Reviewerly/exHarmony huggingface dataset:
FRI-Miner: Fuzzy Rare Itemset Mining

Data mining is a widely used technology for various real-life applications of data analytics and is important to discover valuable association rules in transaction databases. Interesting itemset mining plays an important role in many real-life applications, such as market, e-commerce, finance, and medical treatment. To date, various data mining algorithms based on frequent patterns have been widely studied, but there are a few algorithms that focus on mining infrequent or rare patterns. In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-life applications. In this paper, we introduce a novel fuzzy-based rare itemset mining algorithm called FRI-Miner, which discovers valuable and interesting fuzzy rare itemsets in a quantitative database by applying fuzzy theory with linguistic meaning. Additionally, FRI-Miner utilizes the fuzzy-list structure to store important information and applies several pruning strategies to reduce the search space. The experimental results show that the proposed FRI-Miner algorithm can discover fewer and more interesting itemsets by considering the quantitative value in reality. Moreover, it significantly outperforms state-of-the-art algorithms in terms of effectiveness (w.r.t. different types of derived patterns) and efficiency (w.r.t. running time and memory usage).
The Android Shell

Android is built upon Linux meaning that when using adb (Android’s proprietary command-line tool that allows for communication with a device), you can issue common Linux commands (such as ls, cd, whoami, etc.) as well as several commands unique to the Android operating system. This chapter covers some of the most common and useful of these commands.
MOGAN: Morphologic-structure-aware Generative Learning from a Single Image

In most interactive image generation tasks, given regions of interest (ROI) by users, the generated results are expected to have adequate diversities in appearance while maintaining correct and reasonable structures in original images. Such tasks become more challenging if only limited data is available. Recently proposed generative models complete training based on only one image. They pay much attention to the monolithic feature of the sample while ignoring the actual semantic information of different objects inside the sample. As a result, for ROI-based generation tasks, they may produce inappropriate samples with excessive randomicity and without maintaining the related objects' correct structures. To address this issue, this work introduces a MOrphologic-structure-aware Generative Adversarial Network named MOGAN that produces random samples with diverse appearances and reliable structures based on only one image. For training for ROI, we propose to utilize the data coming from the original image being augmented and bring in a novel module to transform such augmented data into knowledge containing both structures and appearances, thus enhancing the model's comprehension of the sample. To learn the rest areas other than ROI, we employ binary masks to ensure the generation isolated from ROI. Finally, we set parallel and hierarchical branches of the mentioned learning process. Compared with other single image GAN schemes, our approach focuses on internal features including the maintenance of rational structures and variation on appearance. Experiments confirm a better capacity of our model on ROI-based image generation tasks than its competitive peers.
Research on Chinese Community in Korea

Community language is considered as an important issue in linguistic research. Currently, the community language is based on HongKong, Macao, Taiwan, and as well as the other regions outside of China, such as Singapore, Indonesia, Malaysia. This study is for Chinese community language.<BR> In fact, except for the Chinese, any language spoken within the Chinese language is likely to form a Chinese community language. Due to the social variant of language, although it is closely related to the speaker, however it can not be completely confined to the speaker. The people who non-Chinese mother language are having conversation with Chinese or with foreigner on Chinese, there may be occurance of variant oflanguage.however,thisvariantoflanguage is known as “interlanguage” in the field of language teaching.<BR> Theory of interlanguage in order to find an effective method for finding fault with correct deviation, the thesis aims to investigate the effective method for finding fault with correct deviation. We believe that
Pengembangan Perangkat Lunak Pengolahan Data Farmasi Pada Klinik Kesehatan

Disruptive innovation makes it possible for various data management and delivery of information using more information technology. The development of information technology, which is fast and advanced, has a very influential role in various services. For the sake of creating complete services, all health service providers need a system in serving patients. One of the services that currently requires accuracy is the pharmacy department.Pharmacy is a vital part of the health sector. The problem that often arises in data management activities in the pharmaceutical sector is the need for time in fast drug transaction services. The obstacle when implementing it using conventional methods is that it takes a long time and requires more accuracy so that services are hampered.By applying information technology in managing pharmaceutical data, it is able to provide convenience in managing drug data and producing the required reports. The purpose of this research is to build a pharmaceutical data management software that is able to provide convenience in computerized pharmaceutical data management, so that data and information can be stored and managed effectively and efficiently. This research is expected to provide solutions to health services, especially in the field of pharmacy, to provide good service so as to improve the quality of service to patients.
Corpus analysis of coreference chains : annotation and good-enough representations (in a French corpus)

A coreference chain designates the set of linguistic expressions that refer to the same entity. The coreference relation between a chain's elements implies that the referent must be strictly the same for each expression that composes it. However, the referent of an expression is sometimes difficult to identify and the coreference relation between several expressions cannot therefore be strict without any doubt. For a reader, this lack of precision does not necessarily presents difficulties. Nevertheless, the coreference annotation task of a corpus consists in unequivocally identifying the referent of each expression. Non-strict coreference phenomena can therefore generate annotation difficulties. The members of the ANR Democrat project have produced a corpus annotated in coreference. This is an annotation of exact coreference. However, the analysis of the annotation the french « on » pronoun in this corpus reveals a great variability in the annotation of these phenomena from which we derive a classification. To avoid the annotation difficulties related to these phenomena, a more precise framework for the annotation of fuzzy coreference could be considered.
Training Generative Adversarial Networks in One Stage

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. We also computationally analyze the efficiency of the proposed method, and empirically demonstrate that, the proposed method yields a solid $1.5\times$ acceleration across various datasets and network architectures. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation. The code is available at https://github.com/zju-vipa/OSGAN.
A Novel Approach for Hiding Sensitive Association Rules using DPQR Strategy in Recommendation Systems

Mining association rules is considered to be a core topic of data mining. Discovering these associations is beneficial and is highly needed to the correct and appropriate decision made by decision makers in the different fields. Association rule Mining imposes threats to data sharing, since it may disclose patterns and various kinds of sensitive knowledge that are difficult to find. Such information must be protected against unauthorized access. In this paper, we are implementing DPQR strategy (data perturbation and query restriction) to hide the sensitive patterns. Experimental results showed that our proposed system can hide sensitive rules with multiple items in consequent (right hand side (R.H.S) ) and antecedent ( left hand side (L.H.S)) with efficient and faster performance compared to MDSRRC (Modified Decrease Support of R.H.S. items of Rule Cluster) with average improvement 96.22 % as well as generating accurate recommendations without revealing sensitive information. Keywords Data mining, recommender system, privacy.
ANALISIS KUALITAS SISTEM LAYANAN PENILAIAN BUKU PENDIDIKAN AGAMA (SiLPBPA) MENGGUNAKAN METODE SYSTEM USABILITY SCALE (SUS) DAN PENGEMBANGANNYA MENGGUNAKAN SCRUM BERBASIS WEB

SiLPBPA version 1.0 is implemented in the Religious Education Book Assessment Service activities. However, there are many obstacles and problems, namely that some system functions do not match the needs that support their activities. The purpose of this research is to conduct quality analysis and develop SiLPBPA. The methodology for conducting quality analysis is the System Usability Scale (SUS), and its development uses the Agile method with a Scrum framework. The results show that the system's quality gets a score of 71, which means the system is Acceptable, is in Predicate C, and with a good rating. However, some users gave some suggestions on the questionnaire. These suggestions are evaluated, which are used as the basis for system development. The results of developing using Scrum can be completed within 65 hours of the target time of 70 hours shown on the Burndown Chart. Some system functions can be completed properly. This was proven at the Sprint Review event, the Scrum Team and Stakeholders who attended approved the Product Increments that had been developed in all Sprints. So that SiLPBPA can be used in the next Education Book Assessment service activities.
Black-Box IoT: Authentication and Distributed Storage of IoT Data from Constrained Sensors

We propose Black-Box IoT (BBox-IoT), a new ultra-lightweight black-box system for authenticating and storing IoT data. BBox-IoT is tailored for deployment on IoT devices (including low-Size Weight and Power sensors) which are extremely constrained in terms of computation, storage, and power. By utilizing core Blockchain principles, we ensure that the collected data is immutable and tamper-proof while preserving data provenance and non-repudiation. To realize BBox-IoT, we designed and implemented a novel chain-based hash signature scheme which only requires hashing operations and removes all synchronicity dependencies between signer and verifier. Our approach enables low-SWaP devices to authenticate removing reliance on clock synchronization. Our evaluation results show that BBox-IoT is practical in Industrial Internet of Things (IIoT) environments: even devices equipped with 16MHz micro-controllers and 2KB memory can broadcast their collected data without requiring heavy cryptographic operations or synchronicity assumptions. Finally, when compared to industry standard ECDSA, our approach is two and three orders of magnitude faster for signing and verification operations respectively. Thus, we are able to increase the total number of signing operations by more than 5000% for the same amount of power.
Low-level cognitive skill transfer between two individuals' minds via computer game-based framework

The novel technique introduced here aims to accomplish the first stage of transferring low-level cognitive skills between two individuals (e.g. from expert to learner) to ease the consecutive higher level declarative learning process for the target "learner" individual in a game environment. Such low-level cognitive skill is associated with the procedural knowledge and established at low-level of mind which can be unveiled and transferred by only a novel technique (rather than by a traditional educational environment ) like a highly interactive computer game domain in which a user exposes his/her unconscious mind behaviors via the game-hero non-deliberately during the game sessions. The cognitive data exposed by the game-hero would be recorded, and then be modelled by the artificial intelligence technique like Bayesian networks for an early stage of cognitive skill transfer and the cognitive stimuli are also generated to be used as game agents to train the learner.
THE DIHUB MODEL FOR LEARNING AND INNOVATING

This paper describes the Dihub model and its piloting in Helsinki.Dihub (Digital Innovation Hub for Cloud-based Services) is an EU-funded project that aims at the promotion and creation of cloud-based knowledge and innovations in Europe through the construction of an innovation hub platform, a study path and the related processes.These are tested in several pilot rounds during two academic years in five European countries: Bulgaria, Croatia, Estonia, Finland and Portugal.The educational partners involved are institutions from both vocational and higher education.The stakeholders involved in the Dihub model are business owners and experts in companies using cloud services, experts from cloud technology providers, educators and students.
APLIKASI MOBILE ARSIP PRODI MENGGUNAKAN FRAMEWORK CORDOVA (STUDI KASUS : PRODI TEKNIK INFORMATIKA INSTITUT SHANTI BHUANA)

The use of mobile-based applications has become a necessity for routine administration, especially for lecturers at the Shanti Bhuana Institute, Bengkayang, West Kalimantan. Twice weekly study program meetings, namely tuesday and friday, are an obligation so that lecturers can evaluate the performance with the head of the study program and the vice chancellor regarding the progress of each study program line such as research, community service, teaching and learning activities, even workshops. However, the briefing was limited to communication via whats app during the pandemic season making it difficult to recap the minutes during the briefing, especially as a report to the stakeholders. The NopTI application (Notulen Arsip Prodi TI) is presented to make it easier for lecturers to archive routine briefings based on mobile android. By using the cordova framework which is still rare in developing mobile applications and utilizing google drive as a storage medium, it will certainly reduce costs in terms of database storage. Applications are used more than 80% by IT lecturers, 60% use online briefings, 40% offline briefings and black box tested by several IT experts valid 100% fulfilled from the functions tested, and can be used on android 8.0 to 10.0 and a delay of 186.414 ms nice category to open apps. Keywords: administration, mobile, briefings, cordova. .
Unsupervised Image Transformation Learning via Generative Adversarial Networks

In this work, we study the image transformation problem, which targets at learning the underlying transformations (e.g., the transition of seasons) from a collection of unlabeled images. However, there could be countless of transformations in the real world, making such a task incredibly challenging, especially under the unsupervised setting. To tackle this obstacle, we propose a novel learning framework built on generative adversarial networks (GANs), where the discriminator and the generator share a transformation space. After the model gets fully optimized, any two points within the shared space are expected to define a valid transformation. In this way, at the inference stage, we manage to adequately extract the variation factor between a customizable image pair by projecting both images onto the transformation space. The resulting transformation vector can further guide the image synthesis, facilitating image editing with continuous semantic change (e.g., altering summer to winter with fall as the intermediate step). Noticeably, the learned transformation space supports not only transferring image styles (e.g., changing day to night), but also manipulating image contents (e.g., adding clouds in the sky). In addition, we make in-depth analysis on the properties of the transformation space to help understand how various transformations are organized. Project page is at https://genforce.github.io/trgan/.
TRAINING DIGITAL AGE ENGINEERS: A COMPARATIVE ANALYSIS OF EDUCATIONAL PRACTICES

L. Bannikova1, A. Zhilin1, C. Szabó Marianna2, I. Sholina1 1Ural Federal University (RUSSIAN FEDERATION) 2University of Dunaújváros (HUNGARY)
Kemahiran berfikir kritis melalui permainan digital dalam persekitaran pembelajaran konstruktivisme sosial

The process of implementation of the educational transformation requires a new approach and strategy so that each student can afford to own and master the skills required in the 21st century. Therefore to ensure the development of creativity of educators, methods and strategies of teaching and learning undertaken should take into account the tendencies, abilities and capability maturity of students and be able to think at the level is higher. The increase in popularity is the use of computer technology and communication in human daily life especially in the simulation and digital games have attracted the interest of researchers-researchers to mendalaminya that can increase the capacity and fostering critical thinking skills among students. This concept paper discusses the potential use of digital game toward critical thinking skills of students through social constructivism learning environment.
AN APPLICATION OF MACHINE LEARNING FOR ANALYSIS OF ROADWAY ACCIDENTS USING FEATURE CLASSIFICATION

Roadway traffic wellbeing is a significant worry for transportation overseeing organizations just as standard citizens. Data Mining is removing from concealed examples from colossal database. It is ordinarily utilized in an advertising, reconnaissance, extortion location and logical revelation. In data mining, AI is essentially engaged as exploration which is naturally figured out how to perceive complex examples and settle on smart choices dependent on data. Globalization has influenced numerous nations. There has been an intense expansion in the monetary exercises and utilization level, prompting development of movement and transportation. The increment in the vehicles, traffic lead to street accidents. Thinking about the significance of the street wellbeing, government is attempting to distinguish the reasons for street accidents to lessen the accidents level. The dramatic expansion in the accidents data is making it hard to break down the limitations causing the street accidents. The paper depicts how to mine successive examples causing street accidents from gathered data set. We discover relationship among street accidents and anticipate the kind of accidents for existing just as for new streets. We utilize affiliation and characterization rules to find the examples between street accidents and just as foresee street accidents for new streets.
Improved poetry writing skills through inductive model word pictures (pwim) in grade v elementary school students of Pasiraman Lor district Banyumas in the school year 2018/2019

Abstract. This research aims to upgrade Poetry Writing Skills through Picture Word Inductive Model (PWIM) in Grade V of State Elementary School Pasiraman Lor in the academic year 2018/2019. This research motivated by low poetry writing skills. This study will be held in 2 cycles, each cycle consist of 3 meetings, namely the planning, implementation, and reflection stages. The students of V grade that consist of 6 female and 10 male students are the subject of this research. Data collection methods are interviews, observation, documentation, and tests. The validity used is technical and source triangulation. Data analysis shows qualitative descriptive data analysis. The aspects assessed in writing poetry are the novelty’s theme and meaning, the power of imagination, the accuracy of diction, the exposures of exposition, and the teacher's affective response. As the results, the percentage of completeness of students' poetry writing skills was pre-action 23.53%, cycle I 58.82%, and cycle II 88.24%.The conclusion of this study is that poetry writing skills can be improve by using PWMI. Keywords: poetry writing skills, Picture Word Inductive Model (PWIM), quality of learning outcomes, elementary school
The role of verb semantics in Hungarian verb-object order

Hungarian is often referred to as a discourse-configurational language, since the structural position of constituents is determined by their logical function (topic or comment) rather than their grammatical function (e.g., subject or object). We build on work by Komlósy (1989) and argue that in addition to discourse context, the lexical semantics of the verb also plays a significant role in determining Hungarian word order. In order to investigate the role of lexical semantics in determining Hungarian word order, we conduct a large-scale, data-driven analysis on the ordering of 380 transitive verbs and their objects, as observed in hundreds of thousands of examples extracted from the Hungarian Gigaword Corpus. We test the effect of lexical semantics on the ordering of verbs and their objects by grouping verbs into 11 semantic classes. In addition to the semantic class of the verb, we also include two control features related to information structure, object definiteness and object NP weight, chosen to allow a comparison of their effect size to that of verb semantics. Our results suggest that all three features have a significant effect on verb-object ordering in Hungarian and among these features, the semantic class of the verb has the largest effect. Specifically, we find that stative verbs, such as fed 'cover', jelent 'mean' and övez 'surround', tend to be OV-preferring (with the exception of psych verbs which are strongly VO-preferring) and non-stative verbs, such as bírál 'judge', csökkent 'reduce' and csókol 'kiss', verbs tend to be VO-preferring. These findings support our hypothesis that lexical semantic factors influence word order in Hungarian.
Algebraic Properties of Gaussian HTC-identifiable Graphs

In this paper, we explore some algebraic properties of linear structural equation modelsthat can be represented by an HTC-identifiable graph. In particular, we prove that all mixedgraphs are HTC-identifiable if and only if all the regression coefficients can be recovered fromthe covariance matrix using straightforward linear algebra operations. We also find a set ofpolynomials that generates the ideal that encompasses all the equality constraints of the modelon the cone of positive definite matrices. We further prove that this set of polynomials are theminimal generators of said ideal for a subset of HTC-identifiable graphs.