Reviewerly/exHarmony huggingface dataset:
SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes

Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they often suffer from dataset biases (due to not being able to include all possible imaging conditions). In this paper, a combination of the two is proposed. Component specific priors like semantics and invariant features are exploited to obtain semantically and physically plausible reflectance transitions. These transitions are used to steer a progressive CNN with implicit homogeneity constraints to decompose reflectance and shading maps. An ablation study is conducted showing that the use of the proposed priors and progressive CNN increase the IID performance. State of the art performance on both our proposed dataset and the standard real-world IIW dataset shows the effectiveness of the proposed method. Code is made available here.
Research on Air-conditioning Operation Energy Saving in Internet Data Center

In recent years, China's internet data center (IDC) has grown rapidly. At the same time of rapid development, the problem of high energy consumption in IDC has been highlighted. Reducing the energy consumption of IDC cooling equipment has a profound impact on energy saving. This paper analyzes the energy saving of the IDC room by raising the air conditioning temperature and testing the change of temperature and humidity of the cold channel. The test found that for every 1°C increase in air conditioning temperature, IDC room air conditioning energy consumption is reduced by 12.9% ~ 14.0%. This paper is a guide to the construction of green data centers and the energy-efficient operation of IDC room air conditioners.
Indonesian Language Learning Assistance for Foreign Speakers (BIPA) at the Indonesian Embassy in Ankara, Turkey

Indonesian until now has been taught to foreigners in various institutions both at home and abroad. This learning program is known as the Indonesian for Foreign Speakers (BIPA). This study aims to give an assistance of Indonesian language learning for foreign speakers of turkey. During the Covid-19 pandemic, BIPA learning was carried out online. Turkey is one of the countries whose citizens have a considerable interest in studying Indonesian. The researchers used Service Learning (SL) approach (Felten, &amp; Clayton, 2011). It as a medium for integrating voluntary community service with active reflection which is expected to enrich and strengthen the material about bringing oral storytelling in developing students and teachers. The method of this study is by giving an assistance of Indonesian language learning. The BIPA program at the Indonesian Embassy in Ankara Turkey has been going on for 2 periods. In the 2nd period, which was implemented from October 9 to December 15, 11 classes and 4 BIPA levels have been carried out well. This is evidenced by the results of the final examination of the program obtained by the learners, namely by increasing the Indonesian language competence of the learners. The results obtained by the learners at the end of the program showed that their Indonesian language competence was improved. Thus, in the future this program is expected to continue so that more learners can take part in the BIPA program implemented at the Indonesian Embassy in Ankara, Turkey either online or in-person learning. Thus, the BIPA learning program can continue to be implemented in the future.
Review of Basic Principles of Embodiment Design

Abstract: A number of technical research fields have grown and matured over decades through the investigation, study, experimentation, and validation of core principles. Accepted research methodologies and standards similarly emerge and mature, but also tailored to the characteristics and scope of the field. The life and physical sciences are classical examples of this growth and maturation process. Principles of embodiment design is a relatively young field of research investigation. With the first treatises published around the mid-twentieth century, it has grown steadily in the devoted attention and depth of investigation. Because of the broad and interdisciplinary or trans-disciplinary nature of design principles, numerous forms have been suggested across disciplines, between disciplines, and at various levels of granularity or specificity. The time is now apparent to carefully study these efforts, seeking a formaliz��ation of embodiment design principles, definitions, and supporting research methodologies. In this review, I seek to make strides in formalizing principles of embodiment design in terms of the various disparate approaches. Ultimately, this review assists in enabling a fundamental understanding and development of embodiment design principles, and associated processes, as well as guiding researchers and practitioners in advancements and use of such principles
Монгол-Aнгли орчуулга хийдэг нейрон сүлжээнд суурилсан машины орчуулгын гурван шатлалт загвар боловсруулах

The widespread use of neural machine translation has the advantage of allowing users to translate terms and translate untrained data to a certain extent, but in some cases often results in distorted sentence structure. This study aims to address issues such as neural machine translation control, high-probability translation of unrecognized data, correct sentence structure, beginning and ending recognition, and the establishment of an independent, machine translator in one's home country. We have made improvements to the neural network model, such as adjusting neural machine translation to unidentified words in subunits, and defining sentence boundaries and scope. The design is based on the usual PMT and SMT templates used to compare words in a system that takes into account word and sentence structure. However, the model we developed is based on the latest neural machine translation (NMT) architecture, which can make more complex relationships. In this sense, this work can be seen as an attempt to use a combination of statistical machine translation and neural machine translation. We sought and tested in practice a step-by-step approach to combining complex deep neural network models that included longer contexts in a system that considered only short contexts in terms of word and sentence structures.
NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges

NICO challenge of out-of-distribution (OOD) generalization for image recognition features two tracks: common context generalization and hybrid context generalization, based on a newly proposed OOD dataset called NICO $$^{++}$$ . Strong distribution shifts between the training and test data are constructed for both tracks. In contrast to the current OOD generalization benchmarks where models are tested on a single domain, NICO challenge tests models on multiple domains for a thorough and comprehensive evaluation. To prevent the leakage of target context knowledge and encourage novel and creative solutions instead of leveraging additional training data, we prohibit the model initialization with pretrained parameters, which is not noticed in the previous benchmarks for OOD generalization. To ensure the random initialization of models, we verify and retrain models from all top-10 teams and test them on the private test data. We empirically show that pretraining on ImageNet introduces considerable bias on test performance. We summarize the insights in top-4 solutions, which outperform the official baselines significantly, and the approach of jury award for each track.
Programming Language Processing : How AI can Revolutionize Software Development?

The past decade has seen unprecedented growth in Software Engineering— developers spend enormous time and effort to create new products. With such enormous growth comes the responsibility of producing and maintaining quality and robust software. However, developing such software is non-trivial— 50% of software developers’ valuable time is wasted on finding and fixing bugs, costing the global economy around USD$1.1 trillion. Today, I will discuss how AI can help in different stages of the software development life cycle for developing quality products. In particular, I will talk about Programming Language Processing (PLP), an emerging research field that can model different aspects of code (source, binary, execution, etc.) to automate diverse Software Engineering tasks, including code generation, bug finding, security analysis, etc.
Dynamically Active Barriers to Transport

In the preceding chapters, we have discussed definitions and identification techniques for observed material barriers to the transport of fluid particles, inertial particles and passive scalar fields. All these barriers are directly observable in flow visualizations based on their impact on tracers carried by the flow. However, the transport of several important physical quantities, such as the energy, momentum, angular momentum, vorticity and enstrophy, is also broadly studied but allows no direct experimental visualization. These important scalar and vector quantities are dynamically active fields, i.e., functions of the velocity field and its derivatives. We will collectively refer to barriers to the transport of such fields as dynamically active transport barriers. This chapter will be devoted to the development of an objective notion of active barriers in 3D unsteady velocity data. Additionally, 2D velocity fields can also be handled via this approach by treating them as 3D flows with a symmetry.
CRT-500.15 Understanding and Improving Healthcare Delivery in South Asia: A Global Health Perspective

One of the primary objectives of this paper is to establish compatibility between two different jet space functors in the most general context. We first show an adjunction between the jet and the Witt functor on algebras. Following Borger's approach, we then construct the algebraic jet space functor in the general setting where the base is an arbitrary prolongation sequence. We then show that this functor is representable in the category of schemes and that Buium's jet space can be recovered by the π-adic completion of this representable scheme. As an application, this allows us to strengthen a result of Buium on the relation between Greenberg's transform and the special fiber of jet spaces, including the ramified case.
A New Risk Model based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit

Background: Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine-learning risk prediction model for mortality in respiratory intensive care units (RICUs). Methods: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from 2012 to 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. We constructed eXtreme Gradient Boosting (XGBoost) models and compared performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. The model was externally validated using data from Medical Information Mart for Intensive Care (MIMIC-III) database. A web-based calculator was developed for practical use. Results: Among the 1,063 visits, the RICU mortality rate was 13.5%. The XGBoost model achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.860 (95% confidence interval (CI): 0.808 - 0.909) in the test set, which was significantly greater than APACHE II (0.749, 95% CI: 0.674 - 0.820; P = 0.015) and SOFA (0.751, 95% CI: 0.669 - 0.818; P = 0.018). The Hosmer-Lemeshow test indicated a good calibration of our predictive model in the test set with a P-value of 0.176. In the external validation dataset, the AUROC of XGBoost model was 0.779 (95% CI: 0.714 - 0.813). The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NTproBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; P &lt; 0.001). Conclusions: Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that higher NT-proBNP can be a good indicator of poor prognosis.
Levels of methodological competence in foreign language teaching

The notions “methodological competence” and “methodological competency” are differentiated in the scientific literature but their use in the methodological works is not always appropriate according to their essence. There is no gradation of these terms to different levels depending on knowledge, skills and abilities in teaching if to speak about people having different scientific and methodological potential. Therefore, it brings to the necessary to determine the level of methodological competence/competency of pedagogues who teach foreign languages in preschool, secondary, higher educational institutions and scholars-didacts and methodists. Due to the limited number of pages of a scholar paper only the problem of “methodological competence” differentiation is highlighted in the presented work. Purpose is to determine the levels of methodological competence of foreign language teachers with different scientific and methodological potential. Methods. Literature review on the topic of the research, comparative analysis of different definitions of methodological competence, generalization of the scholars-didacts and methodists’ experience. Results. This study analyses the notion “methodological competence” and determines its definition. Three levels of methodological competence have been developed and substantiated. The precise distinction between three levels has been defined and described taking into account the content of seven links of the teaching process introduced by us in the work. The first level of methodical competence correlates with the first level of language and speech competences (Martynova, Bodnar, 2022), as well as with the ability of calendar and lesson planning; the second correlates with the second level of language and speech competences, as well as with the ability to plana foreign language teaching process during the academic year and semesters, with the subsequent composing professional educational programs and syllabus; the third correlates with the third level of language and speech competences, as well as with the ability to plan foreign language self-study process, both in language and communicative and speech aspects, to the possibility of students creating their own speech products in a foreign language. Conclusion. It has been concluded that three established levels of methodological competence will clarify the essence of this concept, detail its components and become objective characteristics of educators’ methodological knowledge and skills.
Blockchains and Cross-Blockchains: Privacy-Preserving Techniques

Blockchain is a new and revolutionary technology whose use is increasing in recent years. The blockchain network has recently been used in a wide variety of scenarios far beyond digital currencies, such as the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and healthcare, where the need for collaboration between different blockchains is critical. The concept of blockchain interoperability, which means connecting multiple blockchain networks, has attracted the attention of many researchers. However, achieving interoperability between blockchains is a challenge for academics and the industry, as concerns about security and privacy are very evident in blockchain interoperability. Although there have been studies on blockchain privacy protection, cross-blockchain privacy protection is still in its infancy and more research is needed. In this article, we examine and compare the privacy protection provided for blockchain and cross-blockchain. We hope this work will provide future researchers with insight into blockchain and cross-blockchain privacy protection technologies.
Numerical Methods with Python

Introduces students to appropriate use of computer programming within the scientific disciplines using Python. Discusses several common applications of programming and implementation using real world examples and hands on programming exercises. Students learn how to model situations such as image recognition, medical diagnosis, spread of disease, and others.
Collaborative possibilistic fuzzy clustering based on information bottleneck

In fuzzy clustering algorithms, the possibilistic fuzzy clustering algorithm has been widely used in many fields. However, the traditional Euclidean distance cannot measure the similarity between samples well in high-dimensional data. Moreover, if there is an overlap between clusters or a strong correlation between features, clustering accuracy will be easily affected. To overcome the above problems, a collaborative possibilistic fuzzy clustering algorithm based on information bottleneck is proposed in this paper. This algorithm retains the advantages of the original algorithm, on the one hand, using mutual information loss as the similarity measure instead of Euclidean distance, which is conducive to reducing subjective errors caused by arbitrary choices of similarity measures and improving the clustering accuracy; on the other hand, the collaborative idea is introduced into the possibilistic fuzzy clustering based on information bottleneck, which can form an accurate and complete representation of the data organization structure based on make full use of the correlation between different feature subsets for collaborative clustering. To examine the clustering performance of this algorithm, five algorithms were selected for comparison experiments on several datasets. Experimental results show that the proposed algorithm outperforms the comparison algorithms in terms of clustering accuracy and collaborative validity.
Open Source Responsibilities

Early one July morning, my husband and I were sipping our coffee and catching up on news when he made a comment that I couldn't ignore. He said something to the effect of: "what an immature response".
Implementation of efficient data storage management using Common Corporate Social Responsibility System

In the proposed system is overcoming the drawbacks of another available CSR systems by providing the COMMON CSR System for CSR activities with efficient storage management in place of manual record maintenance. Currently, there is no available dataset of various CSR activities, therefore the record of these activities are a big concern, and this system provides the record maintenance of data efficiently. One of the incidents of attack on world trade centre, New York (2001), emphasizes us to think about data losses and recovery. Data loss is a major inconvenience that disrupts many functions of corporate activities entertained. This system brings the main focus on efficient storage management by using Excel Sheet because it is convenient to recover data and if required, it is easy to convert it in any other format. This System automatically deletes the data after any specified time period and transfer it in an Excel Sheet. The storage in the Excel Sheet can be used for data analysis and other related activities to extract valuable information. This Common CSR system also provides a user-friendly environment for a better interaction and assistance to the new user through a Chatbot.
Article Writing

The scientific article is the gold standard of scientific publishing. This is indisputable for basic research. To a great extent, it also applies to technology science. Therefore, it makes good sense to emphasize article writing in a book like this.
Evaluation of Existential Hypotheses

Existential hypotheses are most common early in the research process. They mostly appear as working hypotheses that are subsequently refined or reformulated in universal or statistical form. Occasionally, however, also existential hypotheses are exposed to detailed evaluations. In this chapter, we explain how to proceed in such cases.
Evaluation of Universal Hypotheses

When our ideas are fruitful and result in some artifact that seems promising, we move on to the evaluation phase. The goal then is to assess if or to what extent the artifact needs have been fulfilled. The basis for the evaluation can be an artifact description, a model, or a prototype, depending on how far we have got in the research process and the selected evaluation method.
Web of Conferences: A Conference Knowledge Graph

Academic conferences have been proven to be significant in facilitating academic activities. To promote information retrieval specific to academic conferences, building complete, systematic, and professional conference knowledge graphs is a crucial task. However, many related systems mainly focus on general knowledge of overall academic information or concentrate services on specific domains. Aiming at filling this gap, this work demonstrates a novel conference knowledge graph, namely Web of Conferences. The system accommodates detailed conference profiles, conference ranking lists, intelligent conference queries, and personalized conference recommendations. Web of Conferences supports detailed conference information retrieval while providing the ranking of conferences based on the most recent data. Conference queries in the system can be implemented via precise search or fuzzy search. Then, according to users' query conditions, personalized conference recommendations are available. Web of Conferences is demonstrated with a user-friendly visualization interface and can be served as a useful information retrieval system for researchers.
Student Behavior Pattern Mining and Analysis: Towards Smart Campuses

Understanding student behavior patterns is fundamental to building smart campuses. However, the diversity of student behavior and the complexity of educational data not only bring great obstacles to the relevant research, but also leads to unstable performance and low reliability of current student behavior analysis systems. The emergence of educational big data and the latest advances in deep learning and representation learning provide unprecedented opportunities to tackle the above problems. In this talk, we introduce how we mine and analyze student behavior patterns by overcoming the complexity of educational data. Specifically, we propose a series of algorithmic frameworks, which take advantage of network science, data mining, and machine learning to form a data-driven system for mining and analyzing student behavior patterns. Our research not only fills the gap in the field of student abnormal behavior warning and student status monitoring, but also provides insights into data-driven smart city construction.