Reviewerly/exHarmony huggingface dataset:
A Divide-and-Cooperate Machine Learning Model-Based RBF With Its VC Dimension Analysis

In this paper, a Divide-and-Cooperate Machine Learning Model (DCML) based Radial Basis Function Network (RBF) is constructed. This DCML is composed of several sub-RBF networks that take some variables as their inputs. The output of DCML is the sum of sub-RBF networks' outputs. The analysis of VC dimension of DCML shows in theory that its structural complexity is less than conventional Extended Radial Basis Function Network (ENRBF). The experimental results have verified that the DCML outperforms conventional ENRBF.
Developing an IT Course for Emerging Technologies Using a Framework – An Example of an IoT Course V1.0

Aim/Purpose: Academics are often requested to create and teach courses for emerging technologies with perhaps no experience or guidance on how to do so. Background: A Framework to develop IT courses for emerging technologies was created and tested to assist academics; the framework was then tested by developing an IoT course. Methodology: A literature review was conducted to discover theories, models and methods that could be used in the creation of IT courses, followed by interviews with academics who had created many courses. The interviews were analysed using a thematic analysis process, and a Course Development Framework was created. The framework was tested by using it to build and deliver an IoT course. Contribution: The Framework could be used to support academics who have to create and develop IT courses for emerging technologies. Findings: By combining a learning theory such as constructivism, the ADDIE Instructional design model, ARCS-V Motivational model, and Bloom’s Taxonomy, a Course Development Framework was constructed, which could be used to support academics who have to create courses for emerging technologies. Recommendations for Practitioners: The Course Development Framework could be used to develop other IT courses including online courses. Recommendation for Researchers: Future research could be conducted in the effectiveness of using the Course Development Framework to develop other courses including online courses. Impact on Society: Support academics to develop better IT courses for emerging technologies. Future Research: Research in the field of Brain Compatible Learning Principles and combining or using it with the Framework could provide further insights into advancements in course design and development.
ML-Supported Identification and Prioritization of Threats in the OVVL Threat Modelling Tool

Threat Modelling is an accepted technique to identify general threats as early as possible in the software development lifecycle. Previous work of ours did present an open-source framework and web-based tool (OVVL) for automating threat analysis on software architectures using STRIDE. However, one open problem is that available threat catalogues are either too general or proprietary with respect to a certain domain (e.g. .Net). Another problem is that a threat analyst should not only be presented (repeatedly) with a list of all possible threats, but already with some automated support for prioritizing these. This paper presents an approach to dynamically generate individual threat catalogues on basis of the established CWE as well as related CVE databases. Roughly 60% of this threat catalogue generation can be done by identifying and matching certain key values. To map the remaining 40% of our data (~50.000 CVE entries) we train a text classification model by using the already mapped 60% of our dataset to perform a supervised machine-learning based text classification. The generated entire dataset allows us to identify possible threats for each individual architectural element and automatically provide an initial prioritization. Our dataset as well as a supporting Jupyter notebook are openly available.
PENINGKATAN KEMAMPUAN MENULIS PARAGRAF DALAM MENEMUKAN IDE POKOK DENGAN MENERAPKAN METODE INKUIRI SISWA KELAS IV SDN 2 LAEMANTA

This study aims to describe the application of inquiry methods to improve the ability to write paragraphs of class IV of Laemanta Elementary School 2. In the study carried out two cycles, namely cycle I and cycle II with a target of a minimum average value of 75. The research design model is Kemmis and MC. Tagart, research is carried out through a process of planning, action, observation, and reflection. From the results of the increase in writing paragraphs using the Cycle I inquiry method it is known that the number of class averages of 66.50 and completeness of 30% which are categorized quite good, meanwhile, the average value in cycle II is 84% and completeness 85% is included in very good category. So with that, the results obtained have met the classical completeness criteria as specified in the research indicators of at least 75%.
Application of Tiered Technology in the Development of Computer Software

Abstract Software reliability is one of the critical factors to be considered in the process of system design, research and operation. Different from most current studies of software reliability modeling and simulation that are limited to simple series-parallel models, the optimization method is applied to the reliability modeling and simulation of large-scale, complex software system combined with an agrometeorological application system. The application of tiered modeling technology in system development is introduced, and the related tiered technology knowledge is elaborated.
Analysing Communication Strategies of Youtube Video by Students of English Department in Unimus

This research aims at explaining communication strategies (CS) used by the 4 th semester students of English education in University of Muhammadiyah Semarang. Data gained from the transcription of YouTube video in each channel owned by students. This descriptive qualitative study observed the frequency of communication strategies employed by students by analyzing their utterance used Tarone’s typologies of communication strategies. Based on the analysis, it can be seen that the use of communication strategies in speaking class were dominated by Topic Avoidance (28%), followed by Message Abandonment (24%), and Approximation (20%). After that, it is followed by Mime (16%), and Literal Translation (12%). It can also be said that the 4 th semester students of English department of University of Muhammadiyah Semarang employed 25 communication strategies in their YouTube video.
[Application of Python web crawler technology in infodemiology].

Python web crawler technology, which automatically and massively getting information from the Internet by mimicking net users' browsing behavior, is a basic supporting technique to extract and integrate multi-source heterogeneous data in the field of Infodemiology. There are two types of Python web crawler: simple and massive-scale, both collect information simultaneously from the database establishment. Advantages of this technique are characterized as: being simple syntax, in high flexibility and low cost in learning and maintenance. Contents of the current application scenarios include surveillance, implementation and evaluation of health intervention programs on public health issues, as well as on smart doctor seeking. For the last two years, the Chinese government started to encourage the integration and utilization of multi-source heterogeneous data including internet information. Hence, the number of application scenarios for Python web crawler technology are bound to increase in the foreseeable future. Corresponding matched talent cultivations and technical innovations are suggested to add to the current education and research systems on public health issues.Python网络爬虫技术是一种通过模拟用户的网络浏览行为以实现从网络中自动、大量提取信息的技术，是信息流行病学研究收集并整合多源异构信息数据的关键基础。Python网络爬虫可分为简单爬虫与大型爬虫，集数据采集与数据库构建于一体，语法简洁、灵活性高、学习成本低、维护成本低。它适用于信息流行病学的各种应用场景，通过对互联网中健康相关信息的分析，实现多种公共卫生监测、健康干预实施及效果评价、智慧寻医方略优化等目标。近年，我国政府开始鼓励对含互联网信息在内的多源大数据的整合利用，在此背景下，Python爬虫技术的应用场景势必会越来越多，相应的人才培养、技术革新建议纳入到公共卫生教育和科研体系之中。.
Analysis of heavy metal concentration in auto-mechanic dumpsites in Makurdi Metropolis, North Central Nigeria

This research was carried out to assess heavy metals pollution in Makurdi auto-mechanic dumpsites in Kanshio (K), Northbank (NB) and Wadata (W). Atomic absorption spectrophotometer (PG990) was used to determine the heavy metals concentration. Results of the mean heavy metals concentrations (Cd, Cu, Pb and Zn) were found to be K (4.65, 137.12, 257.77 and 294.75), NB (1.97, 65.67, 162.88 and 223.23), and W (2.92, 85.27, 187.20 and 262.00 mg/kg), respectively, they were higher than their referenced maximum permissible level in the soil when compared to World Health Organization (WHO), United States Environmental Protection Agency (USEPA), Mean Shale Concentration (MSC) and World Surface Rock Average (WSRA)  guidelines, indicating high level pollution. The high level of Cu, Pb and Zn, may be due to waste from welding activities, lead (Pb) battery repairs and coatings done in these area. The geo-accumulation index for Cd and Pb for the three dumpsites was found to exhibit moderately to strongly polluted character with values within 2-3, while Cu and Zn were from uncontaminated to moderately contaminated with values 1.Key words: Heavy metals, auto-mechanic dumpsite, geo-accumulation index, contamination factor and pollution load index.
Pengembangan Kurikulum Berbasis Nusantara dan Internasional di PAUD Fastrack Funschool Yogyakarta

Early childhood education is a children's educational institution that has the aim of learning to develop children's intelligence by using a variety of innovative learning programs. The purpose of this study was to find out how the development of an archipelago-based and international curriculum program in learning was implemented at Fastrack Funschool Yogyakarta. From the results of research conducted by researchers show that in the learning process applying the archipelago curriculum program uses learning with multiple intelligence methods, in which developing linguistic intelligence, logical mathematical intelligence, spatial visual intelligence, musical intelligence, kinesthetic intelligence, iterpersonal intelligence, intrapersonal intelligence, natural intelligence, and, spiritual intelligence whereas in the international curriculum the learning model of seven habits of successful people includes being a proactive person, setting clear targets, working according to priorities, always thinking of profitable ways, trying to understand and then understanding, creating synergies, and always taking time to improve The ability of these multitude of intelligence and habituation is considered to be very effective in learning in early childhood education institutions at Fastrack Funschool Yogyakarta.
Managing information security in healthcare

Smart city consists of: waste management, smart energy, education, smart communications, smart transportation, traffic management, smart parking, smart streetlights and smart healthcare. All of these areas require management of information safety. Here, the topic is management of information safety in healthcare. The objective is to show the new approach to management of information safety, which involves all employees in this process. Whether manufacturing or service, public or private, organizations increasingly depend on information andcommunication technology (ICT). ICT presents in such extent that its users are not even aware of its influence. It is a usual part of any organization. However, the dependence on ICT holds a potential hazard for organization’s performance. Some issues about the ICT safety should be addressed in every organization. First, does the managementofan organization is aware of the potential risks and problems in the ICT area, such as potential ICT unavailability (risk culture) or accidental damage? Is there a systematic approach to threat identification, vulnerability exploration, and evaluation of the impact of realized threats on the business? Is an organization aware of the value of ICT, which should be treated in the organization as any other asset influencing business efficiency and effectiveness? Preventive and corrective actions (system of controls) are warranted for mitigating the risk of destruction or abuse of ICT. In this paper, we discuss these questions and suggest possible solutions. There are many works about the topic but these are stressed only one segment in management of information safety. We used case study, observation and structure analysis in our exploration. The results will be presented here. The results will be useful for everybody who is worried about information security in organizations. Value of this paper is showing the need of multidisciplinary approach in management of information safety.
Daya Serap Pelaksanaan Mata Kuliah Kependidikan DiTengah Pandemi Covid-19

Covid-19is a new virus that attacks the whole world. The virus has paralyzed all areas of life. Based on the circular of the Minister of education and culture of the Republic of Indonesia Number 2 and 3 of 2020, the learning is done online at home. Good student absorption can improve student learning outcomes. The purpose of this study is to look for points of concern about students' absorption of educational lectures using online methods viewed from internal and external factors. This research uses a descriptive method. The sample used was 67 respondents. Our findings show the students who have been ill while attending lectures is less interested in educational subjects being an internal factor that can affect student absorption, but strong student confidence in passing courses makes this factor not inhibiting factor students, while external factors are factors that affect the absorption of students taking educational courses with the online method in the middle of the covid-19 pandemic. Disruptions of the family economy caused by parents who cannot work in the middle of a pandemic are the main things that affect the lecture process. Parental support is less than optimal, as well as the location of the house that makes the signal often lost and entered.
MPEG-7 Image Color Features

This chapter delivers an outline of MPEG-7 color descriptors. The choice of these color descriptors is influenced by various factors [, , ]. These include (a) their capacity to classify the likeness of perceptual colors, assessed by descriptor performance in matching video segments and images using color features, (b) minimum complexity of the related feature extraction and matching procedures, because MPEG-7 systems must be capable of handling recovery tasks through broad multimedia repositories, or small mobile devices with minimal computational power, (c) coded description sizes that have a significant role in indexing and distributing descriptors through limited bandwidth networks, and (d) the descriptors’ scalability and interoperability.
Accelerated Foveated Rendering based on Adaptive Tessellation

We propose an optimization method for adaptive geometric tessellation, involving the saccadic motion of the human eye and foveated rendering. Increased demands on computational resources, especially in the field of head-mounted devices with gaze contingency make optimization schemes pertinent for a seamless user experience. For implementing foveated rendering, our algorithm tessellates a 3D model in real-time based on the location of the user's gaze, substituted with a mouse cursor in this project as a proof of concept. Saccades and fixations of the human eye are simulated by delaying the process of tessellation and rendering by the minimum time taken to complete a saccade. Calculations required for tessellation and rendering the changes on the screen are stalled as and when the eye fixates after a saccade. The paper walks through our contribution by describing the theory, the application method, and results from our user study evaluating our method.
Expressiveness and Learning of Hidden Quantum Markov Models

Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.
Thompson Sampling for Adversarial Bit Prediction.

We study the Thompson sampling algorithm in an adversarial setting, specifically, for adversarial bit prediction. We characterize the bit sequences with the smallest and largest expected regret. Among sequences of length $T$ with $k < \frac{T}{2}$ zeros, the sequences of largest regret consist of alternating zeros and ones followed by the remaining ones, and the sequence of smallest regret consists of ones followed by zeros. We also bound the regret of those sequences, the worse case sequences have regret $O(\sqrt{T})$ and the best case sequence have regret $O(1)$. We extend our results to a model where false positive and false negative errors have different weights. We characterize the sequences with largest expected regret in this generalized setting, and derive their regret bounds. We also show that there are sequences with $O(1)$ regret.
Statistical Disclosure Control Methods for Microdata from the Labour Force Survey

The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey. In the first step, the author assessed to what extent the confidentiality of information was protected in the original dataset. In the second step, after applying selected methods implemented in the sdcMicro package in the R programme, the impact of those methods on the disclosure risk, the loss of information and the quality of estimation of population quantities was assessed. The conclusion highlights some problematic aspects of the use of Statistical Disclosure Control methods which were observed during the conducted analysis.
VoNR-IPD: A Novel Timing-Based Network Steganography for Industrial Internet

As the predominant trade secret of manufacturing enterprises, industrial data may be monitored and stolen by competitive adversaries during the transmission via open wireless link. Such information leakage will cause severe economic losses. Hence, a VoNR-IPD covert timing steganography based on 5G network is proposed in this paper, in which VoNR traffic is employed as the steganographic carrier of covert communication in Industrial Internet. Interference of network jitter noise is fully considered and the high-order statistical properties of jittered VoNR interpacket delays (IPDs) are imitated during the modulation of confidential industrial data. Thus, the generated covert VoNR IPDs can possess consistent statistical properties with the normal case in order to improve undetectability. Besides, the synchronization mechanism of steganographic embedding mode is designed to control the embedding density of industrial data flexibly. The experimental results show that our scheme can resist statistical-based detections and the network noise effectively, which outperforms the existing methods in terms of undetectability and robustness.
Data Driven High Performance Data Access

I have started research in intelligent, data driven, management of computer storage as soon as I have finished my undergraduate studies. The presentation provides ideas of how to approach high performance data access as a result of more than 20 years of practical and research experience. Parts of the presentation were shown at 15th USENIX Conference on File and Storage Technologies (FAST), Work in progress (WiP) session, Santa Clara, Feb 27 - Mar 2, 2017, The HotStorage '17, WACI session, Santa Clara, July 10-11, 2017, ICDCN Workshop on Smart and Connected Communities: Technological Foundations, Challenges and Opportunities (SCC-2018) , Varanasi, India, January 4-7, 2018, International Conference on Edge Computing Seattle, USA, June 25-30, 2018, at internal meetings at companies like Dell, HPE, Huawei, Salesforce, at Industry–University Cooperative Research Centers Program (IUCRC) meetings and at my dissertation defense.
Constraint Solving over Multiple Similarity Relations

Similarity relations are reflexive, symmetric, and transitive fuzzy relations. They help to make approximate inferences, replacing the notion of equality. Similarity-based unification has been quite intensively investigated, as a core computational method for approximate reasoning and declarative programming. In this paper we consider solving constraints over several similarity relations, instead of a single one. Multiple similarities pose challenges to constraint solving, since we can not rely on the transitivity property anymore. Existing methods for unification with fuzzy proximity relations (reflexive, symmetric, non-transitive relations) do not provide a solution that would adequately reflect particularities of dealing with multiple similarities. To address this problem, we develop a constraint solving algorithm for multiple similarity relations, prove its termination, soundness, and completeness properties, and discuss applications.
Lower Bounds for Function Inversion with Quantum Advice.

Function inversion is the problem that given a random function $f: [M] \to [N]$, we want to find pre-image of any image $f^{-1}(y)$ in time $T$. In this work, we revisit this problem under the preprocessing model where we can compute some auxiliary information or advice of size $S$ that only depends on $f$ but not on $y$. It is a well-studied problem in the classical settings, however, it is not clear how quantum algorithms can solve this task any better besides invoking Grover's algorithm, which does not leverage the power of preprocessing. Nayebi et al. proved a lower bound $ST^2 \ge \tilde\Omega(N)$ for quantum algorithms inverting permutations, however, they only consider algorithms with classical advice. Hhan et al. subsequently extended this lower bound to fully quantum algorithms for inverting permutations. In this work, we give the same asymptotic lower bound to fully quantum algorithms for inverting functions for fully quantum algorithms under the regime where $M = O(N)$. In order to prove these bounds, we generalize the notion of quantum random access code, originally introduced by Ambainis et al., to the setting where we are given a list of (not necessarily independent) random variables, and we wish to compress them into a variable-length encoding such that we can retrieve a random element just using the encoding with high probability. As our main technical contribution, we give a nearly tight lower bound (for a wide parameter range) for this generalized notion of quantum random access codes, which may be of independent interest.