a method by designing a model to determine the level of security of cities in Indonesia by utilizing big data through the prediction of sentiment analysis of people's perceptions of city security on Twitter. This research was conducted in 25 cities in Indonesia which are divided into 8 big cities, 9 medium cities and 8 small cities. The results of the prediction models designed in this study are generally not much different from the results of the 2019 RKCI (Indonesia Smart Cities Rating) survey in the field of security and disaster. The results of this study found that 4 cities with a maturity level of security are at the Integrative level (score 60 to 79 in GSCM Maturity Level), namely Tangerang, Kediri, Parepare and Probolinggo, while the other 21 cities are at the Scattered level (score 40 to 59). The average score for the big city category is 55.41, while the middle city score is 55.48 and the small city is 53.70. The results of performance measurement of this prediction model are for an accuracy value of 80.10% while a precision value of 81.10% and a recall value of 82.62%.
Multi-Class Classification from Noisy-Similarity-Labeled Data

A similarity label indicates whether two instances belong to the same class while a class label shows the class of the instance. Without class labels, a multi-class classifier could be learned from similarity-labeled pairwise data by meta classification learning. However, since the similarity label is less informative than the class label, it is more likely to be noisy. Deep neural networks can easily remember noisy data, leading to overfitting in classification. In this paper, we propose a method for learning from only noisy-similarity-labeled data. Specifically, to model the noise, we employ a noise transition matrix to bridge the class-posterior probability between clean and noisy data. We further estimate the transition matrix from only noisy data and build a novel learning system to learn a classifier which can assign noise-free class labels for instances. Moreover, we theoretically justify how our proposed method generalizes for learning classifiers. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art method on benchmark-simulated and real-world noisy-label datasets.
Aplikasi Penjualan Tiket Kelas Pelatihan Berbasis Mobile menggunakan Flutter

Abstract — Pincher is an online platform that is useful for assisting in finding and registering for training classes aimed at facilitating users who want to find their identity and channel their talents and inspirations creatively. This pincher application provides information and distributes training classes by selling class tickets. In its development as an online platform, Pincher still uses the website as a means of distributing training classes owned by Pincher, so this tool is less effective and efficient for users because the Pincher website is too complicated to access using a mobile phone or smartphone. Therefore, to help users access the Pincher application using their mobile phones, Pincher applications are made in a simple form that is applied to mobile applications. In addition, Pincher also does not have a feature that can make it easier for customers to determine the class to be chosen. Therefore this mobile application will be equipped with a system that can make it easier for users to choose attractive classes for customers, namely with a recommendation system.&#x0D; &#x0D; Keywords— Ticket Selling Application, Flutter, Mobile Application, Recommendation System.
PSYCHOLOGICAL CHARACTERISTICS INFLUENCING PERSONAL AUTONOMY AS A FACTOR OF PSYCHOLOGICAL WELL-BEING

The article makes an attempt to determine the psychological factors contributing to development and formation of personal autonomy as it understood in the theory of psychological well-being.Different personal characteristics influence an individual’s personal autonomy depending on its level. The self-attitude characteristics are in line with personal autonomy; this means that the higher these characteristics are, the higher personal autonomy is. In other words, if an individual believes in his/her own strengths, abilities, energy, independence, in his/her ability to control own life and be self-consistent, self-understanding, if an individual think on him/herself as a confident, independent, strong-willed and reliable person who knows that he/she has something to be respected, he/she also regard him/herself as an autonomous person to the same degree.  Low indicators of the Inner-Directed scale show that an individual tends to be dependent on people and situation. So, the desire to get rid of such tendency is the first step to real autonomy. The high levels of autonomy do not influenced by the Inner-Directed indicator, because the problem of dependence is already solved by highly autonomous person. Psychological hardiness, as an individual’s ability to overcome difficulties and turn a stressful situation into prospects for personal growth, can regarded as the base for autonomy development, but it cannot stipulate further development of personal autonomy at attainment of some level.The performed analysis show that the respondents with high autonomy show better understanding of their meanings of life, their life goal are clearer, they believe that are able to achieve life tasks and goals, which is uncharacteristic for the respondents with average and low personal autonomy. And namely this is a turning point to attain personal autonomy of the higher that average level. We can argue that existing life goals and awareness on own life path create a psychological superstructure allowing an individual to achieve high personal autonomy and experience full-fledged psychological well-being.
No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.
Pseudo-3D Residual Networks Based Anomaly Detection in Surveillance Videos

We proposed a deep multiple instance learning framework for anomaly detection in surveillance videos. We used training videos with video-level lables and trained the models with multiple instance learning (MIL) methods. We improved the Pseudo-3d ResNet network by adding a batch normalization (BN) operation after each convolution layer, and used the improved network as the feature extractor. In order to achieve better anomaly detection performance, we used the 3-layer full-connected neural network with 50% Dropout regularization as the classifier to obtain anomaly scores, which between 0-1. And the results are compared with detection results of the method with 50% DropConnect regularization. The performance of the proposed approach is evaluated on a large-scale dataset with video-level labels. Experimental results demonstrate that the proposed method in this paper further improves the accuracy of abnormal behavior detection and is more attuned to practical applications as compared to the state-of-the-art approaches.
Pengembangan Rancangan Media Pembelajaran Augmented Reality Perangkat Jaringan Komputer

All The problem of the research is the absence of supporting learning media in learning activities of computer network, are not optimal mastery of concepts by students, and abstract teaching materials that need to be made more concrete, as well as using the use of smartphones in the 4.0 revolution industry. This study aims to develop learning media augmented reality and produce media that are valid, practical, and effective on computer network subjects so that they become alternatives as learning media. This research is a research and development (R&amp;D) using the IDI model. Data analysis techniques for practicality using percentages, and effectiveness of the control class and the experimental class. In conclusion, the augmented reality learning media develop can valid, practical and effective to be used as a learning media on computer network subjects.