Reviewerly/exHarmony huggingface dataset:
Sistem Pendukung Keputusan Pemilihan Masyarakat Penerima Bantuan Program Keluarga Harapan Metode SAW

Uluwae II Village is one of the villages in the District on Nort Bajawa, Ngada Regency, whichis also a target village for receiving sicial assistance in the formof the Hope Program (PKH) assistance. PKH is a program of providing conditional social assistance to Beneficiary Families (PKM). In the particular, the objectives of PKH are to improve access and quality of education and health services for PKH participants, to in crease the education level of PKH participants, and to improve the health and nutritional status of PKH participants. Therefore, Uluwae II village official smust carefully select the right community to receive PKH assistance. In determining the PKH recipient community in Uluwae II village, it was still not right on targer, because the selection procces for the PKH recipient community was not running as expented by the community. Therefore, it is necessary to have a system to determine decision in determining the PKH recipient community. The purpose and benefit of this researchis to design and build decision support system to determine the right community to receive PKH according of the weigh to feach criterion. This research was completed through several stage, namely, collecting information through directre search at the location. The method used the SAW method to calculate the weight and determine the ranking.
Sistem Informasi pada Persediaan Barang Berbasis Web di Frozen Food

Frozen Food Store is a store engaged in the sale of processed frozen instant foods that are durable and easy to serve. Meat-based frozen food products currently vary widely in the market and are consumed quite a lot. Survey data conducted by the Ministry of Agriculture (2015) revealed a very rapid development in the consumption of processed meat at the national level. The average development of processed meat consumption annually is 10.28 percent. The results of another survey stated that the increase in consumption of sausages grew by an average of 4.46 percent per year. Frozen food products such as nuggets, meatballs and sausages are the most consumed processed meat products. The increasing consumer demand for frozen food has an impact on the increasing demand for meat, chicken and beef. Currently at the Affa Frozen Food Store, the data collection process for incoming goods still has many shortcomings, often goods go out and inventory stock is not recorded due to human error, so that data collection in the store is not optimal. . In order for the system to run optimally, it is recommended to use an inventory information system. The purpose of this inventory information system is to overcome the problems that often occur in the currently running system at the Frozen Food Store and be a good solution for companies to create a faster and more accurate system in managing inventory. Often the company has problems
Rancang Bangun Sistem Informasi Inventori Barang Masuk dan Keluar pada Bengkel Aries Star Motor Berbasis Websiste

The problem that occurs in this study is the Aries Star Motor workshop which is engaged in the supply of automotive goods as a place to renovate a two-wheeled vehicle, this workshop has a lot of vehicle parts that are stored and not recorded properly and are still manual by using books. and calculators, be it transactions or reports so as to make workshop performance less effective and efficient. Employees sometimes still cannot distinguish the names of goods and the code of goods. Of course this problem will make financial reports and inventory reports will not be well controlled. The purpose of this research is to design an inventory information system for incoming and outgoing goods at a website-based Aries star motor workshop. The method used in this study is a descriptive qualitative method with a qualitative approach with data collection techniques in the form of observations, interviews, and literature studies. System development using the waterfall method and testing using black box testing the results of the system made are able to design an inventory information system for incoming and outgoing goods properly and can be implemented. The conclusion is that it can implement the design of the incoming and outgoing goods inventory information system at the website-based Aries star motor workshop so that it is better.
Sistem Informasi Pajak Bumi dan Bangunan Berbasis Stand Alone

The problem in this study is that the Land and Building Tax Service at the Potulando Village Office is still manual or conventional, so it requires a lot of place and time in storing data or processing data, besides that the reports produced are inaccurate. The purpose of this research is to design a Stand Alone -Based Land and Building Tax Information System. Sources of data in this study are primary data sources and secondary data sources. This type of research is qualitative while the data collection techniques in this study use three methods, namely, observation, interviews and library studies. The construction of a Stand Alone-Based Land and Building Tax Information System in this study uses Visual Basic.Net . The system development method used in this research is System Development. Life Cycle (SDLC), while The system testing technique in this study uses the Blackbox testing method. The system design in this study uses a use case diagrams, context diagrams, and data flow diagrams. With the existence of a Land and Building Tax Information System , it can improve the tax service process quickly and practically compared to the manual process , facilitate the process of recording taxpayer identity , able to obtain the desired taxpayer data quickly and easily. Facilitate the process of making tax reports , both monthly and annual reports .
Soil Contamination and Risk Assessments on Selected Dumpsites within the Basement Complex and Sedimentary Formations of Ogun State, South-western Nigeria

The concentrations of inorganic elements in soils of Saje, Ita-Oshin, Premier, and Oke-Diya dumpsites were determined using an X-Ray Fluorescence spectrometer to assess the soil quality through chronological changes with depths. A soil core sampler collects 56 samples at 20, 40, 60, and 80 cm depths. Saje showed high mean concentrations of Ca, K, Fe, Rb, and Ti at all depths, while Oke-Diya revealed high mean concentrations of Ca, K, Fe, Rb, Ti, Mn, Zn, V, Cr, and Ni at all depths. These concentrations were in a closed range suggesting a build-up of elements in the soil profiles. Oke-Diya had the highest pollution index, followed by Saje, and the contaminations were from anthropogenic and geologic sources. The mean concentrations of Fe, Zn, Ni, Mn, Ti, K, and Ca were high at all depths in Ita-Oshin, and in Premier, the mean concentrations of K and Fe were high at all depths. Premier had the lowest pollution index. Ita-Oshin and Premier enrichment factors showed geologic sources. Saje and Oke-Diya revealed unacceptable non-carcinogenic risks for adults and children, and Ita-Oshin and Premier recorded acceptable limits. Saje and Oke-Diya samples had high carcinogenic risks for adults due to Cr, Pb, and As. Elements Cr and As in Ita-Oshin, and Pb in Premier evinced unacceptable carcinogenic risks for adults. Saje, Oke-Diya, and Premier indicated unacceptable carcinogenic risks for children due to Cr. The study showed toxins build-up in the soil system, and the study areas are not suitable for growing crops and sinking water wells for agricultural and domestic uses.
Knowledge Distillation for Image Signal Processing Using Only the Generator Portion of a GAN

Knowledge distillation, in which the parameter values learned in a large teacher network are transferred to a smaller student network, is a popular and effective network compression method. Recently, researchers have proposed methods to improve the performance of a student network by using a Generative Adverserial Network (GAN). However, because a GAN is an architecture that is ideally used to create realistic synthetic images, a pure GAN architecture may not be ideally suited for knowledge distillation. In knowledge distillation for image signal processing, synthetic images do not need to be realistic, but instead should include features that help the training of the student network. In the proposed Generative Image Processing (GIP) method, this is accomplished by using only the generator portion of a GAN and utilizing special techniques to capture the distinguishing feature capability of the teacher network. Experimental results show that the GIP method outperforms knowledge distillation using GANs as well as training using only knowledge distillation.
The Effect of Individual Learning To Technology Capability

The spread of Covid-19 throughout the world prompted the WHO to establish a pandemic status. This situation encourages the government to make social distancing policies through the work from home (WFH) program. No exception, the Ministry of Education determines the status of WFH from early childhood education to higher education. For higher education, e-learning has become a program in learning, especially in the 4.0 era. However, not all lecturers and students have e-learning capability. The purpose of this study explores individual learning to improve technology capabilities, especially in overcoming the Covid-19 Pandemic. The study was carried out throughout Indonesia by distributing questionnaires via a google form. The distribution is carried out for 6 (six) weeks through various networks. The responses from distributing questionnaires were 420 respondents. Data analysis was performed using regression and factor analysis through SPSS. The factor analysis confirms that individual learning consists of three elements (1) recognizing learning opportunities, (2) applying new knowledge, and (3) self-directedness. The result shows that individual learning has an effect on technology capability, and applying new knowledge has a greater effect to achieve technology capability. This finding supports Cognitive Learning theory, which states that individual learning supports actual behavior change and promotes adaptation to environmental conditions. Individuals may be more motivated through individual learning. Individual learning is a process involving a change in an agent's behavior or knowledge
A Study on Satisfaction of Teachers Taking Charge of Innovative Education District in Gyeonggi-do: Focusing on the Case of the P Area in Gyeonggi-do

Objectives This is a research and analysis on the perception of teachers in charge of the introduction and operation of the innovative education district in Gyeonggi-do Province P. The purpose is to redesign the innovative education district project in the P area based on the analysis. Methods A survey was conducted on 108 teachers in charge of the innovative education district in Gyeonggi-do P, and a research method was used on the operation of the innovation education district. Results First, the innovative education district, which started in 6 regions of Gyeonggi-do in 2011, has been extended to 16 provincial offices of education. Also, the innovative education district is set as the future education direction. Second, for teachers, the general matters of the innovative education district, teacher satisfaction, and the satisfaction level was 4.13, 4.22, and 4.02, which was a high average. On the other hand, it appears necessary to redesign the project by gathering feedback from multiple areas, such as teachers, parents, and village activists, since the village school utilizing local resources responded with a "3.80" and the awareness of world culture for multicultural understanding with a "3.85". Third, as a result of the average analysis of the innovative education district survey of teachers in area P, significant results according to their teaching career were clearly revealed. In particular, those with less than 10-15 years of teaching experience generally gave positive and high scores to the questionnaire responses. This seems to be the case since the majority of teachers with between 10 and 15 years of experience are head teachers or young teachers in their 40s, who are engaged in the education industry and possess a deep knowledge of the sector. Conclusions This is the expansion of the innovative education district, and it should be operated as a project with the village community and parents, not as a teacher-centered project. Periodic teacher training is necessary to go together.
Sufficient instance networks for computing support in graph databases

Abstract Because displaying connections within the data via a graph is natural and intuitive for humans, graph databases have grown in popularity. A graph database can be made up of multiple smaller graphs or a single huge graph. Finding statistically significant subgraphs (also known as graph patterns) in graph databases is referred to as graph mining. The search process within the graph database and a specified support measure to assess the statistical importance of these graph patterns make up this task’s two components. Finding occurrences of graph patterns in the database, which is a computationally intensive process, is necessary for this support measure, which is frequently difficult to compute. We concentrate on the scenario of a single graph database in this paper. We demonstrate, using the relationship between support measures and flows in networks of pattern instances established in prior work, that the network of pattern instances may be successfully pruned to contain just particular kinds of subgraphs for any suitable support measure. This strategy decreases the size of the instance network, which can lower the effort required to compute a support measure. We demonstrate that any legitimate support measures in graph databases can use this trimming.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Morressier • Number of submissions received : 89 • Number of submissions sent for review : 89 • Number of submissions accepted : 53 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 59.6 • Average number of reviews per paper : 2 • Total number of reviewers involved : 10 • Contact person for queries : Name : M. Tanzil Multazam Email : tanzilmultazam@umsida.ac.id Affiliation : Universitas Muhammadiyah Sidoaro
Machine Learning Construction: Implications to Cybersecurity

Statistical learning is the process of estimating an unknown probabilistic input-output relationship of a system using a limited number of observations. A statistical learning machine (SLM) is the algorithm, function, model, or rule, that learns such a process; and machine learning (ML) is the conventional name of this field. ML and its applications are ubiquitous in the modern world. Systems such as Automatic target recognition (ATR) in military applications, computer aided diagnosis (CAD) in medical imaging, DNA microarrays in genomics, optical character recognition (OCR), speech recognition (SR), spam email filtering, stock market prediction, etc., are few examples and applications for ML; diverse fields but one theory. In particular, ML has gained a lot of attention in the field of cyberphysical security, especially in the last decade. It is of great importance to this field to design detection algorithms that have the capability of learning from security data to be able to hunt threats, achieve better monitoring, master the complexity of the threat intelligence feeds, and achieve timely remediation of security incidents. The field of ML can be decomposed into two basic subfields: construction and assessment. We mean by construction designing or inventing an appropriate algorithm that learns from the input data and achieves a good performance according to some optimality criterion. We mean by assessment attributing some performance measures to the constructed ML algorithm, along with their estimators, to objectively assess this algorithm. Construction and assessment of a ML algorithm require familiarity with different other fields: probability, statistics, matrix theory, optimization, algorithms, and programming, among others. To help practitioners, specially those of cyberphysical security, to understand the theoretical foundations of ML, before they delve into whole books, we compile the very basics of the first of these two subfields (construction) in this chapter. In addition to explaining the mathematical foundations of the field, we emphasize the intuitive explanation and concepts.
Machine Learning Assessment: Implications to Cybersecurity

After discussing the construction of machine learning (ML) algorithms in the previous chapter, this chapter is dedicated to their assessment and performance estimation (with an emphasis on classification assessment), a topic that is equally important specially in the context of cyberphysical security design. The literature is full of nonparametric methods to estimate a statistic from just one available dataset through resampling techniques, e.g., jackknife, bootstrap and cross validation (CV). Special statistics of great interest are the error rate and the area under the ROC curve (AUC) of a classification rule. The importance of these resampling methods stems from the fact that they require no knowledge about the probability distribution of the data or the construction details of the ML algorithm. This chapter provides a concise review of this literature to establish a coherent theoretical framework for these methods that can estimate both the error rate (a one-sample statistic) and the AUC (a two-sample statistic). The resampling methods are usually computationally expensive, because they rely on repeating the training and testing of a ML algorithm after each resampling iteration. Therefore, the practical applicability of some of these methods may be limited to the traditional ML algorithms rather than the very computationally demanding approaches of the recent deep neural networks (DNN). In the field of cyberphysical security, many applications generate structured (tabular) data, which can be fed to all traditional ML approaches. This is in contrast to the DNN approaches, which favor unstructured data, e.g., images, text, voice, etc.; hence, the relevance of this chapter to this field.
Identifying Unique Causal Network from Nonstationary Time Series

Identifying causality is a challenging task in many data-intensive scenarios. Many algorithms have been proposed for this critical task. However, most of them consider the learning algorithms for directed acyclic graph (DAG) of Bayesian network (BN). These BN-based models only have limited causal explainability because of the issue of Markov equivalence class. Moreover, they are dependent on the assumption of stationarity, whereas many sampling time series from complex system are nonstationary. The nonstationary time series bring dataset shift problem, which leads to the unsatisfactory performances of these algorithms. To fill these gaps, a novel causation model named Unique Causal Network (UCN) is proposed in this paper. Different from the previous BN-based models, UCN considers the influence of time delay, and proves the uniqueness of obtained network structure, which addresses the issue of Markov equivalence class. Furthermore, based on the decomposability property of UCN, a higher-order causal entropy (HCE) algorithm is designed to identify the structure of UCN in a distributed way. HCE algorithm measures the strength of causality by using nearest-neighbors entropy estimator, which works well on nonstationary time series. Finally, lots of experiments validate that HCE algorithm achieves state-of-the-art accuracy when time series are nonstationary, compared to the other baseline algorithms.
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey

Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learning-based representations. However, video models trained on existing datasets suffer from significant performance degradation when deployed directly to real-world applications due to domain shifts between the training public video datasets (source video domains) and real-world videos (target video domains). Further, with the high cost of video annotation, it is more practical to use unlabeled videos for training. To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models. This paper surveys recent progress in VUDA with deep learning. We begin with the motivation of VUDA, followed by its definition, and recent progress of methods for both closed-set VUDA and VUDA under different scenarios, and current benchmark datasets for VUDA research. Eventually, future directions are provided to promote further VUDA research.
A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Synthetic data is often presented as a method for sharing sensitive information in a privacy-preserving manner by reproducing the global statistical properties of the original data without disclosing sensitive information about any individual. In practice, as with other anonymization methods, privacy risks cannot be entirely eliminated. The residual privacy risks need instead to be ex-post assessed. We present Anonymeter, a statistical framework to jointly quantify different types of privacy risks in synthetic tabular datasets. We equip this framework with attack-based evaluations for the singling out, linkability, and inference risks, the three key indicators of factual anonymization according to the European General Data Protection Regulation (GDPR). To the best of our knowledge, we are the first to introduce a coherent and legally aligned evaluation of these three privacy risks for synthetic data, and to design privacy attacks which model directly the singling out and linkability risks. We demonstrate the effectiveness of our methods by conducting an extensive set of experiments that measure the privacy risks of data with deliberately inserted privacy leakages, and of synthetic data generated with and without differential privacy. Our results highlight that the three privacy risks reported by our framework scale linearly with the amount of privacy leakage in the data. Furthermore, we observe that synthetic data exhibits the lowest vulnerability against linkability, indicating one-to-one relationships between real and synthetic data records are not preserved. Finally, we demonstrate quantitatively that Anonymeter outperforms existing synthetic data privacy evaluation frameworks both in terms of detecting privacy leaks, as well as computation speed. To contribute to a privacy-conscious usage of synthetic data, we open source Anonymeter at https://github.com/statice/anonymeter.