Reviewerly/exHarmony huggingface dataset:
AGE-SPECIFIC DYNAMICS OF ESSENTIAL OIL CONTENT IN ABIES SIBIRICA BARK. THEORETICAL ASPECTS OF EVALUATION

The variability of the essential oil content in Abies Sibirica bark in the Eastern and Western parts of the Krasnoyarsk forest-steppe was evaluated in eight age groups. The model of the age dynamics of changes in the essential oil content in the bark of Abies Sibirica, which is adequate to the experimental data, was obtained. With increasing age of fir, the content of fir oil in the bark increases from 1.4±0.1% of a. s. s. in 20–30 years of age to 2.8±0.2% of a.s.s. at the age of 70–80 years, with further decreases to 0.4±0.1% of a. s. s. in overmature trees.&#x0D; The results of parametric identification of the model for Abies Sibirica growing in the Krasnoyarsk forest-steppe are presented. Based on the results of 50.000 computational experiments, the simulation method estimates the statistics of changes in the model coefficients and 95% of the boundary of the forecast values of the content of fir oil in the bark. A compartment model is proposed that explains the age-specific dynamics of essential oil content in tree greens and Siberian fir bark. The obtained solution compartment model allows us to associate its parameters with the coefficients of models of changes in the content of fir oil in tree greens and bark obtained by processing experimental data.
Benign Interaction of Security Domains

Whenever data is communicated outside a security domain there is the risk that it may influence data coming back in a way that is not permitted by the security domain. This may arise when different security domains relate to different parallel processes that exchange information through communication. We provide general definitions of the demands on the communication and sanitisation primitives so as to mitigate the risk. For interesting instantiations of these definitions we provide algorithms for checking that the demands have been met. The development is illustrated by a worked example dealing with the outsourcing of data management to the cloud.
Notice of Removal: Machine Learning in Healthcare - Application of Advanced Computational Techniques to Improve Healthcare

Since 2006, Deep Learning, also known as Hierarchical Learning, has developed as a new field of research in Machine Learning. Deep learning models are used to solve problems that shallow architectures (e.g., regression) cannot solve due to the curse of dimensionality. Automatically created statistically robust characteristics are derived from the data using a two-stage learning procedure that incorporates multiple layers of nonlinear processing. This review article, which serves as the introduction to the special session on deep learning, provides state-of-the-art models and summarizes current understanding on this type of learning method, which is used to tackle a variety of difficult categorization tasks. Deep Learning is a relatively recent area of research in Machine Learning that was founded with the purpose of getting Machine Learning closer to one of its original objectives: Artificial Intelligence. Deep Learning is concerned with the acquisition of several levels of representation and abstraction that aid in the interpretation of various forms of data, including images, audio, and text.
Scale Semantic Flow Preserving Across Image Pyramid

Image pyramid based face detector is powerful yet time consuming when cooperated with convolutional neural network, and thus hard to satisfy the computational requirement in real-world applications. In this paper, we proposed a novel method using Semantic Preserving Feature Pyramid (SPFP) to eliminate the computational gap between image pyramid and feature pyramid based detectors. Since a feature tensor of an image can be upsampled or downsampled by Reversible Scale Semantic Flow Preserving (\(RS^2FP\)) network, we do not need to feed images with all scales but a middle scale into the network. Extensive experiments demonstrate that the proposed algorithm can accelerate image pyramid by about 5\(\times \) to 7\(\times \) on widely used face detection benchmarks while maintaining the comparable performance.
Research on the Classification Calculation Based on the Local Features of Hard-Pen Chinese Characters

At present, in the actual teaching of primary and secondary schools, the lack of full-time teachers and guidance is the biggest dilemma facing the hard-pen Chinese character education. With the rapid development of big data and artificial intelligence, computer vision technology can be extended to the field of education, which can effectively solve the problem of Chinese character classification in primary and secondary schools. In this paper, based on the expert evaluation index and the structural characteristics of Chinese characters, two models are used to complete the classification and evaluation of Chinese characters. One method uses improved HOG feature combined with PCA dimension reduction, and then uses SVM classifier to complete the classification calculation of left and right structure Chinese characters. In the other method, SSIM and Hash are used to assign different weights to the edge block, detail block and smooth block of the image respectively. Finally, the similarity of the whole image is obtained by weighting, and the evaluation and calculation of enveloping structure and single character structure of Chinese characters are completed. The experimental results show that both of the two models are effective in calculating the corresponding Chinese character structures. On this basis, a relatively perfect evaluation process of hard-pen Chinese characters in primary and secondary schools is put forward, which can solve the evaluation problem of hard-pen Chinese characters in primary and secondary schools.
The Speed and Threshold of the Biased Hamilton Cycle Game

We show that there is a constant C such that for any b < n/ln n − Cn/(ln n)3/2, Maker can win the Maker-Breaker Hamilton cycle game in n + Cn/√ln n steps.
Text Classification Based on ALBERT and Mutil-head Attention Capsule Network

In order to solve the problems that the Capsule Network cannot encode the long-distance dependency in the text when performing the text classification task and the excessive parameter amount of its fully connected capsule layer causes the training to be slow. This paper first uses the BiGRU network to obtain the contextual semantic information of the text, obtains the word dependency relationship within the text sequence through the Multi-Head Self-Attention mechanism, and generates a hidden layer vector with global semantic information. An improved Capsule Network is proposed. The hidden layer vector is input into the improved capsule network, and dynamic routing is used to obtain the relationship between the local and global text. In addition, this paper uses the ALBERT pre-training model to obtain the dynamic feature representation of the text to improve the characterization ability of the word vector. Through experiments on the THUCNews data set, the overall F1 value on the verification set reached 96.44%. The results show that the model in this paper has a better classification effect than other existing network models.
Simplod: Simple SPARQL Query Builder for Rapid Export of Linked Open Data in the Form of CSV Files

In the last decade, linked open data (LOD) became the de-facto highest standard of publishing data on the Web, a.k.a. 5-star open data. One of the advantages of LOD is better data discovery thanks to the linkable nature of the data. Unfortunately, in the wider IT expert community, RDF and SPARQL is considered unnecessarily complex, hard to understand and hard to process, especially when transferring open data in bulk. However, it is this wider IT expert community which is the one supposed to both publish data produced in the public administration as open data, and to build consumer-facing applications on top of the data. In this paper, we propose an approach to making LOD easily accessible to the community used to CSV files. We propose a tool called Simplod focused on rapid, straightforward and customizable formulation of a SPARQL SELECT query intended for customizable bulk transformation of LOD published in SPARQL endpoints into CSV files. In addition, Simplod configurations can be stored in and shared via Solid pods.
Dyadic Deontic Logic in HOL: Faithful Embedding and Meta-Theoretical Experiments

A shallow semantical embedding of a dyadic deontic logic by Carmo and Jones in classical higher-order logic is presented. The embedding is proven sound and complete, that is, faithful. This result provides the theoretical foundation for the implementation and automation of dyadic deontic logic within off-the-shelf higher-order theorem provers and proof assistants. To demonstrate the practical relevance of our contribution, the embedding has been encoded in the Isabelle/HOL proof assistant. As a result a sound and complete (interactive and automated) theorem prover for the dyadic deontic logic of Carmo and Jones has been obtained. Experiments have been conducted which illustrate how the exploration and assessment of meta-theoretical properties of the embedded logic can be supported with automated reasoning tools integrated with Isabelle/HOL.
Towards classification of stative verbs in view of corpus data

Abstract The paper presents work in progress on the compilation and automatic annotation of a dataset comprising examples of stative verbs in parallel Bulgarian-Russian corpora with the goal of facilitating the elaboration of a classification of stative verbs in the two languages based on their lexical and semantic properties. We extract stative verbs from the Bulgarian and the Russian WordNets with their assigned conceptual information (frames) from FrameNet. We then assign the set of probable Bulgarian and Russian stative verbs to the verb instances in a parallel Bulgarian-Russian corpus using WordNet correspondences to filter out unlikely stative candidates. Further, manual inspection will ensure high quality of the resource and its application for the purposes of semantic analysis.
Improving Information System Development and Maintainability Factors Through Standardized Processes: An Empirical Study

This paper examines how standardized processes affect information systems (IS) development project performance, key maintainability factors and the maintainability of the IS delivered. Main findings from a survey of 97 information technology (IT) professionals show that software process improvement models (SPIM), project management maturity models (PMMM) and formal development methodologies (FDM) can (i) improve the chances of success of IS projects and (ii) reduce key IS development problem factors that lower the maintainability of the IS delivered. Important findings also include a list of the top 10 specific issues affecting the maintainability of IS. Implications for research and practice are presented.
Analyzing Heuristic Job Scheduling Algorithms by Varying Cloudlet Load in a Cloud Infrastructure

The cloud based innovative applications are increasing regularly and hence the data and job load also increasing proportionally. Cloud based service providers are also increasing their infrastructure and service facility to serve in a much better way to its clients. The job processing load will also increase the waiting time and hence affect the service response time at user’s end. So, it is always a matter of great importance that which job scheduling algorithm should be applied to serve the client in an efficient manner. This is the main motivation for framing this research paper. In this paper, we are taking the main five heuristic job scheduling algorithms like FCFS (First Come First Server), SJF (Shortest Job First), MaxMin, MinMin, and Saffrage for analyzing on the pre-decided cloud infrastructure. Among these heuristic algorithm, MaxMin algorithm outperforms than others in all the test cases i.e. with the cloudlet load of 100, 200, 300, …, 1000 cloudlets. Hence we can say that the MaxMin is the best scheduling algorithm among these five heuristic job scheduling algorithms.
The anomaly segmentation via dynamic branch fusion

Anomaly segmentation is an important task in computer vision. At present, anomaly segmentation has achieved milestone development. Many representative works have been proposed, especially unsupervised learning methods and pre-training methods, but pre-training methods are difficult to solve cross-The gap brought by domains has not been paid much attention to in previous research work on how to mine the hidden information contained in the data itself. Because of the diversity of anomaly detection, it is crucial to make full use of the information of the object itself. This paper proposes a novel dynamic branch fusion structure, through mining the hidden information inside the data, so as to re-model more targeted abnormal segmentation. The method has been tested on two known benchmarks to verify the effectiveness of the proposed method.
Research on Risk Early Warning Methods of Enterprise Finance and Taxation Management Based on Big Data Mining

With the rapid development of China's economy, the current market competition in various industries has become fierce. Many companies realize that risk warning must be carried out during the development of the company, otherwise it will be detrimental to their own development. Therefore, it is necessary to strengthen the risk warning of fiscal and taxation. Although many companies have embarked on this work, the effect is not satisfactory, so that many companies question the early warning of fiscal and tax risks. How to improve the level and efficiency of financial and tax risk early warning is a top priority. This article analyzes the problems in the development of various industries in the financial and tax risk early warning, and establishes a risk prediction model.
Feature Similarity Learning Enhanced Knowledge Graph-based Convolutional Networks for Recommendation

In the recommendation algorithm based on the knowledge graph, the introduction of graph neural network effectively mines the rich auxiliary information in knowledge graph. While exploring the structure of graph neural network, more studies have begun to strengthen the mining of user historical interactive information. However, these methods ignore the similarity of user characteristics, so the learned embedding representation fails to fully reflect user preferences. In this article, we propose a new model FSKGCN. By modeling the similarities between user characteristics, we design an architecture that integrates and knowledge propagation and user similar characteristics. Specifically, FSKGCN develops a user feature similarity modeling layer, which calculates the similarity between user features by introducing a multi-head attention mechanism, and further encodes the user's high-order features according to the similarity score. Compared with other KG-based methods, FSKGCN provides a new architecture that combines knowledge information with user similar characteristics. We conducted CTR experiments on two real-world datasets, and the results show that FSKGCN is significantly better than the latest benchmark method.
DMQC Project: Design Technologies, Implementation, and Research of the Properties of a Digital Multi-Qubit Coprocessor

In this paper a project for further research in digital quantum computing is presented. The experimental basis of the project is a previously developed digital quantum processor, which has been implemented in FPGA and checked for execution of a quantum Fourier transform, that is the main quantum operation of the Shor's algorithm. The project is directed to development of the design technologies, implementation, and research of the properties of a digital multi-qubit coprocessor. The novelty of this project and the methodology of its implementation are investigated. The necessary hardware and tools for designing a digital multi-qubit coprocessor are described and the effects of using the coprocessor in various research fields are discussed.
Genesis of theoretical grounds for the use of triangulation in sociological research

The article considers the history of formation of qualitative and quantitative methodological approaches in sociology. The historical reconstruction of the fundamental ideas of representatives of these areas makes it possible to better understand their relationship, to determine the possibilities of their interaction in the study of social reality. In this regard, the article analyses the qualitative and quantitative strategy of sociological research taking into account their ontological, epistemological, axiological and methodological principles. A meaningful analysis of the three «waves», the stages of sociological cognition makes it possible to understand the specifics of the development of sociological knowledge, determine central ideas and values, and better understand the dynamics of the interaction of existing approaches and paradigms. Qualitative and quantitative methodological approaches in social sciences arose almost simultaneously, but their further development was extremely uneven, and their use to study and explain social reality for a long time was not based on the principles of parity interaction. The expansion of the practice of sociological research in a situation of high dynamics of social processes made it possible to increase the level of research culture, the understanding of values and cognitive differences in qualitative and quantitative methodologies made it possible and necessary to use them together. The basis for combining qualitative and quantitative methodological approaches is their functional additionality, which is expressed in the ability to most fully represent various aspects of the study of the same subject area. We are talking about methodological, conceptual triangulation as a strategy for the interaction of qualitative and quantitative approaches in the study of social problems using qualitative and quantitative methods for collecting and analysing sociological information. Such a technique increases the completeness and reliability of the received information, expands research capabilities.
Blueprinting BlockchainsIntroduction

Abstract The final chapter dives deeper into the territory of spin-off blockchains offered as technological modes of organisation for decentralising a host of socioeconomic practices. Recent discussions of platform capitalism are used to critique claims that blockchains are an incorruptible mode of democratic governance. Instead, blockchain capitalism is offered as a more accurate transaction model where profit necessitates certain points of centralisation through dominant distributed ledger technologies. A close examination of blockchain typologies reveals the co-option of these architectures by the very centralised banking firms and governments they were initially designed to bypass. As financial giants and central banks design their own distributed ledgers to increase the efficiency of business practices and monetary policy, innovation from the disruptive edges is once again absorbed into “the centre” by the corporate/state powers that be.
A Comparative Study on Indian Sign Language Representation

Communication among people can happen with the help of verbal or nonverbal language. Nonverbal communication is shared only among the hearing and speech impaired and is not common among others. Non-verbal communication is also different for different countries around the world. A solution to remove the gap between verbal and non-verbal communicators is to create an automated language translation model that can effortlessly convert sign language to text or audio. This area has been under research for a long time, but an economical and robust system that can efficiently convert signs into speech still does not exist. This paper focuses on different approaches that were put forward to turn Indian sign language into audio signals. The Sign Language Recognition (SLR) system is classified as isolated and continuous sign language models based on its input.
Applying GA-SVM for Optimizing Statistical and Semantic Features in Document Classification

The objective of this research is to develop a hybrid model for optimizing the performance of text classification techniques. The authors applied the Genetic Algorithm and Multi-Class Support Vector Machine on the publicly available datasets viz. 20 Newsgroup corpus, and the Reuters 21,578 corpus. They also used their handcrafted 'Creative corpus' prepared by collecting news articles from the Times of India news portal. They evaluated the performance of their model on large as well as small corpora. They employed the Genetic Algorithm that dynamically decides the weights of the contextual features to achieve the highest classification accuracy. The model achieves the highest accuracy of 100 % on small datasets of Reuters 21,578 and Creative corpus. The authors also presented a comparative analysis of the statistical and context-based approaches applied for the text classification. Based on the experimental results they proved that statistical approaches are better for text classification in the case of small-sized documents. Whereas the context-based approaches are efficient in the classification of huge documents enriched with text. This showed the importance of the hybrid approach. The hybrid approach taps the power of ontological databases and can adapt to varying corpora flawlessly. Thus, it makes effective use of textual data available in reports for crime detection, crime classification, and disease diagnosis, etc.
An Eye-Tracking Study on EFL Learners’ Subject-Verb Agreement Processing

Eye-tracking technology enables us to collect and analyze a wide range of eye movement measures, to connet these to language processing, and to gain insight into online language processing (Conklin, Pellicer-S&#225;nchez, and Carrol, 2018). The current study aims to investigate how EFL university students process verb agreement in the sentences with subject of complex noun phrases. Two types of subject noun phrases were investigated: syntactic process induced with single token and semantic process induced with multiple token. Overall, analyses of eye-movements revealed that the participants showed distinct processing patterns for single and multiple tokens.
MULTIPLE: Multi-level User Preference Learning for List Recommendation

On many streaming platforms, several individual items with certain common correlations are organized into lists, making list recommendation a distinctive and significant task. Multiple data including user-list interactions, user-item interactions and the list-item hierarchical structure are available for list recommendation. However, existing list recommendation models fail to conduct in-depth analyses of user preferences implied in the interactions and also fail to make use of the sequential information among interactions. In this paper, we propose a model named MULTIPLE, which recognizes the structural and compositional complexity of user preferences and explicitly learns user preferences from different perspectives, i.e., list-level, item-level and dual-level. In particular, MULTIPLE designs a gated and attentive sequence learning module to identify the drift of user preferences. By taking the list-item hierarchical structure as a bridge, MULTIPLE designs an attention network to distill essential items from the constituent items of lists and help learn the dual-level user preferences. We conduct extensive experiments on real-world datasets. The performance enhancement on recall@K and NDCG@K verifies the effectiveness of the model.