Reviewerly/exHarmony huggingface dataset:
Cellulose Valorization for the Development of Bio-based Functional Materials via Topochemical Engineering

Bio-based resources are of high scientific importance these days due to their abundance, renewability, environment compatibility, recyclability, and many more aspects of sustainable material production and its consumption. Cellulose, the naturally occurring plant polymer has been a widely worked out substrate material for value addition as their supply from the forest products industry and agricultural wastes is huge and inexpensive. Cellulose is a fascinating polymer, with the repeating units of monosaccharide molecules containing secondary and tertiary –OH functional groups. These –OH groups are the primary target locations to modify cellulose chemically or physically. Topochemical engineering is a method of directed assembly or directed disassembly of functional materials. Directed assembly and disassembly are controlled via the design of intermolecular interactions in a topological space. This chapter covers recent advances in topochemical engineering of cellulose-based functional materials with the emphasis on valorization methods, chemistry, composition, and applications for the readers of sustainable biomaterials chemistry.
A Collaborative Multi-modal Fusion Method Based on Random Variational Information Bottleneck for Gesture Recognition

Gesture is a typical human-machine interaction manner, accurate and robust gesture recognition can assist to achieve more natural interaction and understanding. Multi-modal gesture recognition can improve the recognition performance with the help of complex multi-modal relationship. However, it still faces the challenge of how to effectively balance the correlation and redundancy among different modalities, so as to guarantee the accuracy and robustness of the recognition. Hence, in this paper, a collaborative multi-modal learning method based on Random Variational Information Bottleneck (RVIB) is proposed. With random local information selection strategy, some information is compressed by information bottleneck, and the rest is retained directly, so as to make full use of effective redundant information while eliminating invalid redundant information. Experiments on open dataset show that the proposed method can achieve 95.77% recognition accuracy for 21 dynamic gestures, and can guarantee the recognition accuracy when some modality is missing.
The application of Internet of things technology in integrity management of urban gas pipe network

Abstract On the Internet of things technology in the gas pipeline integrity management the present situation of the study is less. This paper aims to improve the integrity management level of gas pipe network and combines the mature experience of Internet of things in the integrity management of long-distance pipelines. The integrity management system of urban gas pipe network based on Internet of things technology is proposed, and the main application scenarios are analyzed. The results show that the application of Internet of things technology improves the uniformity of data collection in pipe network operation management, promotes the sharing of data, and avoids multiple data conversion. The detection results of various sensing means, methods and instruments can be integrated to ensure the reliability of risk assessment and integrity assessment. To realize the scientific and reasonable allocation of limited human resources and equipment, and to evaluate the effectiveness of the existing integrity management system, to achieve the goal of gradually improving the integrity management. This paper provides specific guidance and ideas for urban gas pipe network management and operation units to carry out high level integrity management.
Students’ performance in French subjects in the COVID-19 confinement

Education has been a hot issue during 2020's confinement due to COVID-19. In this paper, we analyze the implementation of distant learning in foreign language subjects (French L2 and Translation from French to Spanish). To analyze data accurately, we have used subjects with similar methodology, tools and teachers in the last years. By doing so, we are able to isolate and describe the effect of confinement in learning through the comparison of students' performance during the 2020 confinement and those of previous years. The main results show that students' reaction to confinement was positive, with more commitment handing in all or most of the assignments. This could be one of the factors resulting in better and increasing scores through the confinement. Finally, we also show some significant decreasing scores during the gradual release in May.
Parallel Intelligent Satellite Based on Digital Quadruplets

In view of the complexity of satellite platforms and systems, the ACP parallel system theory is applied to the field of satellites. The concept and basic framework of parallel satellites are proposed to define the digital quadruplets of parallel satellites: physical satellites, description satellites, prediction satellites, and guidance satellites. Establish a parallel satellite from a physical satellite to a descriptive intelligence of a descriptive satellite, a predictive intelligence from a descriptive satellite to a prediction satellite, and a guidance intelligence framework system from a prediction satellite to a guidance satellite. Based on this framework system, the parallel control concept of parallel satellites is proposed to the leap from traditional digital satellite to intelligent.
Item Recommendation based on Multimedia Variational Autoencoder

Abstract At present, with the rapid development of science and technology and the rapid development of computer related technology, with the development of this series of technologies, multimedia communication has rapidly become the means of communication and communication. Digital image technology, a very important form of information representation in multimedia communication, has now become a mainstream way. The technology provides more convenience for people's lives, but also has many problems, such as security, transmission and storage. For example, some images will involve personal privacy, business secrets, and even government secrets. In addition, the rapid transmission of information in limited bandwidth is also very important. Especially in the presence of cloud storage, it can not only store large amounts of information, such as files, videos and images, but also provide a large enough online space to store shared data. In this case, the real-time and security requirements of information transmission are higher and stricter. It is of great theoretical and practical significance to compress the image and reduce the bandwidth and ensure the security of image encryption. Image encryption is also called digital image encryption. Image compression is mainly based on the use of different technologies to change the pixel value and location of the image to protect the image. Image compression mainly compresses the image by removing redundant or unrelated information. In this paper, the concept of the variational self encoder is introduced firstly, and then the image compression and encryption based on the variational self coder generation model are studied and summarized briefly.
Prediction of Solar Radiation using Hybrid Discriminant-Neural Network

A timely and accurate prediction of solar radiation results in proper plant growth, seed germination and stages of flowering and fruiting. Neural Network is becoming popular in designing predictive models. However, issues like importance of variables and long training process has limited its accuracy. The objective of this study is to explore the performance of predictive model by integrating neural network with traditional step-wise discriminant analysis forming a hybrid model. The inclusion of selected features from discriminant analysis to the neural network will improve the accuracy of the designed predicted model. The paper also examines that the hybrid approach outperforms the neural network by selecting different architecture of neural network.
The comparison of the ordinal logistic model with the classical regression model

Abstract This research’s aims was to provide an alternative for statistical user who were interested in analysing the functional relationship between ordinal response and explanatory variable. The data used was the data of Students on Generation 2017 JPMIPA FKIP Bengkulu University. The test statistic from the modified probability ratio in ordinal logistic had a higher test power than the F test in the classical linear regression. Overall the probability of rejection of ordinal logistic tended to be lower than the classical regression. At condition of p close to one, the test power of the ordinal logistic would be the same as the classical regression. Ordinal logistic model tended to be more selective in choosing variables that significantly affect the response, resulting in a simpler model than the classical regression. The results of the research with the classical linear model obtained variables that influence the student GPA were UN score, High School Report, UNIB admission pathway, High School Status and interaction of UN Score and High School Status. While the ordinal logistic model produced influential independent variables were the UN Score, High School Report, High School Status and the interaction of UN Score and High School Status.
RELATIONSHIP ANALYSIS BETWEEN TEXTURE INFORMATION OF GRAVEL IMAGES AND HYDRAULIC CONDUCTIVITY

本論文では,砂礫画像からテクスチャ情報を抽出し,テクスチャ情報から透水係数を推定する方法を検討する.テクスチャ情報として,画像の平行移動,拡大縮小,45°刻みの回転に関して不変なHeralickらの濃度共起行列から計算される14種類の特徴量を用いる.テクスチャ情報と透水係数の関係性の分析として,相関分析と重回帰分析を行う.本実験では,長崎市浦上川で採取された川砂を用いて砂礫試料を作成し,その表面画像と飽和透水係数を用いた.実験の結果,Heralickらの濃度共起行列から計算される特徴量を用いて透水係数を推定することは有効であることがわかった.
METHODOLOGICAL ACTIVITY OF THE TOMSK REGIONAL UNIVERSAL SCIENTIFIC LIBRARY NAMED AFTER A. S. PUSHKIN: FROM THE PAGES OF HISTORY

The article deals with the history and current state of methodological activities of the Tomsk regional universal scientific library named after A. S. Pushkin – the methodological center for open to general use public libraries – from 1930 to 2019. It observes the matter and peculiarities of the regional library scientific- methodological system during this period. It is noted, that a transition from the directive style of library management to the interaction of regional and municipal levels of management has taken place. In modern conditions, innovative forms have been introduced into the work of the methodological service, which is an important factor for the further development of the regional library business. Innovations have affected the system of professional development. An educational and methodological center has been created within the methodological department, which activity demonstrates new approaches to the educational function. For the effective work with young librarians, the club of online communication “#Profichat” has been organized. The website “Professionals” is also in demand, created to inform library workers of the region about events in the library activities of the country and the region. The most important achievement of TOUNB methodological service is its participation in the national project “Culture”, which is highly effective: a new generation of libraries has appeared in the region, and about 40 employees of regional libraries have improved their skills in Federal libraries. The restraining factors in the methodological activities development are lack of methodologists in a number of central library systems and insufficiency of publications that address issues of development and organization of scientific and methodological support for libraries.
Unseen Filler Generalization In Attention-based Natural Language Reasoning Models

Recent natural language reasoning models have achieved human-level accuracy on several benchmark datasets such as bAbI. While the results are impressive, in this paper we argue by experiment analysis that several existing attention-based models have a hard time generalizing themselves to handle name entities not seen in the training data. We thus propose Unseen Filler Generalization (UFG) as a task along with two new datasets to evaluate the filler generalization capability of a natural language reasoning model. We also propose a simple yet general strategy that can be applied to various models to handle the UFG challenge through modifying the entity occurrence distribution in the training data. Such strategy allows the model to encounter unseen entities during training, and thus not to overfit to only a few specific name entities. Our experiments show that this strategy can significantly boost the filler generalization capability of three existing models including Entity Network, Working Memory Network, and Universal Transformers.
A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Q&amp;amp;A Platforms

Online Health Q&A platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors and direct a bounty to their most preferred answer, have become a prominent channel for patients to seek and receive medical advice in China. Common characteristics of these platforms include bounty-motivated problem solving, limited search functionality, and lack of peer-assessment. To explore the quality of medical advice promoted on these platforms, we analyzed data on patients’ evaluation of 497k answers to 114k questions on one of the most popular OHQPs, 120ask.com, over a 3-month period. We assembled a panel of independent (offline) physicians and instructed them to professionally evaluate the quality of 13k answers. We found that the quality of medical advice offered on the platform was on average high and that low-quality answers were rare (6%). However, our results also indicate that patients, as laypeople, lacked the ability to discriminate advice based on quality. They were as likely to choose the best answer as the worst. When poor advice was offered, patients chose it over a high-quality alternative as much as 15% of the time. The medical accuracy of patient evaluation was worse in some critical categories (cancer, heart and liver disease) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we leveraged natural language processing techniques to construct a rich set of answer features and estimated a deep learning model to determine how patients responded to features. Importantly, we identified the extent to which patients respond positively or negatively to different heurist cues. While our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues which cannot effectively guide them towards the best advice. We also found that the platform reputation metric was not correlated with the quality of advice-giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Finally, we found that OHQPs enabled or exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings.
Geochemistry of groudwater and surface water in the Sentsa River Basin (Eastern Sayan Ridge)

The object of this&amp;nbsp;research is the&amp;nbsp;groundwaters and surface waters of the Sentsa River Basin. The area of explorations is situated in the Eastern Sayan hydrogeological folded region. Cold and thermal waters occur in the PR and PZ metamorphic and igneous rock. These waters are discharged through the spring series in the river valleys laid along fault zones. The main type of fieldwork was hydrogeological sampling. The analysis of the macrocomponent composition of waters&amp;nbsp;was conducted in the &amp;laquo;Geodynamics and Geochronology Center&amp;raquo; (Institute of the Earth&amp;rsquo;s Crust SB RAS, Irkutsk), the trace element composition was determined by ICP MS on an Element-2, Finnigan MAT (Germany) at the Vinogradov Institute of Geochemistry SB RAS (Irkutsk). The scientific novelty consists in the fact that this article provides not only the characteristics of chemical composition of cold and thermal waters, but also the&amp;nbsp;primary data on&amp;nbsp;the distribution of microelements&amp;nbsp;in the ground and surface waters of the Sentsa River Basin. Groundwater (thermal and cold) are revealed to have a bicarbonate Ca-Na composition, withal river and lake waters are generally bicarbonate Ca. Thermal waters (in the Khoyto-Gol River Basin) are largely enriched in Li, Be, B, Si, Mn, Ge, As, Br, Rb, Sr, Cs, Ba, W and depleted in Al, P, Cu, Zn, Ag relative to surface and rainwater. The REE distribution profile in groundwater and surface waters have peculiarities that are characterized by a positive europium anomaly and the prevalence of light REE over heavy ones.
PeopleXploit : A hybrid tool to collect public data

This paper introduces the concept of Open Source Intelligence (OSINT) as an important application in intelligent profiling of individuals. With a variety of tools available, significant data shall be obtained on an individual as a consequence of analyzing his/her internet presence but all of this comes at the cost of low relevance. To increase the relevance score in profiling, PeopleXploit is being introduced. PeopleXploit is a hybrid tool which helps in collecting the publicly available information that is reliable and relevant to the given input. This tool is used to track and trace the given target with their digital footprints like Name, Email, Phone Number, User IDs etc. and the tool will scan & search other associated data from public available records from the internet and create a summary report against the target. PeopleXploit profiles a person using authorship analysis and finds the best matching guess. Also, the type of analysis performed (professional/ matrimonial/criminal entity) varies with the requirement of the user.
IoT-based group size prediction and recommendation system using machine learning and deep learning techniques

Abstract In an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.
'Future-Proofing Legislation for the Digital Age'

For the past decades, scholars from varied scientific fields have insisted on the need to develop ‘future-proof solutions’. Future proofing science, design, architecture, and technology entails the creation of solutions that are forward-looking, sustainable, resilient, and can adapt to complex challenges. In the last years, lawmakers have also become intrigued by the question whether laws and policies could also be future-proof. This question has become particularly relevant considering the rapid changes that characterize the digital age. The implementation of a future-proof approach to legislation has nonetheless remained overlooked in the legal literature. This paper aims to fill this gap with an interdisciplinary analysis of future proofing and a discussion of the challenges of implementing this approach to lawmaking. We contend that future proofing law is a challenging task for legislators but a cautious forward-looking approach could ensure that legislation becomes more adaptable and flexible to innovation. Drawing on the interdisciplinary literature on future proofing, we suggest the broader employment of experimental legislation and regulatory impact assessments.
The differential of probabilistic entailment

Suppose elements a1,…,ak of a boolean algebra A are assigned probabilities π1,…πk. Boole asked to determine the possible probabilities of some other element a∈A, given the assignment ai↦πi,(i=1,…,k). De Finetti's solution of Boole's problem yields a closed interval Ia⊆[0,1] such that the set of possible probabilities of a coincides with Ia. Ia is nonempty iff the assignment is consistent in de Finetti's sense. Now suppose the probability of ak undergoes a small perturbation πk↦πk+dπk. We study the resulting modification of Ia. For instance, we prove that the one-sided derivatives ∂length(Ia)/∂πk± always have a rational, (generally non-integer) value. In the particular case when each probability πi is rational and A is presented as a Lindenbaum algebra, all these derivatives are Turing computable. Their existence domain is decidable by the Tarski-Seidenberg algorithm for polynomial equations and inequalities in real closed fields. Our results build on de Finetti's consistency notion and his solution of Boole's problem, and extend Hailperin's polyhedral methods for combining bounds on probabilities.
IMPLEMENTASI DATA MINING PENGELOMPOKAN JUMLAH DATA PRODUKTIVITAS UBINAN TANAMAN PANGAN BERDASARKAN JENIS UBINAN DENGAN METODE CLUSTERING DIKAB LANGKAT (STUDI KASUS : BADAN PUSAT STATISTIK LANGKAT)

Based on data from the ubinan survey at the Central Statistics Agency (BPS) Langkat, there are several sample areas and there are results of farmer productivity in growing food crops. For this reason, it is necessary to group the number of data from the ubinan survey based on the type of food crop ubinan to classify the amount of harvest by farmers in planting food crops in each district. Data Mining is a process of finding information by identifying patterns in data sets. The process of finding information can be done by grouping the data using the Clustring method with the K-Means algorithm. By using K-Means the aim is to facilitate grouping the number of data on the productivity of food crops according to the type of ubinan and the productivity of food plants. And the data used in this research is the ubinan survey data for 3 years, namely 2017 to 2019. From the results of the analysis of the program that has been tested using matlab and the variables have been determined, it can be seen that, for cluster 1, the results of the type of food crop tubing and kecamatan, the amount of data is 423, for cluster 2 the number of data is 387 data types, the number of production and the district is 387 data, for cluster 3 the number of data is 432 data.