Reviewerly/exHarmony huggingface dataset:
Multivariate Statistical Analysis of Hydrochemical Data to Assess the Groundwater Hydrochemistry in the Areas of Hydrogeological Windows

The main purpose of the research was to study the distinctive features of groundwater chemistry of the target aquifer in Moscow in the areas of hydrogeological windows. In these areas, the contaminants can rapidly enter from the shallow to more deep aquifers that leads to increase of contaminants concentrations and deterioration of water quality. Studies were performed using Multivariate statistical analysis. To determine the background concentrations, the representative sampling was analyzed for the distribution of the elements -indicators in the target and overlying aquifers. The principal component analysis (PCA) were applied to evaluate the main factors of the chemical composition formation in the areas of hydrogeological windows of different categories and outside the windows. The results of research allowed to reveal a complex linear correlation between the components concentrations and to identify the main factors of groundwater chemistry formation: the anthropogenic factor in the areas of hydrogeological windows - percolation the water contaminated with anti-icing agents, communal drains, and the natural factor outside the windows - dissolution of water-bearing carbonate rocks and gypsum layers in carbonate rocks. The theoretical semivariogram model taking into account the effect of the nugget and the wave effect was used to construct the schemes of the spatial distribution of the indicator components concentrations. Borders were identified between zones that differ in terms of formation, which confirms the possibility of using a hydrogeochemical characteristic to identify hydrogeological windows. The research was financially supported by the project of the Russian Foundation for Basic Research No. 17-05-01016.
Middle-level Fusion for Lightweight RGB-D Salient Object Detection.

Most existing lightweight RGB-D salient object detection (SOD) models are based on two-stream structure or single-stream structure. The former one first uses two sub-networks to extract unimodal features from RGB and depth images, respectively, and then fuses them for SOD. While, the latter one directly extracts multi-modal features from the input RGB-D images and then focuses on exploiting cross-level complementary information. However, two-stream structure based models inevitably require more parameters and single-stream structure based ones cannot well exploit the cross-modal complementary information since they ignore the modality difference. To address these issues, we propose to employ the middle-level fusion structure for designing lightweight RGB-D SOD model in this paper, which first employs two sub-networks to extract low- and middle-level unimodal features, respectively, and then fuses those extracted middle-level unimodal features for extracting corresponding high-level multi-modal features in the subsequent sub-network. Different from existing models, this structure can effectively exploit the cross-modal complementary information and significantly reduce the network's parameters, simultaneously. Therefore, a novel lightweight SOD model is designed, which contains a information-aware multi-modal feature fusion (IMFF) module for effectively capturing the cross-modal complementary information and a lightweight feature-level and decision-level feature fusion (LFDF) module for aggregating the feature-level and the decision-level saliency information in different stages with less parameters. Our proposed model has only 3.9M parameters and runs at 33 FPS. The experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods.
Game Theoretic Analysis on Encrypted Data Deduplication in Cloud

Deduplication is a technology for removing duplicated data by only storing one copy in the cloud. User concerns about security and privacy lead them to store sensitive data in an encrypted form. Existing encrypted cloud data deduplication (Here, referred to as Deduplication) schemes can be classified into three types: server-controlled deduplication (S-DEDU), client-controlled deduplication (C-DEDU), and hybrid deduplication (H-DEDU), based on which stakeholder (Cloud Service Provider (CSP), data owner or combination of both) can control the deduplication. However, the deduplication schemes widely proposed in the literature are rarely applied in practice. Rational CSPs and data users are profit-driven and decide whether to adopt a deduplication scheme based on the benefits gained from it. Storing one copy for each data item narrows the relationship between data users and amplifies the impact of adverse behaviors, such as data disclosure and mismanagement in CSPs. In this dissertation, we apply game theory as the main methodology to capture the dynamic interaction of system stakeholders in the three types of deduplication schemes and to design incentive mechanisms for motivating all involved stakeholders to participate and cooperate. We build a non-cooperative game between CSPs and data holders in S-DEDU, in which all players choose whether to participate in deduplication. We propose a bounded discount-based incentive mechanism for promoting the willingness of data holders to participate in S-DEDU while at the same time guaranteeing the profits of CSPs. In the case of C-DEDU, we define a unified discount model and an individualized discount model and investigate their applicability in C-DEDU. We establish a non-cooperative game model between a data owner and a data holder, where each player chooses to follow or deviate from the deduplication scheme design. By analyzing the Nash Equilibrium of this game model, we identify a free-riding behavior in the unified discount model and a privacy violation problem in the individualized discount model, as well as innovate two solutions for promoting cooperation while at the same time preserving data privacy. Finally, in the case of H-DEDU, we refine the two-dimensional action sets in S-DEDU and C-DEDU by transforming them into uncountable ones, as well as build a Stackelberg game for studying the interaction among a CSP, a data owner, and data holders. We analyze the cooperative behaviors of all players for revealing the optimal strategies employed by the stakeholders and prove the existence of a Nash Equilibrium in the H-DEDU system.
Data Mining for Processing of Research and Community Service by Lecturer Using Decision Tree Method

Data on community service and research by the lecturer are important and must be possessedby every lecturer and institution. This is necessary for an effort to ensure quality management of the institution.Therefore needed to get an appropriate algorithm in classifying the data. The Decision Tree algorithm is one of themost powerful and well-known classifications and prediction methods. The decision tree method converts huge facts into decision trees that represent rules. Rules can be easily understood with natural language. Decision Tree algorithm has achieved the main objective of the system to obtain research information and community service at the lecturers. The system work can provide an overview about of the quality of lecturers with data calculated using the Decision Tree method which has an accuracy of 66.67%
MODEL PROFESSIONAL DEVELOPMENT BERBASIS INTERNET UNTUK MENINGKATKAN TEAM LEARNING SEKOLAH

INTERNET BASED PROFESSIONAL DEVELOPMENT MODEL TO IMPROVE SCHOOL LEARNING TEAMThe tendency of the fact where the teachers do not need to learn or do not want to improve professional competence is a reflection of the opposite of team learning. Schools need to improve their performance to achieve maximum results. This study aimed to analyze an Internet-Based Professional Development model that can improve the ability of kindergarten teachers to carry out professional development independently and build team learning as well. Internet-Based Professional Development is an alternative effort in overcoming the improvement of kindergarten teachers' ability to organize Professional Development as well as team-learning. Limited trial research was conducted at Smartskul Preschool Kindergarten with 5 teachers. Extensive trials were carried out at TKI Al-Hamidiyah Depok with 25 teachers, and at TKIT Aliya Bogor with 13 teachers. The method of research used was research and model development (Research Professional Development / R D). Each trial carried out three times actions in limited trials and two actions in extensive trials at each school. In this study, the data analysis used was qualitative, quantitative, and formative. To measure the validity, this research used non-test and expert judgment. The Internet-Based Professional Development model consisted of input, process, and output. The input consisted of the Internet-Based Professional Development module, and kindergarten teachers and principals. The process consisted of planning, implementing monitoring, as well as evaluation reflection. The output of the Internet-Based Professional Development model was able to improve the ability of kindergarten teachers in organizing Internet-Based Professional Development as well as being able to build team learning.
Inteligencia artificial e inspección de educación. Reorganizar el servicio de inspección de educación (sie) para el siglo xxi.

espanolDos objetivos inspiran este trabajo, derivado de dos realidades que inciden en la educacion y en la Inspeccion educativa. La primera realidad es la incorporacion masiva e intensiva de las tecnologias de la informacion y la comunicacion en el ambito educativo. La segunda es la inmersion social en una etapa en la que todo se mide, en todos los aspectos de la vida academica, intelectual y social. Ambas realidades suponen suficiente motivacion para plantear dos implicaciones para la Inspeccion de Educacion: la necesidad de su adaptacion a las tecnologias de la informacion y la comunicacion, y su inevitable especializacion. EnglishTwo objectives inspire this work, which derives from two realities that have an impact on education and therefore on the Education Inspectorate. The first reality is the massive and intensive incorporation of information and communication technologies in the educational scope. The second one is the social immersion in a stage in which everything is measured, concerning academic, intellectual and social life. Both of these realities provide enough motivation to set out two implications for the Education Inspectorate: the need to adapt to information and communication technologies, and an unavoidable specialization.
Automatic de-identification of Data Download Packages

The General Data Protection Regulation (GDPR) grants all natural persons the right of access to their personal data if this is being processed by data controllers. The data controllers are obliged to share the data in an electronic format and often provide the data in a so called Data Download Package (DDP). These DDPs contain all data collected by public and private entities during the course of citizens' digital life and form a treasure trove for social scientists. However, the data can be deeply private. To protect the privacy of research participants while using their DDPs for scientific research, we developed de-identification software that is able to handle typical characteristics of DDPs such as regularly changing file structures, visual and textual content, different file formats, different file structures and accounting for usernames. We investigate the performance of the software and illustrate how the software can be tailored towards specific DDP structures.
Developing a Printable Indonesian Local-Content Based English Games Book to Teach Tenses for the 8th Grade (A Research & Development Study at Junior High School 15 of Serang)

This study aims at finding out students’ needs in learning English tenses, process of developing a printable Indonesian-local content based English games book, and quality of a printable Indonesian-local content based English games book. Research and Development (R & D) of Borg & Gall (1983) design was employed in this research. The researcher adopted 7 steps from Borg and Gall (1983); Research and Information Collecting, Planning, Developing Preliminary Form of Product, Preliminary Field Testing, Main Product Revision, Main Field Testing, and Operational Product Revision. Four instruments were used in this research; an Open-Ended Questionnaire, Semi Structure Interview, Expert Validation Sheet, and Students’ Assessment Sheet. The open-ended questionnaire and the semi structure interview were employed to collect data of students’ needs in English learning tenses, both instruments were analyzed qualitatively based on Miles and Huberman’s theory. The experts’ validation sheet and the students’ assessment sheet were used to collect data of developing process and quality of the product, both instruments were analyzed quantitatively by using descriptive statistics adopted from Johnson & Christensen. First data analysis revealed that the students needed learning media based on printable games book in learning English tenses. Second, the seven processes of the developing product were passed successfully. Third, the printable Indonesian-local content based English games book is qualified to be used. In conclusion, the developed product can be an alternative learning-teaching aids for 8th grade  of SMPN 15 Kota Serang.
PERBANDINGAN METODE PENGENALAN WAJAH MELALUI SURVEILLANCE BERBASIS PENGENALAN WAJAH

Until now, the facial recognition method is still very difficult to do, especially when the facial confirmation process is accurate in real time. Facial recognition methods that have been tested, such as eigenface, Local Binary Pattern Histogram (LBPH), and fisherface, are feasible methods to be tested directly by applying these three methods to facial recognition-based surveillance systems. This study aims to compare the level of real-time accuracy in personal identification on the three methods through 4 parameters, namely accuracy, FAR (False Acceptance Rate), FRR (False Rejection Rate), and time condition, namely lighting conditions based on time, namely morning, noon, afternoon and evening. Based on the results of the tests that have been done, the average accuracy is obtained, namely the highest average with the eigenface method with an accuracy of 73.64%, FAR 0.11%, FRR 0.15%, the LBPH method obtains the highest average with an accuracy of 80, 91%, FAR 0.13%, FRR 0.07%, and finally fisherface got the highest average with 90.00% accuracy, 0.05% FAR, 0.05% FRR, in identifying personal. The results obtained by the Fisherface method tend to have the highest accuracy value based on the average both in terms of accuracy and the lighting conditions that have been tested.
DRONE SIMULATION FOR MILITARY SURVEILLANCE IN THE NORTH-EAST OF NIGERIA

The paper presents new simulation tools that are capable of controlling, coordinating, manipulating, detecting and tracing of drones in the North-East of Nigeria where the activities of terrorist group popularly called Boko Haram is highly dominated. Simulation of the environment for a modern human being has become necessity allowing him to increase his work efficiency and ease of comfort. There has been immense development in the area of an individual's routine tasks and those can be simulated. Simulation is an imitation of the operation of a real-world process or system. Simulation can be used in many contexts, such as performance optimization, testing, training, education and video games. This program we intend to develop will show the eventual real effects of alternative conditions and courses of action. We will use simplified approximations and assumptions within the simulation, fidelity and validity of the simulation outcomes. The simulation process is done using Object-Oriented simulation tools. The process is monitored on the screen or display for clarity purposes for onward satisfaction of the viewer.
말타(Malta) 나선문양의 문명사적 상징 연구 - 몽골어번역에 나타난 오류의 유형화 시도 -

In 1928, the Malta ruins, located 80 km north of Irkutsk, were radiocarbon dating revealed to be the remains of the last ice age, 21,000 BP. More than 30 human-shaped dolls made of mammoth ivory, 50 bird-shaped sculptures, and spiral hole plaques were unearthed in this ruins. In particular, the spiral hole plaque attracted attention religiously as a spiral pattern that first appeared in the history of human art. This indicates that the people of this site created and adhered to something of a community philosophy. This paper examines the meaning of the spiral hole plaque through the analysis of the religious philosophical symbolism of the Malta spiral pattern and the location of civilization in Malta. As a result, Malta has emerged as a cross-breed area of culture and race, as well as a passage connecting east, west, north and south in civilization. In addition, it was found that the existence of swan sculpture, snake, spirit dolls, including spiral hole plaques, are reflected in northern shamanismincluding Buriad today. This shows that Malta''s ancient system of thought forms the basis of the Northern Cultural Archetype
Semi-automated image data labelling using AprilTags as a pre-processing step for machine learning

Data labelling is a pre-processing step to prepare data for machine learning. There are many ways to collect and prepare this data, but these are usually associated with a greater effort. This paper presents an approach to semi-automated image data labelling using AprilTags. The AprilTags attached to the object, which contain a unique ID, make it possible to link the object surfaces to a particular class. This approach will be implemented and used to label data of a stackable box.The data is evaluated by training a You Only Look Once (YOLO) net, with a subsequent evaluation of the detection results. These results show that the semi-automatically collected and labelled data can certainly be used for machine learning. However, if concise features of an object surface are covered by the AprilTag, there is a risk that the concerned class will not be recognized. It can be assumed that the labelled data can not only be used for YOLO, but also for other machine learning approaches.
Unsupervised abstractive summaries of controllable length

The Canadian company Croesus offers a virtual dashboard for financial advisors. Their flagship product, a software called Croesus Advisor, brings together financial portfolio management and customer relationship management (CRM) functionalities.Croesus is leading a Research & Development (R&D) project whose aim is to automate the generation of reports about the performance of financial portfolios over a given period. The generated reports will have to measure and explain variations of portfolio performances with respect to benchmark market(s) using natural language. Such a project is in the domain of Natural Language Generation (NLG).This R&D project involves more than one research problem. First, the time series describing the different assets in a given portfolio must be analyzed in order to identify significant events. Then, the publications describing these events must be identified and retrieved. Finally, the sequence of publications describing significant events should be summarized in a coherent and informative narrative of controllable length. This work focused on a particular aspect of the R&D project, that is, the unsupervised training of automatic abstractive summarization models with the capacity to control their output’s length.Experiments were performed using Recurrent Neural Network and Transformers trained with and without supervision. A Transformer-based model trained without supervision yielded ROUGE-1, ROUGE-2 and ROUGE-L F1 scores of 21.29, 6.4 and 19.41, respectively. Furthermore, a similar model trained in a supervised fashion achieved ROUGE-1, ROUGE-2 and ROUGE-L F1 scores of 40.27, 18.46 and 37.59. To the best of this author’s knowledge, the model trained under supervision constitutes the current state-of-the-art in abstractive summarization of controllable length.These results, alongside research conducted in parallel to this work, mitigated the risk with respect to the automatic summarization aspect of the R&D project to a tolerable level. Work on the remaining research problems of the project is set to start soon.
Single Stage Class Agnostic Common Object Detection: A Simple Baseline

This paper addresses the problem of common object detection, which aims to detect objects of similar categories from a set of images. Although it shares some similarities with the standard object detection and co-segmentation, common object detection, recently promoted by \cite{Jiang2019a}, has some unique advantages and challenges. First, it is designed to work on both closed-set and open-set conditions, a.k.a. known and unknown objects. Second, it must be able to match objects of the same category but not restricted to the same instance, texture, or posture. Third, it can distinguish multiple objects. In this work, we introduce the Single Stage Common Object Detection (SSCOD) to detect class-agnostic common objects from an image set. The proposed method is built upon the standard single-stage object detector. Furthermore, an embedded branch is introduced to generate the object's representation feature, and their similarity is measured by cosine distance. Experiments are conducted on PASCAL VOC 2007 and COCO 2014 datasets. While being simple and flexible, our proposed SSCOD built upon ATSSNet performs significantly better than the baseline of the standard object detection, while still be able to match objects of unknown categories. Our source code can be found at \href{https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection}{(URL)}
Peguasaan Kosakata Bahasa Mandarin Tingkat HSK I pada Siswa Sekolah Menengah Atas

Abstract. This research aims to describes the mastery of Mandarin language vocabulary at HSK level I of 11th grade students of SMA Dian Harapan Makassar. The method used of this research was to conduct a vocabulary mastery test which consisted three kinds of tests, that is vocabulary translation test, sentence completion test and matching characters of Hanzi with Pinyin test which analyzed using the percentage formula, The results reached 96.4% with the conclusion that mastery of Mandarin vocabulary at HSK level of 11th grade students of SMA Dian Harapan was included in the perfect category.
Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions.

This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. For many low-resource and endangered languages, only single-speaker recordings may be available, demanding a need for domain and speaker-invariant language ID systems. In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results for the language identification task.