Reviewerly/exHarmony huggingface dataset:
Informative eco-geochemical assessment of soil layer pollution in Chisinau during the peak period of industrial activity

We have carried out a retro-assessment of the data on metal pollution of soils in the city of Chisinau dated by the peak of industrial activity in the late 80s of the twentieth century. The proximity of geochemical spectra in soils of industrial areas and in children's institutions was observed. Statistical analysis of the main components indicated a high specialization of metal pollution in the industrial areas. Soil pollution in areas of children's institutions had a clear residential-transport character. The elimination of a multisectoral industrial production, the redevelopment of industrial areas and an increase in traffic intensity in the city over the past 30 years should have led to a significant change in the spatial structure and spectrum of pollution in the city of Chisinau.
The 1st International Workshop on Federated Learning with Graph Data (FedGraph)

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple ''data silos", among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs. øurs aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.
Revisiting Cold-Start Problem in CTR Prediction

Click-through rate (CTR) prediction is one of the core tasks in industrial applications such as online advertising and recommender systems. However, the performance of existing CTR models is hampered by the cold-start users who have very few historical behavior data, given that these models often rely on enough sequential behavior data to learn the embedding vectors. In this paper, we propose a novel framework dubbed GF2 to alleviate the cold-start problem in deep learning based CTR prediction. GF2 augments the embeddings of cold-start users after the embedding layer in the deep CTR model based on the Generative Adversarial Network (GAN), and the obtained generator by GAN can be further fine-tuned locally to enhance the CTR prediction in cold-start settings. GF2 is general for deep CTR models that use embeddings to model the features of users, and it has already been deployed in real-world online display advertising system. Experimental results on two large-scale real-world datasets show that GF2 can significantly improve the prediction performance over three polular deep CTR models.
<scp>SmartQuery:</scp> An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction

Graph neural networks have achieved significant success in representation learning. However, the performance gains come at a cost; acquiring comprehensive labeled data for training can be prohibitively expensive. Active learning mitigates this issue by searching the unexplored data space and prioritizing the selection of data to maximize model's performance gain. In this paper, we propose a novel method SMARTQUERY, a framework to learn a graph neural network with very few labeled nodes using a hybrid uncertainty reduction function. This is achieved using two key steps: (a) design a multi-stage active graph learning framework by exploiting diverse explicit graph information and (b) introduce label propagation to efficiently exploit known labels to assess the implicit embedding information. Using a comprehensive set of experiments on three network datasets, we demonstrate the competitive performance of our method against state-of-the-arts on very few labeled data (up to 5 labeled nodes per class).
My husband, do you want our family (韓) to be miserable (弊)? Please give me your wife a lot of (煩) agony (刑), then, it will be so. Our family will be surely diminished and be poor! (Tcheonzamun 577th-592nd)

French missionary Dallet (1874) wrote in his book that ‘The thousand character essay’ (It is called as ‘Tcheonzamun’ in Korean language) had been used as a textbook for Chinese character education for children and Dallet (1874) described that this same book had been utilized both in the ancient China and in the ancient Korea. For the present researcher, there were two methods for the translation. The first method for the translation is through Korean pronunciation of the Chinese characters on the Tcheonzamun poem. The second method is through the meaning of Chinese characters of the poem. The title of this study is ‘My husband, do you want our family (韓) to be miserable (弊)? Please give me your wife a lot of (煩) agony (刑), then, it will be so. Our family will be surely diminished and be poor!’ And the next is the result of the research of the poem (Tcheonzamun 577th-592nd).
Application of K-Means for Clustering Based on the Severity of COVID-19 in Indonesian Private Hospitals

In December 2019, coronavirus (COVID-19) caused by SARS-CoV-2 was first discovered in Wuhan, China. This virus has a high transmission rate and can be transmitted through droplets, airborne, and aerosols. The clinical manifestations are very diverse ranging from mild, moderate, and severe. Therefore, this study aims to conduct a clustering of the spread of the Covid-19 pandemic to facilitate the identification and handling. The method of the K-Means algorithm can be used as a method to obtain the desired clustering. The implementation and evaluation were conducted using RapidMiner tools and Davies Bouldin Index (DBI) respectively. Furthermore, the data sources by Kangdra (2020) were used with a total sample of 110 for the period March-June 2020. The results showed that the optimal cluster is located at k: 2 with a DBI value: 0,094 as the lowest value. Therefore, the cluster is strong since a smaller DBI value gives a better cluster. The clustering obtained is Cluster 1 and 2 with mild and moderate severity. The results are expected to facilitate a better zone identification of the COVID-19 severity level and rising people awareness.
36.3: A Dynamic Contrast Enhancement Method for TV Picture Quality Improvement

Images or videos displayed in TV usually have undesirable contrast, due to different imaging environments or imaging devices. There are numerous contrast adjustment methods in TV picture quality improvement solutions. However, most of these methods produce undesirable enhancement results. In order to deal with this problem, a contrast enhancement method is proposed to dynamically improve picture quality for TV display. This method uses histogram distribution and APL (Average Pixel Level) of the considered image to compute the corresponding adjust curve. Then the obtained mapping curve is applied to the whole image for improving contrast. The proposed method has been applied on a smart TV. Results demonstrate this method has good performance in terms of enhancing image contrast and maintaining image naturalness.
Breast Tumor Classification in Digital Tomosynthesis Based on Deep Learning Radiomics

Breast cancer is the most frequently diagnosed cancer in women globally. Early and accurate detection and classification of breast tumors are critical in improving treatment strategies and increasing the patient survival rate. Digital breast tomosynthesis (DBT) is an advanced form of mammography that aids better in the early detection and diagnosis of breast disease. This paper proposes a breast tumor classification method based on analyzing and evaluating the performance of various of the most innovative deep learning classification models in cooperation with a support vector machine (SVM) classifier for a DBT dataset. Specifically, we study the ability to use transfer learning from non-medical images to classify tumors in unseen DBT medical images. In addition, we utilize the fine-tuning technique to improve classification accuracy.
Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features

Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.
Combining Support and Attack Interactions for Argumentation Based Discussion Analysis

Online discussion is one of the commonly used tools to engage users to discuss relevant topics for society, where user contributions in the form of posts, comments and votes are essential to their success. The scale, complexity and dynamism of this information leads to a growing interest in understanding what are the major accepted or rejected opinions in different domains by social network users. In this work, we explore how to combine attack and support interactions to extract possible consensus based on some abstract argumentative semantics, and we characterize the set of properties or postulates that this consensus should satisfy.
Towards Cross-Sites Generalization for Prostate MRI Segmentation to Unseen Data

Learning a model from multi-source data is a challenging and topical learning problem. Thus, generalization capacity has been proposed to deal with the domain shift (i.e. various imaging vendors, modalities, and protocols) across domains. This paper tackles the out-of-distribution generalization for prostate segmentation in MRI imaging. We propose a simple approach based on the pretraining-finetuning scheme to boost the deep neural network’s generalization to unseen data in prostate MRI segmentation. This paper introduces an objective loss that seeks to minimize cross-domain distribution by adapting Kullback–Leibler (KL) divergence. To manifest the effectiveness of our approach, we perform experiments on a multi-source public dataset for prostate MRI imaging collected from six vendors. As a result, the proposed model can yield promising cross-domains generalization capacity to unseen target domain.
Argumentation Reasoning with Graph Isomorphism Networks for Reddit Conversation Analysis

Abstract The automated analysis of different trends in online debating forums is an interesting tool for sampling the agreement between citizens in different topics. In previous work, we have defined computational models to measure different values in these online debating forums. One component in these models has been the identification of the set of accepted posts by an argumentation problem that characterizes this accepted set through a particular argumentation acceptance semantics. A second component is the classification of posts into two groups: the ones that agree with the root post of the debate, and the ones that disagree with it. Once we compute the set of accepted posts, we compute the different measures we are interested to get from the debate, as functions defined over the bipartition of the posts and the set of accepted posts. In this work, we propose to explore the use of graph neural networks (GNNs), based on graph isomorphism networks, to solve the problem of computing these measures, using as input the debate tree, instead of using our previous argumentation reasoning system. We focus on the particular online debate forum Reddit, and on the computation of a measure of the polarization in the debate. We explore the use of two different approaches: one where a single GNN model computes directly the polarization of the debate, and another one where the polarization is computed using two different GNNs: the first one to compute the accepted posts of the debate, and the second one to compute the bipartition of the posts of the debate. Our results over a set of Reddit debates show that GNNs can be used to compute the polarization measure with an acceptable error, even if the number of layers of the network is bounded by a constant. We observed that the model based on a single GNN shows the lowest error, yet the one based on two GNNs has more flexibility to compute additional measures from the debates. We also compared the execution time of our GNN-based models with a previous approach based on a distributed algorithm for the computation of the accepted posts, and observed a better performance.
Cryptocurrencies and Technology Indices: A Relationship and Causality Analysis

This article analyzes the relationship between cryptocurrencies and technology indices. The dataset includes eight different cryptocurrencies selected based on the traded volume and fifteen country technology indices. The analysis is carried out using multivariate regression of each of the cryptocurrencies vs. the technology indices, and Granger causality test. Results show that there exists a significant relationship between cryptocurrencies and different technology indices. We also show that the relationship fails between cryptocurrencies and technology indices if there is a complete ban on trading in cryptocurrencies in a country, and even if there is a partial ban, certainty of laws, integration, and acceptance of the cryptocurrency in the system plays a key role to establish relationship between cryptocurrencies and various technology indices. These findings provide an insight into the cryptocurrency regulations and trades undertaken by speculators.
A human-in-the-loop recommendation-based framework for reconstruction of mechanically shredded documents

The advances in machine learning – particularly in deep learning – have enabled automatizing the reconstruction of shredded documents with significant accuracy. However, despite the recent remarkable results, the state-of-the-art on fully automatic reconstruction still has room for improvement, mainly due to imprecision on the evaluation of how the shreds fit each other (compatibility/cost evaluation). To tackle this problem, we propose a human-in-the-loop reconstruction framework that takes user inputs to improve the solutions (permutation of shreds). In our approach, the user verifies whether adjacent shreds of a solution are also adjacent in the original document. Unlike the current literature, our framework includes a recommender module that automatically selects pairs of shreds to be analyzed by a human. Four recommendation strategies were proposed and evaluated. Results achieved by coupling deep learning reconstruction methods into our framework have shown that introducing the human in the loop can reduce errors by more than 40%.
The Establishment and Development Sidelight of the Development Geography Professional Committee of the Geographical Society of China

The Development Geography Professional Committee is emerged under the macro background that much global attention is paid to development issues, the status of development geography becomes increasingly prominent, and the theory of development geography develops rapidly with increasingly rich achievements. This paper first introduces the background and organization of the Development Geography Professional Committee of the Geographical Society of China (hereafter referred to as the Committee), and reviews the establishment and development process of the Committee.Then, it summarizes a series of achievements made by the Committee in setting up academic platforms, promoting academic exchanges, and promoting talent training.In November 2015, the Industrial Policy and Development Geography Working Group of the Geographical Society of China was established and it held the first academic seminar in Beijing.Experts, scholars and leaders of relevant scientific and technological management departments from nearly 30 scientific research institutions, universities (departments), enterprises and public institutions were invited.In 2019, the Development Geography Professional Committee was formally established. The Committee has always clearly defined its orientation, given consideration to both the basic and pioneering aspects, and actively built academic platforms.It has founded journals such as "Frontier of Development Geography", Journal of Development Geography, compiled the monograph "Introduction to Development Geography" and other works.The Committee has always adhered to its original orientation, giving consideration to both ideological and academic aspects, and has held eight annual academic conferences and organized four large international/domestic conference parallel sessions.The Committee has always paid attention to long-term development and educational practice, actively promoted talent training by organizing education and training, offering development geography courses and compiling development geography textbooks, etc.Looking to the future, the Development Geography Professional Committee will closely focus on major scientific issues, consolidate various working foundations, strive to form. a scientific research paradigm that the direction of development geography matches the needs of national major strategies, explore a new mode of regional coordination and sustainable development and the new path of quality industrial policy transformation, and promote the development of development geography subject, to realize its arduous historical mission.
ANALISIS PEMBANGUNAN BENDUNGAN DIGOEL DI KABUPATEN BOVEN DIGOEL DALAM RANGKA PENGEMBANGAN WILAYAH DI PAPUA

One of the pillars of the Food Sovereignty program is the availability of irrigation infrastructure and realizing rice selfsufficiency in Papua Province and Boven Digoel Regency in particular. The concrete effort is to program the development of new water infrastructure with optimum service capacity and the development of potential areas to meet food availability. Merauke River Departement, programmed the Construction of the Digoel Dam in Boven Digoel Regency. This study aims to analyze the development strategy of the Digoel Dam, which includes technical aspects, land use and environment, economic and socio-cultural aspects, benefits and regional development, and asset investment aspects. The method of analysis is quantitative and qualitative. Results study concluded that the technical aspects were carried out through good planning, implementation and supervision of construction. Aspects of land use and the environment, through land use in accordance with the spatial plans of Boven Digoel Regency, development and management that is environmentally sound. Economic and socio-cultural aspects, namely the existence of the Digoel Dam must provide economic benefits to increasing the income and welfare of the community whose social work is farming, raising livestock, fishing and trading. The benefits aspect of the Digoel Dam is as a source of water for irrigation with a potential area of 6,090 Ha, a source of raw water for the surrounding community with a capacity of 32.25 liters/second, a power plant with a planned total electricity generation of 13.96 MW, reducing the potential for flooding in the surrounding area and the potential for flooding in the surrounding area and tourism development to improve the local economy. Aspects of regional development by encouraging the development of the Boven Digoel Region which is supported by sufficient food security. Aspects of asset inventory, namely the existence of the Digoel Dam must provide beneficial benefits from its existence as an economic asset, historical asset and tourism asset.
Review of Security Principles and Security Functions in Critical Information Infrastructure Protection

There is quite a lot of research on Critical Information Infrastructure Protection (CIIP), but what threats and vulnerabilities are considered in the security principle offered is unknow. Likewise, the security functions provided have not been measured. This study is a review of CIIP using the Kitchenham framework. From 31 scientific publications and 5 CIIP standards, it was found that there were 13 threats and 16 vulnerabilities were categorized into three security principles. As a result of measuring security functions on CIIP, we found that only 25% provide all security functions.
Lei Lei and Dilin Liu, Conducting Sentiment Analysis. Cambridge University Press, 2021. pp. 104. ISBN 978-1-108-82921-2 (paperback), 978-1-108-90967-9 (E-book)

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Designing E-comic for English Reading Material for Grade Eight in Tanjungpinang

The research aimed to design an e-comic for English Reading Material for online learning at grade eight of SMPN 11 Tanjungpinang. The design of e-comic for English reading material can be considered to make the students interested and motivated in learning English during online environment. This research focused on designing English reading material about recount text for grade eight. The research design was R&amp;D based on ADDE by Richey and Klein consisted of analyze, design, develop, and evaluation. The subject of this research was the 8.4 class students which consisted of 25 students. The instruments were interview and questionnaires. The researcher was analyzed the data by using qualitative and quantitative techniques. The results of the research showed that the practicality of e-comic was very high (91.5%). It can be concluded that the e-comic for English reading material was interested, motivated, and contextualized to learn by the students at SMPN 11 Tanjungpinang.