Reviewerly/exHarmony huggingface dataset:
Five Essential Factors for Data Sharing

This white paper, published by Springer Nature, proposes and discusses key ways to improve on research data sharing practices. The related infographic is available in figshare: https://doi.org/10.6084/m9.figshare.7937195.v1The report builds on previous white papers on research data sharing, summarising the findings and data from large-scale surveys into researcher views and behaviours. The findings are summarised as five essential factors on what needs to be done to make good research data practice common and widespread. This includes an analysis of the currently data policy landscape and impacts, researcher motivations for sharing data and practical solutions to making data more FAIR.Specifically, this follows up with research conducted in China and Japan to complement the existing outputs based in North America and Europe. The report also summarises evidence from external sources on the impact of, and need for, data sharing.All reports and summary data are openly available in the figshare repository from the links below. Data were collected in online surveys run between 2017-2019 and are stored in Excel spreadsheet format. Individual surveys vary slightly in methodology, with respondents self-selecting to an extent. See individual datasets for more detail. Additional data supporting the findings of this report (for example the 2016 Nature online survey on reproducibility) are linked under References.Practical Challenges for Researchers in Data Sharing White paper: https://doi.org/10.6084/m9.figshare.5975011Full survey dataset: https://doi.org/10.6084/m9.figshare.5971387.v2Infographic: https://doi.org/10.6084/m9.figshare.5996786.v4The State of Open Data Report 2018 White paper: https://doi.org/10.6084/m9.figshare.7195058.v2Full survey dataset: https://doi.org/10.6084/m9.figshare.7234985.v1Interactive data: https://knowledge.figshare.com/articles/item/state-of-open-data-2018Challenges and Opportunities for Data Sharing in China White paper: https://doi.org/10.6084/m9.figshare.7718441.v1Full survey dataset: https://doi.org/10.6084/m9.figshare.7321604.v1Infographic: https://doi.org/10.6084/m9.figshare.7782761Research Data Sharing in Japan White paper: forthcoming, 2019 Full survey dataset: https://doi.org/10.6084/m9.figshare.6328952.v1 Infographic: https://doi.org/10.6084/m9.figshare.6609056.v1
Improving the Usability of Voice User Interfaces: A New Set of Ergonomic Criteria

Technological progress has made possible to create voice user interfaces that feel more natural, as smart speakers notably show. As those develop, issues of usability have also been spotted that underline the fact that the way we are used to designing interfaces do not apply directly to voice inter-actions. Since heuristic and ergonomic criteria are the first way designers can improve their interfaces, we propose a new set of criteria for VUIs, based for the first time on an extensive review of academic and professional guidelines, and plugged into existing general heuristics. These criteria underline the need for VUIs to make up for the lack of visual cues, to take into account the social nature of conversations, as well as the need to own the fact that talking objects will be interpreted as full characters with a personality, and therefore need to be designed as such.
Experimental research of motivational and reflexive components of students’ reading culture

The purpose is to analyze the state of studying the problem of students' reading culture development using psychological, philological, pedagogical, sociocultural, and communicative approaches and to present the results of diagnostics of the real state of its structural components. Interdisciplinary analysis of psychological, pedagogical, methodical, culturological, and linguistic literature, study of normative documents of the Ministry of Education and Science of the Republic of Kazakhstan, conceptual and terminological analysis of basic definitions of the research, system analysis, pedagogical observation, pedagogical experiment, and student questionnaires. The authors defined structural components of students’ reading culture. The motivation and reflexive activity were defined as the components of the students' reading culture. To determine the expressiveness of these qualities, we used methodology of determining the level of reflexivity, as well as diagnostics of the motivational structure of personality. The 1st and 3rd year students specializing in Pedagogy and Psychology (5B010300) at the Abai Kazakh National Pedagogical University (Almaty city) took part in the experiment. The results of the experiment showed that the motivation for reading literature among students is below average. The levels of reflexivity for activities are high. The conducted content analysis of the category of reading culture substantiated its essence.
Group Signature with Verifier-Local Revocation Based on Coding Theory

Group signature with verifier-local revocation (VLR-GS) is a special variant of revocable group signature that not only allows a user to anonymously sign messages but also only requires the verifiers to possess some up-to-date revocation information. To date, a number of VLR-GS schemes have been proposed under bilinear groups and lattices, while they have not yet been instantiated based on coding theory. In this paper, we present a code-based VLR-GS scheme in the random oracle model, which is the first construction to the best of our knowledge. Concretely, our VLR-GS scheme does not rely on the traditional paradigm which utilizes an encryption scheme as a building block and achieves logarithmic-size group signature. To obtain the scheme, we first introduce a new code-based Stern-like interactive zero-knowledge protocol with member revocation mechanism based on syndrome decoding problem. Moreover, we employ the binary Goppa code embedded for our scheme with efficiency and security analysis.
Multi-View Representation Learning via Dual Optimal Transportation

Recently, multi-view representation learning has gained rapid growth in various fields. Most of previous multi-view learning methods rely on strong notions of distances that often provide no useful gradients in deep network training, which greatly degrades the performance in merging complementary information of views. To address this challenge, a multi-view representation learning network with dual optimal transportation (MDOT-Net) is proposed to capture fusion representations embedded in a common manifold with the weak topology. In MDOT-Net, the multi-view representation learning is modelled as an optimal transportation (OT) problem in manifold fitting, which is further decomposed into the intra-view OT and the inter-view OT. The intra-view OT is implemented by a view-specific adversarial variational network, which accurately captures local manifold structures within views by leveraging fusion knowledge. The inter-view OT is implemented by a view-fusion adversarial inference network, which models fusion representations compatible with diversities of sub-manifolds by utilizing view-specific knowledge. The two OTs boost mutually by providing prior knowledge to each other in multi-view representation learning. Finally, numerous experiments are conducted on four benchmark datasets, and the results demonstrate MDOT-Net achieves the state-of-the-art performance.
Intelligent System for Ranking Big Data in Search Engine

The spread of Internet sources has increased the volume of big data that is difficult to handle in traditional ways. So, most users need modern search systems to facilitate the search and retrieval of information in the presence of big data. However, the main challenge in the first and second conventional generations of search engines are linking different web data based on the syntax of keywords not on the semantic meaning and without a knowledge base. This manuscript proposes a framework based on modern technologies such as ETI processes, ontology graphs, and indexing RDF using wide column NoSQL technique. The main contribution of our work is introducing a mathematical model that is used to calculate the similarity score between a query and stored RDF documents based on semantic relations. Various operations were carried out to measure the proposed model's efficiency using data sources such as DBpedia, YAGO dataset. According to experimental results, the proposed model is achieving high precision compared to other related systems.
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network

Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.
Fast and Accurate Optimizer for Query Processing over Knowledge Graphs

This paper presents Gpl, a fast and accurate optimizer for query processing over knowledge graphs. Gpl is novel in three ways. First, Gpl proposes a type-centric approach to enhance the accuracy of cardinality estimation prominently, which naturally embeds the correlation of multiple query conditions into the existing type system of knowledge graphs. Second, to predict execution time accurately, Gpl constructs a specialized cost model for graph exploration scheme and tunes the coefficients with target hardware platform and graph data. Third, Gpl further uses a budget-aware strategy for plan enumeration with a greedy heuristic to boost the overall performance (i.e., optimization time and execution time) for various workloads. Evaluations with representative knowledge graphs and query benchmarks show that Gpl can select optimal plans for 33 of 39 queries and only incurs less than 5% slowdown on average compared to optimal results. In contrast, the state-of-the-art optimizer and manually tuned results will cause 100% and 36% slowdown, respectively.
Geological Landslide Disaster Monitoring Based on Wireless Network Technology

With the comprehensive influence of natural evolution and human activities, the damage degree of geological disasters is increasing. How to effectively early warning geological disasters has become a problem of concern. How to effectively provide early warning of geological disasters has become a concern of people. This research mainly discusses the geological landslide disaster monitoring based on wireless network technology. First, establish two important early warning indicators of rainfall and geological landslide displacement. The monitoring system is powered by a rechargeable 12V lithium battery, combined with solar panels, which can be charged when the sun is full to ensure the stable operation of the system. The AT45DB161B chip with 16M bytes storage capacity is selected to store data such as geological landslide displacement and rainfall. Use Microsoft SQL Server 2008 database management system to complete database content query, addition, modification, and deletion operations. The TLP521-2 photocoupler is used to isolate the GPIO interface of STM32 from the external unit to improve the anti-interference ability. The communication between the field data collector and the monitoring center data server adopts the GPRS packet data transmission method based on the TCP/IP protocol. Currently, the PDU in the network is an IP data packet. The realization of the TCP/IP protocol at the field data collector is all completed in the master single-chip microcomputer. Use SIEMENSMC35GSM/GPRS module as data transmission terminal. The monitoring results show that the absolute error of the test data does not exceed 6mm in the horizontal distance, the vertical height difference does not exceed 9mm. The results show that the monitoring of geological landslide based on wireless network technology improves the accuracy of distance estimation and reduces the positioning error, which can provide scientific guidance for the planning, monitoring and early warning of landslide area.
Forensic Analysis on False Data Injection Attack on IoT Environment

False Data Injection Attack (FDIA) is an attack that could compromise Advanced Metering Infrastructure (AMI) devices where an attacker may mislead real power consumption by falsifying meter usage from end-users smart meters.Due to the rapid development of the Internet, cyber attackers are keen on exploiting domains such as finance, metering system, defense, healthcare, governance, etc. Securing IoT networks such as the electric power grid or water supply systems has emerged as a national and global priority because of many vulnerabilities found in this area and the impact of the attack through the internet of things (IoT) components.In this modern era, it is a compulsion for better awareness and improved methods to counter such attacks in these domains.This paper aims to study the impact of FDIA in AMI by performing data analysis from network traffic logs to identify digital forensic traces.An AMI testbed was designed and developed to produce the FDIA logs.Experimental results show that forensic traces can be found from the evidence logs collected through forensic analysis are sufficient to confirm the attack.Moreover, this study has produced a table of attributes for evidence collection when performing forensic investigation on FDIA in the AMI environment.
Smart contracts in the context of digitalization: the legal realities of world experience

The development of digital technologies is forcing lawyers to analyze phenomena that have recently looked fantastic. This means that a phenomenon like smart contracts has ceased to be a theoretical idea of improving commodity monetary transactions and now needs a legal justification. In this article, we have analyzed smart contracts in terms of their belonging to digital technologies and the legal field, that is, how they can be equated with legal agreements, if they are their analogues, what application can it find given the legislation in the field of civil (contractual) law. The purpose of our study was to establish a link between smart contacts and their legal regulation, with the feature of smart contract implementation perspectives. The methodology used are systemic and formal-legal methods, as well as methods of analysis and synthesis. The results found highlight that a smart contract is a computer code that is entered into a blockchain network to execute a transaction, the usual expression of which is an agreement between the parties. From a legal point of view, smart contracts are only a part of ordinary agreements (contracts) under the practice of international and national law.
Evaluation of Cloud-Based Ethereum Network Performance

In this study, we evaluate the performance of the cloud-based Ethereum network. We researched 3 Ethereum networks, namely: Ethereum mainnet, Ethereum testnet Ropsten, and Ethereum testnet Rinkeby. We analyzed the computational resource utilization required to run the Ethereum node for a month as well as the costs involved.
Providers' Perceptions of Radical Technological Innovation (RTI) in Healthcare: An Exploratory Study Using Chatbot Technology as an Exemplar

Radical technological innovations (RTIs) transformed how healthcare professionals disseminate information and improved the processes by which they care for their patients. Artificial Intelligence (AI) in particular is spawning many tools, such as chatbot systems, which have the capability to transform patient care. Despite its potential benefits, they cannot be integrated into practice without the support of the healthcare providers. This qualitative study explores the perceptions and cognitions of providers in healthcare organizations regarding RTI and to determine how their previous experiences with technological change affect these perceptions. The insights generated from this study help to discover intra-organizational and inter-organizational factors that influence the adoption of technology in the healthcare context.
Information System Security Protection Based on SDN Technology in Cloud Computing Environment

Cloud computing is a modern computing mode based on network, which is widely participated by the public, and provides virtualized dynamic computing resources in the form of services. Cloud computing builds an effective communication platform with the help of computer internet, so that users can get the same computing resources even if they are in different areas. With its unique technical characteristics and advantages, cloud computing has been deployed to practical applications more and more, and the consequent security problems of cloud computing have become increasingly prominent. In addition to the original cloud computing environment, this paper proposes to build a secure cloud with cloud technology, deploy security agents in the business cloud, connect the business cloud, security cloud and security agents through SDN (software defined network) technology, and dynamically divide the business cloud into logically isolated business areas through security agents. Therefore, security is separated from the specific implementation technology and deployment scheme of business cloud, and an information security protection scheme under cloud computing environment is proposed according to the characteristics of various factors, so as to enhance the security of network information.
A Study on a Distributed Data Fabric-based Platform in a Multi-Cloud Environment

In a multi-cloud environment, it is necessary to minimize physical movement for efficient interoperability of distributed source data without building a data warehouse or data lake. And there is a need for a data platform that can easily access data anywhere in a multi-cloud environment. In this paper, we propose a new platform based on data fabric centered on a distributed platform suitable for cloud environments that overcomes the limitations of legacy systems. This platform applies the knowledge graph database technique to the physical linkage of source data for interoperability of distributed data. And by integrating all data into one scalable platform in a multi-cloud environment, it uses the holochain technique so that companies can easily access and move data with security and authority guaranteed regardless of where the data is stored. The knowledge graph database mitigates the problem of heterogeneous conflicts of data interoperability in a decentralized environment, and Holochain accelerates the memory and security processing process on traditional blockchains. In this way, data access and sharing of more distributed data interoperability becomes flexible, and metadata matching flexibility is effectively handled.
Scalable and Modular Robustness Analysis of Deep Neural Networks

As neural networks are trained to be deeper and larger, the scalability of neural network analyzer is urgently required. The main technical insight of our method is modularly analyzing neural networks by segmenting a network into blocks and conduct the analysis for each block. In particular, we propose the network block summarization technique to capture the behaviors within a network block using a block summary and leverage the summary to speed up the analysis process. We instantiate our method in the context of a CPU-version of the state-of-the-art analyzer DeepPoly and name our system as Bounded-Block Poly (BBPoly). We evaluate BBPoly extensively on various experiment settings. The experimental result indicates that our method yields comparable precision as DeepPoly but runs faster and requires less computational resources. Especially, BBPoly can analyze really large neural networks like SkipNet or ResNet that contain up to one million neurons in less than around 1 hour per input image, while DeepPoly needs to spend even 40 hours to analyze one image.
An Effective Approach for Classifying Acute Lymphoblastic Leukemia Using Hybrid Hierarchical Classifiers

Acute Lymphoblastic Leukemia is an anomaly that affects White Blood Cells. This type of cancer occurs when there is an error in blood cell DNA. Children are prone to this type of cancer. After the initial stages, it spreads to other organs like the liver and spleen. The problem with this type of cancer is that, unlike other forms of cancer, it doesn’t cause any tumors hence it is very hard to detect. Manual testing methods were used before the automation, but it was time-consuming and very much prone to errors. To solve that problem, automated testing methods were introduced. Different systems [1, 3, 4] were introduced in the past, but most of them have variable accuracies. These Automated Systems [1, 3, 4] used image processing and unsupervised machine learning techniques to classify the images into cancerous and healthy. The proposed system uses Hybrid Hierarchical Classifiers to classify the cancer cells, which will be an improvement over the previous systems and solves the problem of variable accuracies.
Application of Cluster Analysis Algorithm in the Construction of Education Platform

With the rapid development of the Internet, people use the Internet to carry out social and learning needs to continue to improve. Like other industries, the education field is more and more aware of the importance of education platform, and constantly applies its new functions to education and teaching. Clustering algorithm provides an effective evaluation method for the evaluation of students’ personality and learning status in distance education. It can provide targeted guidance and learning strategies according to the clustering analysis results. This paper studies the construction of English teaching education platform based on clustering analysis algorithm, aiming at exploring new methods of English teaching by using new network means, and trying to solve some problems existing in junior middle school English teaching, and puts forward some new ideas for improving English teaching.
Relation Extraction with Word Graphs from N-grams.

Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios. To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention over the graph. Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly. Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.