Reviewerly/exHarmony huggingface dataset:
Bim-Based Artificial Engineering Integration Method for Building Engineering Database

Abstract In order to perfect the database system of building engineering and meet the demand of data processing and integration, a BIM - based artificial intelligence system integration research method of building engineering database was designed. The role of artificial intelligence integration method in building engineering database system was deeply analyzed, based on which the effectiveness of BIM based artificial intelligence integration method in building engineering database was further discussed. After experimental verification, the bim-based artificial intelligence integration method of building engineering database was more conducive to further improving the efficiency of architectural engineering, effectively reducing the design cycle of architectural engineering and realizing the optimization design of architectural engineering. And its integration efficiency was about 30% higher than the traditional integration method.
Personalization of Deep Learning

We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. We show that both ``curriculuum learning'' and ``personalized'' data augmentation lead to improved performance on data of an individual. Mostly, this comes at the cost of reduced performance on a more general, broader dataset.
Augmented/Virtual Reality Promises for ELT Practitioners

Augmented reality (AR) and virtual reality (VR) are expanding into various fields of education. Thanks to AR and VR, teachers can bring a whole new dimension to the realm of language education, resulting in enhanced learning and increased motivation and engagement. With smartphones and inexpensive VR headsets, teachers are able to take a step forward in blurring the boundaries between the real and virtual worlds, giving their students immersive learning experiences. However, many ELT practitioners are not familiar with or keen on integrating AR/VR-related learning tools and resources into their classes (Bonner &amp; Reinders, 2018; Santos et al., 2016). To get readers more acquainted with AR/VR uses in language education, the author first outlines the basic concepts within this area of research and practice. An AR/VR application is introduced in detail, followed by discussion on the use of this app in language learning. 拡張現実（AR）および仮想現実（VR）は絶えず様々な教育分野に拡大している。ARとVRによって、学習の強化やモチベーションとエンゲージメントの向上が可能となり、言語教育の領域に全く新しい次元をもたらすことができる。スマートフォンや安価なVRヘッドセットの使用によって、現実世界と仮想世界の境界を曖昧にすることで、学生は没入型の学習体験を得られる。しかし、ELT実務家の多くは、AR・VR関連の学習ツールとリソースを教育実践に統合する方法に馴染みがなかったり、統合自体に関心がない現状がある（Bonner &amp; Reinders, 2018; Santos et al., 2016）。読者の言語教育におけるAR・VRの使用法に関する詳細な理解を深めるために、まずこの分野の研究と実践の基本的な概念を概説する。また、AR及びVRアプリケーションについて詳しく説明した上で、このアプリケーションを言語学習に使用する方法について説明する。
Relation-Aware Global Attention for Person Re-identification

For person re-identification (re-id), attention mechanisms have become attractive as they aim at strengthening discriminative features and suppressing irrelevant ones, which matches well the key of re-id, i.e., discriminative feature learning. Previous approaches typically learn attention using local convolutions, ignoring the mining of knowledge from global structure patterns. Intuitively, the affinities among spatial positions/nodes in the feature map provide clustering-like information and are helpful for inferring semantics and thus attention, especially for person images where the feasible human poses are constrained. In this work, we propose an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning. Specifically, for each feature position, in order to compactly grasp the structural information of global scope and local appearance information, we propose to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions (e.g., in raster scan order), and the feature itself together to learn the attention with a shallow convolutional model. Extensive ablation studies demonstrate that our RGA can significantly enhance the feature representation power and help achieve the state-of-the-art performance on several popular benchmarks. The source code is available at https://github.com/microsoft/Relation-Aware-Global-Attention-Networks.
Components of defence strategies in society’s information environment: a case study based on the grounded theory

The goal of this study is to explore the components of defence strategies faced by society in its information environment, and how these strategies are inter-related. This qualitative in-depth case study applied past research and empirical evidence to identify the components of defence strategies in a society’s information environment. The data collected was analysed using the Grounded Theory approach and a conceptual framework with the components of defence strategies and the relationships between these components was developed using the Grounded Theory. This study shows that the goal of politically and militarily hostile actors is to weaken society’s information environment, and that their operations are coordinated and carried out over a long time period. The data validates past studies and reveals relationships between the components of defence strategies. These relationships increase confidence in the validity of these components and their relationships, and expand the emerging theory. First, the data and findings showed 16 inter-connected components of defence strategies. Second, they showed that the political, military, societal, power, and personal goals of the hostile actors carrying out cyber operations and cyber attacks are to weaken society’s information environment. Third, they revealed that cyber operations and cyber attacks against networks, information and infrastructures are coordinated operations, carried out over a long time period. Finally, it was revealed that the actors defending society’s information environment must rapidly change their own components of defence strategies and use the newest tools and methods for these components in networks, infrastructures and social media.
Quasi-distributions for arbitrary non-commuting operators

We present a new approach for obtaining quantum quasi-probability distributions, P(α,β), for two arbitrary operators, a and b, where α and β are the corresponding c-variables. We show that the quantum expectation value of an arbitrary operator can always be expressed as a phase space integral over α and β, where the integrand is a product of two terms: One dependent only on the quantum state, and the other only on the operator. In this formulation, the concepts of quasi-probability and correspondence rule arise naturally in that simultaneously with the derivation of the quasi-distribution, one obtains the generalization of the concept of correspondence rule for arbitrary operators.
Security of Healthcare Systems with Smart Health Records Using Cloud Technology

The information technology in healthcare become trustful and more reliable with the advancement in technology. The cloud computing is one of the advance information technologies which is comprises of huge and bulky computers and linked with online web and internet. This is also termed as distributed computing which transfer data from one source to another and can be access and controlled by authorized users in healthcare organization. The cloud computing and its related challenges builds a protected Electronic Health Record (EHR) in an exceedingly cloud computing setting has attracted heaps of attention healthcare business and educational community. The thought of Cloud computing is turning into a preferred data technology (IT) infrastructure for facilitating EHR or good health records sharing and integration. As advancement in technology comes with some limitation and data security, reliability and privacy are some of the hot issues which are existing in cloud computing. The proposed chapter includes various factors affecting cloud computing security and comparative analysis of existing algorithms for cloud computing security in healthcare.
Memory-Based Graph Networks

Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also introduce two new networks based on this layer: memory-based GNN (MemGNN) and graph memory network (GMN) that can learn hierarchical graph representations. The experimental results shows that the proposed models achieve state-of-the-art results in eight out of nine graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data. Code and reference implementations are released at: https://github.com/amirkhas/GraphMemoryNet
Analisis Prediksi dan Hubungan antara Debit Air dan Curah Hujan pada Sungai Ciliwung di Kota Bogor

Water demand should be balance with water availability. The population of Bogor City was increased every year, so that water demand become increased. Currently, Government of Bogor City only utilizes the Cisadane river as a source for drinking water, but Bogor City has 2 rivers there are Cisadane river and Ciliwung river. So, Ciliwung river can be solution for this problem. Water discharge and rainfall influence water availability. Distribution log pearson type 3 used to predict the water discharge and rainfall and linier regression analyzed the relationship between water discharge (dependent variable) with rainfall as X1 and water level as X2 (independent variable). The result of distribution log pearson type 3 every return period 2, 5, 10, 25, dan 50 years is water discharge and rainfall were increased. Average increase of water discharge every return period is 1.6 m3/s and average increase of rainfall every return period is 251 mm. Partially, water discharge influenced by water level 97.7% and 2% by rainfall. But, = 0.000 &lt; = 0.05. it is mean rainfall and water level cannot be ignored because it can influence water discharge with regression model = -3.01 + 0.000 X1 + 0.202 X2.
New Solution Implementation to Protect Encryption Keys Inside the Database Management System

Due to the attacks' growth on sensitive databases by deploying advanced tools, beyond access control and authentication mechanisms, the database encryption remains a useful and effective way to ensure robust security of data stored within it.Any database encryption solution is based on a specific encryption model that determines how data is encrypted inside it.A relevant database encryption model must necessarily adopt a strong security policy of data encryption keys.It determines how these keys are generated, stored, and protected.In this work, we will implement an original solution that protects the encryption keys when encrypting data occurred at the Database Management System level.Our solution suggests protecting the keys by their encryption with other ones named Master Keys, which are generated according to the encryption granularity defined by the database encryption model.The proposed solution protects the keys of two database encryption models: at the level of the columns and the level of tables.
Human Assist Control of Electric Wheelchair by Using Control Barrier Function

In a modern aging society, electric wheelchairs are an essential means of transportation for the elderly. However, human operation errors of the electric wheelchair cause some accidents. For a control system operated by a human, human assist control to avoid accidents attracts much attention in recent years. For this problem, Nakamura et al. have proposed a relaxed control barrier function and a human assist control law. However, considering the operability and safety, it was difficult to apply the proposed human assist control to an electric wheelchair. In this paper, we propose a novel human assist control law that can consider an assist profile function based on a relaxed CBF. We aim to design human assist control law that can be applied to electric wheelchairs. The effectiveness of the proposed control method is confirmed by simulations and actual experiments using an electric wheelchair.
A Study on the Trade-Economic Effects and Utilization of AEO Mutual Recognition Agreements

Purpose:The AEO (Authorized Economic Operator) program, created in 2001 in the United States due to 9.11 terrorist's attack, fundamentally changed the trade environment.Korea, which introduced AEO program in 2009, has become one of the world's top countries in the program by ranking 6th in the number of AEO certified companies and the world's No. 1 in MRA (Mutual Recognition Agreement) conclusions.In this paper, we examined what trade-economic and non-economic effects the AEO program and its MRA have in Korea.Research design, data and methodology: In this study we developed a model to verify the impact between utilization of AEO and trade-economic effects of the AEO and its MRA.After analyzing the validity and reliability of the model through Structural Equation Model we conducted a survey to request AEO companies to respond their experience on the effects of AEO program and MRA.As a result, 196 responses were received from 176 AEO companies and utilized in the analysis.Results: With regard to economic effects, the AEO program and the MRA have not been directly linked to financial performance, such as increased sales, increased export and import volumes, reduced management costs, and increased operating profit margins.However, it was analyzed that the positive effects of supply chain management were evident, such as strengthening self-security, monitoring and evaluating risks regularly, strengthening cooperation with trading companies, enhancing cargo tracking capabilities, and reducing the time required for export and import.Conclusions: When it comes to the trade-economic effects of AEO program and its MRA, AEO companies did not satisfy with direct effects, such as increased sales and volume of imports and exports, reduced logistics costs.However, noneconomic effects, such as reduced time in customs clearance, freight tracking capability, enhanced security in supply chain are still appears to be big for them.In a rapidly changing trade environment the AEO and MRA are still useful.Therefore the government needs to encourage non-AEO companies to join the AEO program, expand MRA conclusion with AEO adopted countries especially developing ones and help AEO companies make good use of AEO and MRA.
Analysis and use of building heating and thermal energy management system

To explore the role and influence of thermal energy management system on building heating, by building a thermal energy management system based on the Internet of Things, the situation of heating system and building heating is analyzed, the heat utilization rate of building heating, the stability of heating temperature, the change of heating energy consumption are mainly studied, and the energy consumption of building and the comprehensive effect of thermal energy management system and residents? satisfaction are analyzed. The research results show that through the role of the Internet of Things thermal energy management system, the heat utilization rate of heating buildings has increased from about 65% to about 80%, about 15%. The fluctuation of heating water temperature is reduced from 12?C before the system is adopted to 4 ?C, which improves significantly. The coal consumption per hour of heating system is reduced from 63 kg/h to 50 kg/h, and the coal saving is about 15%. This not only saves resources but also reduces environmental pollution. The heat management system based on the Internet of Things has significantly improved the heating system and building heating. Through the application of thermal energy management system, not only the heat utilization rate is increased, but also the consumption of resources is reduced and the environment is protected. Meanwhile, it solves the problem of building heating and the maximization of efficiency in the operation of heating companies. The research on building heating and thermal energy management system has a positive effect on the follow-up research.
Expert System of Diagnosing Disease in Banana Plants Using Dempster-Shafer and Certainty Factor

The increasing demand for banana production for local and export needs is followed by the increasing need for quality banana plant seeds.However, the growth of banana plants is always disrupted by attacks by pests and diseases, especially Fusarium Wilt disease, so that this is one of the factors affecting the quality of banana plants and can be a threat to the decreasing quantity of banana production.The ignorance of banana planters and still at least a diagnosis of banana plant diseases is very important to make an inference engine in the form of a banana plant expert system.The Dempster-Shafer method and Certainty Factor are proposed to be applied to expert systems.The appropriateness of the Dempster-Shafer simulation simulation results with expert answers can be a clue that Dempster-Shafer and Certainty Factor can be used as an inference engine to diagnose banana plant diseases.
Automatic Tuning of High Piano Tones

Piano tuning is known to be difficult because the stiffness of piano strings causes the tones produced to be inharmonic. Aural tuning is time consuming and requires the help of a professional. This motivates the question of whether this process can be automated. Attempts at automatic tuning are usually assessed by comparing the Railsback curve of the results with the curve of a professional tuner. In this paper we determine a simple and reliable rule for tuning the high tones of a piano with the help of a listening test. This rule consists of matching the two tones in an octave interval so that the first partial frequency of the upper tone becomes exactly the same as the second partial frequency of the lower tone. This rule was rated best among four tuning rules that were compared in the test. The results found are explained using a beat-based analysis, and are consistent with some previous studies. They are also tested against the existing method of using Railsback curves, and it is shown that comparison using Railsback curves is an unreliable method of assessing different tunings. The findings from this paper can be used to create a complete automatic tuner that could make the process of piano tuning quick and inexpensive.
Generating Natural Language Explanations for Group Recommendations in High Divergence Scenarios

In some scenarios, like music or tourism, people often consume items in groups. However, reaching a consensus is difficult as different members of the group may have highly diverging tastes. To keep the rest of the group satisfied, an individual might need to be confronted occasionally with items they do not like. In this context, presenting an explanation of how the system came up with the recommended item(s), may make it easier for users to accept items they might not like for the benefit of the group. This paper presents our progress on proposing improved algorithms for recommending items (for both music and tourism) for a group to consume and an approach for generating natural language explanations. Our future directions include extending the current work by modeling different factors that we need to consider when we generate explanations for groups e.g. size of the group, group members' personality, demographics, and their relationship.
Humanizing Big Data: Recognizing the Human Aspect of Big Data

The term "big data" refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as "recreational" genetic testing such as ancestry testing, as well as research studies, provide one type of "big data." Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this "humanizing" process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.
INFRASTRUCTURE FOR LARGE SCALE HARVESTING OF WEBOMETRIC INDICATORS

The main purpose of scientific rankings is not only comparison, but also stimulating the development of universities and research institutes, improving the effectiveness of their educational and research processes. However, existing webometric ratings are not suitable for this task because of the lack of the possibility of their analysis and verification. The solution is to change the approach to webometric measurements, to perform them based on the principles of regular collection and openness of source data. The article discusses the indicators used in the tasks of academic webometrics, their reliability and stability, substantiates the need for regular collection of the values of these indicators to improve the quality of data and analyze their dynamics. Based on the experience of implementing a project to research the Russian academic web space (http://www. webometrix.ru), problems are analyzed that arise when collecting the values of webometric indicators of sites of research organizations and institutions of higher education on a global scale with a frequency of at least 1 time per month. The author describes a distributed system for collecting values of webometric indicators and evaluates the necessary degree of parallelization of the process. The developed approach is universal for the tasks of collecting large amounts of information by the method of extracting data from website pages, and the collection of webometric data is also relevant for the tasks of analyzing other thematic segments of web space, such as library websites.