Reviewerly/exHarmony huggingface dataset:
An improved text classification modelling approach to identify security messages in heterogeneous projects

Abstract Security remains under-addressed in many organisations, illustrated by the number of large-scale software security breaches. Preventing breaches can begin during software development if attention is paid to security during the software’s design and implementation. One approach to security assurance during software development is to examine communications between developers as a means of studying the security concerns of the project. Prior research has investigated models for classifying project communication messages (e.g., issues or commits) as security related or not. A known problem is that these models are project-specific, limiting their use by other projects or organisations. We investigate whether we can build a generic classification model that can generalise across projects. We define a set of security keywords by extracting them from relevant security sources, dividing them into four categories: asset, attack/threat, control/mitigation, and implicit. Using different combinations of these categories and including them in the training dataset, we built a classification model and evaluated it on industrial, open-source, and research-based datasets containing over 45 different products. Our model based on harvested security keywords as a feature set shows average recall from 55 to 86%, minimum recall from 43 to 71% and maximum recall from 60 to 100%. An average f-score between 3.4 and 88%, an average g-measure of at least 66% across all the dataset, and an average AUC of ROC from 69 to 89%. In addition, models that use externally sourced features outperformed models that use project-specific features on average by a margin of 26–44% in recall, 22–50% in g-measure, 0.4–28% in f-score, and 15–19% in AUC of ROC. Further, our results outperform a state-of-the-art prediction model for security bug reports in all cases. We find using sound statistical and effect size tests that (1) using harvested security keywords as features to train a text classification model improve classification models and generalise to other projects significantly. (2) Including features in the training dataset before model construction improve classification models significantly. (3) Different security categories represent predictors for different projects. Finally, we introduce new and promising approaches to construct models that can generalise across different independent projects.
Development Factors of Blockchain Technology Within Banking Sector

A concept of blockchain technology is a hybrid of mathematics, information technology and cryptography. This combination of well know technologies allows treat blockchain like a solution which can move banking sector to the next level of development. Blockchain technology can be also identified as a disruptive tool with a promise of huge implementation potential. Nevertheless, blockchain technology should be treated only in category of support for existing systems and models in the perspective upcoming years. The process of implementation, adoption and diffusion of blockchain technology within global business requires capital, recourses and reengineering currently understanding of global economy. Blockchain features especially valuable for banking i.e.: transparency, immaturity, safety, publicity, cost saving, time stamped and speed. The global banking sector is currently exploring implementation potential of blockchain technology in the context of payments, services, financial management. The main aim of the article is to clarify the implementation potential of blockchain technology, the risk related to implementation process, challenges and opportunities produced by blockchain for banking sector. The paper is aimed at critically analyzing the impact of blockchain technology for banking industry. Methods applied for this paper are case study and literature review.
An Analysis of Iranian University Library Websites from Standpoint Five Effective Factors on Google SEO : Iranian University Library Websites and Google SEO

The main purpose of this study was, analysis of library websites of Iranian governmental universities affiliated with the ministry of science from Standpoint, five effective factors on Google Search Engine Optimization (SEO). First, the five factors that impact Google SEO, by the Google Help Center and some previously published articles, Identified. Then 42 library websites of governmental universities affiliated with the ministry of science exception for Payam Noor and Farhangian located in Tehran and other metropolitan cities of Iran with descriptive survey methods were comparison analyses. Data collection tools were analytical databases W3C, Similarweb, Ahrefs. The data analysis tool was "LibreOfficeCalc" software. All stages of the evaluation of library websites from standpoints, SEO factors, approved by eight Google SEO experts. In the four factors impacting Google SEO, the top 11 websites of the research community (42 websites) were identified. Also, Just, 33% of the research community, used "Digital Security Certificate" or HTTPS Protocol for their university library websites (fifth effective factor). It's worthy, the university library managers in Iran, have high pay attention to improve Google SEO and effective factors on this. Previous studies have noted the role of search engine optimization in libraries or academic libraries, but accurate measurement of five effective factors on Google SEO, in Iranian university library websites, is the innovation of this study.
PSYCHOLOGICAL AND PEDAGOGICAL CONDITIONS OF THE INTERNALITY FORMATION AS A FACTOR OF THE FUTURE PSYCHOLOGISTS’ PERSONAL AND PROFESSIONAL SELF-DEVELOPMENT

The article considers the psychological and pedagogical conditions of personal and professional self-improvement of future professionals, including psychology students. Such conditions include technologies of self-education and self-education, specially designed forms, methods and approaches of interactive work, focused on stimulating of motivation and expanding of self-development resources. In the process of professional training of future psychologists, specially designed training sessions are crucial if specific content and methodological requirements for targeted training of personal and professional self-development are taken into account. Usually such training is realized by activating the internal factors of self-improvement process, one of which is internality - a personal property that acquires special significance in the context of the specifics of a psychologist’s professional activity. It is assumed that an effective psychological and pedagogical condition for the formation of internality as a factor of future psychologists’ personal and professional self-development is a specially organized educational activities of students. In this regard, a special training course "Theoretical and methodological foundations of the future psychologists’ internality formation" was designed. The methodological basis of special training course is a personal-acmeological approach and experimentally substantiated data concerning the results and means of applicants’ for higher education personal and professional self-development. The purpose, tasks, psychological and pedagogical conditions of formation of students’ internality by educational materials are formulated. The structure of the special course, the content of classroom and independent work are revealed, the forms of control are offered. The content and methodological features of the special course, which contribute to the formation of internality as a factor of future psychologists’ readiness for personal and professional self-development, and the expected learning outcomes are highlighted.
Visualisation of Temporal Network Data via Time-Aware Static Representations with HOTVis

The visual analysis of temporal network data is often hindered by the cognitively demanding nature of dynamic graphic visualizations. Addressing this issue, the graph visualization tool HOTVis generates time-aware static network visualizations that highlight the causal topology of temporal networks, i.e. which nodes can directly and indirectly influence each other, and are thus considerably easier to interpret than state-of-the-art dynamic graph visualizations.
Fast recovery of correlated failures in distributed stream processing engines

In a large-scale cluster, correlated failures usually involve a number of nodes failing simultaneously. Although correlated failures occur infrequently, they have significant effect on systems' availability, especially for streaming applications that require real-time analysis, as repairing the failed nodes or acquiring additional ones would take a significant amount of time. Most state-of-the-art distributed stream processing systems (DSPSs) focus on recovering individual failures and do not consider the optimization for recovering correlated failure. In this work, we propose an incremental and query-centric recovery paradigm where the recovery of failed operator partitions would be carefully scheduled based on the current availability of resources, such that the outputs of queries can be recovered as early as possible. By analyzing the existing recovery techniques, we identify the challenges and propose a fault-tolerance framework that can support incremental recovery with minimum overhead during the system's normal execution. We also formulate the new problem of recovery scheduling under correlated failures and design algorithms to optimize the recovery latency with a performance guarantee. A comprehensive set of experiments are conducted to study the validity of our proposal.
Proof of Random Trust Consensus Mechanism for Power Resource Sharing System

With the development of society, traditional centralized management can’t satisfy reliability and safety of power resource sharing network. Blockchain has the characteristics of decentralization, traceability, and openness. It can provide a safe and reliable environment for untrusted parties without a third party. Thus, blockchain technology can realize a safe and reliable distributed power resource sharing network. However, a major challenge is to find a consensus mechanism that is suitable for power resource sharing network. Therefore, in order to solve the challenge, we propose a lightweight consensus mechanism, Proof of Random Trust(PoRT). Compared with Proof of Work(PoW), PoRT overcomes the shortcomings of large-scale of calculation and concentration of power. Different from traditional BFT algorithm, PoRT has better scalability. At the same time, PoRT realizes the separation of transaction verification and block verification and improves the efficiency of block generation. Moreover, PoRT uses random trust value to increase the degree of decentralization,so fault tolerance, attack resistance,and collusion resistance are improved.
Unravelling the Human Perspective and Considerations for Urban Data Visualization

Effective use of data is an essential asset to modern cities. Visualization as a tool for analysis, exploration, and communication has become a driving force in the task of unravelling our complex urban fabrics. This paper outlines the findings from a series of three workshops from 2018-2020 bringing together experts in urban data visualization with the aim of exploring multidisciplinary perspectives from the human-centric lens. Based on the rich and detailed workshop discussions identifying challenges and opportunities for urban data visualization research, we outline major human-centric themes and considerations fundamental for CityVis design and introduce a framework for an urban visualization design space.
Modelling user reactions expressed through graphical widgets in intelligent interactive systems

Nowadays, most interactive social systems allow users to react to their contents and exploit user reactions to provide intelligent behaviours, such as adaptation or recommendation. Therefore, carefully understanding and designing the user/system dialogue that revolves around reaction provisioning is a crucial aspect. In this paper, we introduce the UpRISEmodel with the aim of formally describing the user/system interaction while providing and using reactions. Then, we show how this model can be used to formally represent and describe interactive social systems that collect user reactions, as well as to compare them. In addition, we exemplify how the UpRISEmodel can provide a sort of checklist that stimulates system designers to approach design/redesign tasks involving user reactions in a thorough and well-structured manner, suggesting all the possibly relevant points with respect to different usability and performance-related goals. This approach can be seen as the first step towards more transparency in the design of intelligent interactive systems.
The determine factors of student satisfaction with e-learning in Malaysia Higher Education Institutions: a scoping review

Abstract The aim of this scoping review is to investigate work published on student satisfaction with e-learning in Malaysia Higher Learning Institutions (HLIs). A scoping review was conducted using the methodological framework of Arksey and O’Malley to identify a broad range of relevant literature, regardless of study design. A systematic search was performed using Google Scholar in May 2020 and then was updated in October 2020. The database was searched for literature published between 1 st January 2019 and October 2020. Two authors independently assessed literature eligibility and extracted data to answer our research question ‘From the previous literature, what is the effects of e-learning/LMS on student learning satisfaction in Malaysia Higher Learning Institutions?’ Seven articles were included in the review. There appears to be a paucity of research regarding student satisfaction with e-learning in Malaysia HLIs. Various theories and models have been included in the papers. However, all the articles mentioned the factors that determine student satisfaction with e-learning. The study demonstrates that when e-learning is delivered purposefully and effectively in the management and support of student learning, it has a positive effect on student satisfaction. However, more research in this area is required.
LaPOLeaF: Label propagation in an optimal leading forest

This paper presents an efficient graph semisupervised learning (GSSL) method that meets the criterion of optimization without iterations. Most existing GSSL methods require iterative optimization to achieve a preset objective because they consider data points to be in peer-to-peer relationships. Additionally, existing GSSL methods must learn from scratch for unseen data because graph structures are specifically built for a given dataset. By leveraging the partial order relationships induced by the local density and distances between data, we developed a novel label propagation algorithm based on the data structure of an optimal leading forest (OLeaF). The time complexity of our method is O(N) for both labeling unclassified data and labeling new data from a dataset after an OLeaF is constructed. Therefore, the two main weaknesses of traditional GSSL are addressed. Additionally, the constructed leading forest offers good interpretability for learning results. We scale the proposed method to accommodate big data by utilizing the block distance matrix technique and locality-sensitive hashing. Extensive experiments on datasets with different characteristics demonstrate the superior efficiency and competitive accuracy of the proposed method.
Extraction of large-scale geological anomalies and positioning regularities of rich deposits: A case study of the Minle sedimentary manganese deposit, Hunan, China

In the exploration of sedimentary manganese deposits, prospecting models with large-scale ore-generating geological anomalies are required to satisfy productive demands. This study presents a novel approach combining theories of sedimentary basin analysis with techniques of quantitative analysis of mineral resources for extracting large-scale geological anomalies based on variations of intra-manganese-basin tectonic and sedimentary environments. For this purpose, the Minle sedimentary manganese deposit in Hunan province, China, was taken as a case study. First, the stratigraphic and lithological characteristics were analyzed to divide the region on the scales of manganese basins and mining areas. Subsequently, suitable paleogeographic features were selected as ore-generating geological anomalies and analyzed for three-dimensional quantitative evaluation of mineral resources. Finally, the thicknesses and grades of ore bodies were quantitatively analyzed to summarize the positioning regularities of rich ores. Pre-mineralization tectonic and sedimentary environments were found to be closely related to the distribution of rich ores. Areas suitable for the formation of rich ores were found to be those that feature coarse clastic sedimentation at the edge of the barrier-tidal flat-lagoon system and experienced a shift in the stable transitional environment from the barrier-tidal flat-lagoon to the bay before mineralization. Large-scale sedimentary features, fault activities, structural styles, and evidence of multiple mineralization mechanisms should also be taken into account in the selection of ore-generating geological anomalies.
A method of workpiece location based on improved generalized Hough transform

Abstract In order to solve the problem that the workpiece is not easy to identify in the complex environment in the industrial processing, sorting and other automatic production links, a workpiece positioning method based on Improved Generalized Hough transform, also known as eif-ght, is proposed. In order to solve the problem of large time consumption and high memory consumption of traditional generalized Hough transform in target detection, this paper uses the rotation invariant feature angle formed by any two edge points in the image as the R table index under the condition of image preprocessing to realize the representation of target shape, and uses particle swarm intelligent evolutionary algorithm to speed up the search The final target is determined by similarity measurement. The experimental results show that when the workpiece rotates and shifts, and in the complex environment with noise, local occlusion, nonlinear illumination and so on, it can achieve stable and efficient positioning.
A new view to the spatial distribution of the Paleogene lithostratigraphic units in the Goren Chiflik Horst based on borehole data

The present article aims to provide new data on the spatial distribution and lithological features of the Paleogene lithostratigraphic units in the Goren Chiflik Horst, which is part of the Moesian Platform in Eastern Bulgaria. Eight formal Paleogene lithostratigraphic units were recognized on the basis of primary lithological data derived from 29 borehole sections. They are as follows: the Komarevo Fm. (Thanetian), the Beloslav Fm. (Ypresian), the Dikilitash Fm. (Ypresian), the Aladan Fm. (Ypresian), the Avren Fm. (uppermost Ypresian–lowermost Priabonian), the Dvoynitsa Fm. (Lutetian), the Dolni Chiflik Mb. of the Avren Fm. (Bartonian–Priabonian), and the Ruslar Fm. (Oligocene). Their spatial distribution and relationships are visualized by drawing up 11 geological cross-sections. Additional data on their lithology, thickness, boundaries and age are also given.
The Brain and the New Foundations of Mathematics

Many concepts in mathematics are not fully defined, and their properties are implicit, which leads to paradoxes. New foundations of mathematics were formulated based on the concept of innate programs of behavior and thinking. The basic axiom of mathematics is proposed, according to which any mathematical object has a physical carrier. This carrier can store and process only a finite amount of information. As a result of the D-procedure (encoding of any mathematical objects and operations on them in the form of qubits), a mathematical object is digitized. As a consequence, the basis of mathematics is the interaction of brain qubits, which can only implement arithmetic operations on numbers. A proof in mathematics is an algorithm for finding the correct statement from a list of already-existing statements. Some mathematical paradoxes (e.g., Banach–Tarski and Russell) and Smale’s 18th problem are solved by means of the D-procedure. The axiom of choice is a consequence of the equivalence of physical states, the choice among which can be made randomly. The proposed mathematics is constructive in the sense that any mathematical object exists if it is physically realized. The consistency of mathematics is due to directed evolution, which results in effective structures. Computing with qubits is based on the nontrivial quantum effects of biologically important molecules in neurons and the brain.
Zero-shot Multi-lingual Interrogative Question Generation for "People Also Ask" at Bing

Multi-lingual question generation (QG) is the task of generating natural language questions for single answer passage in any given language. In this paper, we design a system for supporting multi-lingual QG in the "People Also Ask" (PAA) module for Bing. For zero shot setting, the primary challenge is to transfer the knowledge from trained QG model in the pivot language to other languages without further addition of training data in these languages. Compared to other zero-shot tasks, the differentiating and challenging aspect in QG is to preserve the question structure so that the resulting output is interrogative. Existing models for similar tasks tend to generate natural language queries or copy sub-span of the passage, failing to preserve the question structure. In our work, we demonstrate how knowledge transfer in multi-lingual IQG (Interrogative QG) can be significantly improved using auxiliary tasks either in multi-task or pre-training task setting. We explore two kinds of tasks - cross-lingual translation and multi-lingual denoising auto-encoding of questions, especially when using translate-train. Using data for 13 languages from Bing PAA as well as online A/B tests, we show that both of these tasks significantly improve the quality of zero-shot IQG on non-trained languages.
Exploratory User Study on Graph Temporal Encodings

A temporal graph stores and reflects temporal information associated with its entities and relationships. Such graphs can be utilized to model a broad variety of problems in a multitude of domains. Re-searchers from different fields of expertise are increasingly applying graph visualization and analysis to explore unknown phenomena, complex emerging structures, and changes occurring over time in their data. While several empirical studies evaluate the benefits and drawbacks of different network representations, visualizing the temporal dimension in graphs still presents an open challenge. In this paper we propose an exploratory user study with the aim of evaluating different combinations of graph representations, namely node-link and adjacency matrix, and temporal encodings, such as superimposition, juxtaposition and animation, on typical temporal tasks. The study participants expressed positive feedback toward matrix representations, with generally quicker and more accurate responses than with the node-link representation.