Reviewerly/exHarmony huggingface dataset:
Scalable Quantum Networks with Artificial Atoms

The central challenge in building quantum computers and long-range quantum networks is distributing entanglement across multiple individually-controllable quantum memories. While proof-of-concept experiments are possible with today’s optical components, scaling these systems to tens, hundreds, or thousands of individually controllable quantum memories requires very-large-scale integration of electronic and photonic circuits.
QoS Aware Classification of Composite Web Services Using Rough Approximation

A typical Web service composition approach contains four phases: (1) construction of the compositions, (2) generation of executable plans, (3) classification of executable plans, and (4) selection and deployment of the best executable plan. This paper adopts and extends a graph based composition approach by using a more reliable classification method, namely Dominance-based Rough Set Approach (DRSA). This method is very suitable to take into account the Quality of Service (QoS) aspects in Web service composition. The paper also illustrates the application of the DRSA on a set of composite Web services and evaluates its performance.
Preparing Computing Graduates for the Workplace: An Assessment of Relevance of Curricula to Industry

This chapter documents research which investigated how relevant the content of the Computing courses offered within undergraduate programs of the Computing Department at the National University of Samoa (NUS) were to meet the needs of the industry and the workforce. The study, which was conducted in 2018–2019, surveyed 16 institutions and 20 graduates from the Computing programs. Findings from the survey indicated that the current course offerings within the Computing Department are to a large extent relevant to the needs of the industry and the workplace. Most of the recommendations from the current survey were similar to those in the 2008 and 2013 survey and had already been incorporated into the current curriculum. The main recommendations for improving the curriculum are an improved emphasis on operating systems, cybersecurity, strategic planning, and problem-solving skills and introduction of other database platforms such as Oracle as well as more web-based languages. Recommendations for improvement of the survey are (i) increasing the scope to include an evaluation of the postgraduate diploma, (ii) the inclusion in future surveys of questions on soft skills, and (iii) specific consideration of the graduate profile and learning outcomes.
Using 2.5D Sketches for 3D Point Cloud Reconstruction from A Single Image

3D reconstruction from a single image is a highly uncertain problem. Unknown information such as and depth of the self-occluded part of an object requires strong prior knowledge of the object. In order to make better use of prior information and generate high-quality 3D model, we first propose a 3D point cloud generator to obtain prior knowledge of the 3D point cloud in datasets. We then design a model to recover 3D shape of the object from a 2D image, which first estimates a 2.5D sketch from the input 2D image and then transfer the knowledge obtained in the 3D point cloud domain to the 2D image domain through the 2.5D sketch. Because 2.5D sketches are easier to obtain from 2D images and the information in a 2.5D sketch is more abundant than that in a 2D image Experiments demonstrate that our method is highly competitive to state-of-art works in 3D point cloud reconstruction on both synthetic datasets and real datasets.
Leadership Development in Students as Part of Attitude Development

Leadership development corresponds to the focus on the individual’s success and competitiveness strategy. This is the optimal direction of the organization of attitude development because it covers two aspects of the student’s personality development: professionally-oriented and self-centric. The aim of the study is to identify and compare the leadership level in second- and fourth-year students to see dynamics of development and implementation of the leadership phenomenon in the professional and personal making up of future specialists. The task was to: (1) diagnose the existing educational impact of the university environment on the formation of leadership competencies; (2) clarify the relationship between the objective level of leadership skills development and assessment of leadership characteristics by students themselves; (3) determining tactics for further support and / or stimulation of personal and professional leadership skills of students; (4) elucidating the relationship between leadership skills and academic performance; (5) determining the universal prerequisites for the formation of leadership qualities of students. Based on the theoretical analysis of the issue, the authors developed an objective and subjective diagnostic model for leadership skills. In this study, data of the objective diagnostic technique are the key. Subjective diagnostic technique for leadership skills provides insights for problem interpretation. At the level of the first group of respondents, the average Leadership Skills Level of the second-year students was quite low and was found within the medium level. The second group of respondents consisting of the fourth-year students showed a slight but effective improvement. The Leadership Skills of this group were found at a sufficient level. Positive dynamics was revealed for all criteria of leadership skills as a result of applying objective diagnostic methods: decreased percentage of students with negative and relatively low markers of Leadership Skills Level and corresponding increase in percentage of students with positive markers of Leadership Skills Level. Further research can be organized in the direction of identifying and developing successful universal and professionally-oriented tactics for leadership development in students as part of attitude development.
Information Security Awareness Evaluation Framework and Exploratory Study

Information security is among the most challenging problems in the contemporary highly interconnected network environment. Many past and recent studies reveal the human factor is the weakest element in the security systems. The present paper focuses on security awareness evaluation of key users regarding the information security vulnerabilities, threats and countermeasures. With purpose to develop a methodology for raising awareness of the users regarding information security challenges, an evaluation of the users’ needs of secure browsing and networking was conducted. The applied method was questionnaire with four major sections and the respondents’ results from five countries were analysed for validity and correctness. The results for the vast majority of the respondents revealed there is significant need for security training.
A sequence labelling approach for automatic analysis of ello: tagging pronouns, antecedents, and connective phrases

Encapsulators are linguistic units which establish coherent referential connections to the preceding discourse in a text. In this paper, we address the challenge of automatically analysing the pronominal encapsulator ello in Spanish text. Our method identifies, for each occurrence, the antecedent of the pronoun (including its grammatical type), the connective phrase which combines with the pronoun to express a discourse relation linking the antecedent text segment to the following text segment, and the type of semantic relation expressed by the complex discourse marker formed by the connective phrase and pronoun. We describe our annotation of a corpus to inform the development of our method and to finetune an automatic analyser based on bidirectional encoder representation transformers. On testing our method, we find that it performs with greater accuracy than three baselines (0.76 for the resolution task), and sets a promising benchmark for the automatic annotation of occurrences of the pronoun ello, their antecedents, and the semantic relations between the two text segments linked by the connective in combination with the pronoun.
A Secure Blockchain-Based Solution for Management of Pandemic Data in Healthcare Systems

This paper tackles the issue of data manipulation in pandemics wherein often data is manipulated or underreported by certain government bodies for various reasons. This problem came to light during the first few months of the COVID-19 pandemic and had a major effect on the public health response in countries. To solve this issue, a blockchain based system and implementation is proposed to allow hospitals and testing centers to transmit their data while also minimizing the risk of manipulation from government bodies. The paper makes use of the United States CDC framework for data reporting by testing labs and hospitals in order to develop this proposal for a blockchain system. To accomplish this, the paper then delineates the implementation of private data collections and other features of the Hyperledger software. A potential web-app interface is also presented and the transaction logic has been described in detail. Along with this, an evaluation of the risks, limitations and potential extension of the system has been performed.
Consideration of Presentation Timing in Bicycle Navigation Using Smart Glasses

In recent years, applications for bicycle navigation and navigation devices for bicycles attached near bicycle handlebars have appeared. The numbers of outings and home deliveries using them are increasing. However, when checking and operating these navigation systems, one must devote close attention to the handle. Safety confirmation of the surroundings can therefore be neglected. As described herein, we use smart glasses as a means to resolve this difficulty. Because smart glasses for cyclists are used overseas, investigation results have shown that much unnecessary information exists for navigation and show them to be unsuitable for navigation. Therefore, this research was conducted to examine the navigation application specifically and to verify the contents necessary for a system suitable bicycle riding. We examine and evaluate the appropriate timing for presenting information.
Fair Exposure: A Multi-stakeholder Personalized Recommendation System Based on Multi-objective Optimization

The conventional way of recommendation is exclusively focused on end-user personalization and accuracy improvement. Nowadays, business models are adapting a multi-sided fairness policy, and the multi-sided fairness could be achieved with a multi-stakeholder recommendation model. Recent studies suggested that the existing end-user focused recommendation model lacks in justifying other stakeholders’ needs and does not create any personalized recommendation environment. Thus, this research proposes a multi-objective optimization-based personalized recommendation framework suitable for preferences for every stakeholder in the system. Particularly this work considers the two stakeholders in the proposed recommendation model producer and system. We design two dedicated objective functions to address the preferences of producer and system stakeholders. The producer’s objective function is to enhance the producer’s exposure in the recommendation list, and the objective function of the system is intended to observe balance and fair recommendation. Next, to optimize the conflicting utilities of stakeholders, we propose a stakeholder optimization-based recommendation framework. The proposed model aims to improve utility gain and establish a balanced trade-off recommendation solution between stakeholders. The proposed model is evaluated based on cumulative utility gain against the baseline algorithms. The evaluation result describes the significant improvement in cumulative utility gain when the standard algorithms are altered with proposed objective functions.
Total Variation Regularized Low-Rank Tensor Decomposition with nonlocal for single image denoising

Various noises in the image will reduce the quality of the image and seriously affect the processing of subsequent computer tasks. The recovery of single images is a more challenging problem than recovery of spectral images due to the lack of spectral information. In order to solve this problem, in this paper, we propose a method combining non-local self-similar priors and tensor decomposition to fully explore the inherent low-rank structure of a single image. Specifically, we use tucker decomposition to characterize the global self-similar patch of a single image. At the same time, we introduce anisotropic spatial-spectral total variation regularization to describe the segmented smooth structure in the image. In order to deal with the complex noise situation in the real scene. We model the noise in two parts, one part is sparse spot noise, and the other part is ubiquitous noise. Then we use the augmented Lagrange multiplier method to solve it. Experiments have proved that the introduction of non-local self-similar priors is crucial to the denoising problem of a single image. The proposed method is superior to all comparison methods.
METHODOLOGICAL ASPECTS OF TEACHING THE DISCIPLINE «PHILOSOPHY» IN THE FRAMEWORK OF DISTANCE LEARNING IN HIGHER EDUCATION

The paper analyzes the methodological aspects of teaching the discipline «Philosophy» in higher education in the situation of distance education and focuses on the need to preserve the holistic epistemological anthroposophical discourse and multidimensional, system-forming knowledge in the «digital» learning format.
Estimating Human Legibility in Historic Manuscript Images - A Baseline

For accessing degraded historic text sources, humanist research increasingly relies on image processing for the digital restoration of written artifacts. A problem of these restoration approaches is the lack of a generally applicable objective method to assess the results. In this work we motivate the need for a quality metric for historic manuscript images, that explicitly targets human legibility. Reviewing previous attempts to evaluate the quality of manuscript images or the success of text restoration methods, we can not find a satisfying solution: either the approaches have a limited applicability, or they are insufficiently validated with respect to human perception. In order to establish a baseline for further research in this area, we test several candidates for human legibility estimators, while proposing an evaluation framework based on a recently published dataset of expert-rated historic manuscript images.
Incorporating User Preferences in Multi-objective Feature Selection in Software Product Lines Using Multi-Criteria Decision Analysis

Software Product Lines Engineering has created various tools that assist with the standardisation in the design and implementation of clusters of equivalent software systems with an explicit representation of variability choices in the form of Feature Models, making the selection of the most ideal software product a Feature Selection problem. With the increase in the number of properties, the problem needs to be defined as a multi-objective optimisation where objectives are considered independently one from another with the goal of finding and providing decision-makers a large and diverse set of non-dominated solutions/products. Following the optimisation, decision-makers define their own (often complex) preferences on how does the ideal software product look like. Then, they select the unique solution that matches their preferences the most and discard the rest of the solutions—sometimes with the help of some Multi-Criteria Decision Analysis technique. In this work, we study the usability and the performance of incorporating preferences of decision-makers by carrying-out Multi-Criteria Decision Analysis directly within the multi-objective optimisation to increase the chances of finding more solutions that match preferences of the decision-makers the most and avoid wasting execution time searching for non-dominated solutions that are poor with respect to decision-makers’ preferences.
Merged Reality: A Second “Second Offline”

This paper examines and expands upon the idea of a second offline from the perspective of market research carried out mainly between 2007 and 2018, primarily but not exclusively on early adopters of information and communication technology. Second offline is here taken to denote a situation where people are never completely offline anymore because the Internet has become so pervasive that it is used intermittently and without reflection, for example, in smartphones, also in situations that seemingly do not necessitate its use, creating an ever-present digital overlay of information on top of the physical world. We then conclude that there are two distinct types of second offline. In the first type, which many modern consumers currently experience, people can separate between physical and digital realities if they try. However in the second type, here called merged reality, people can no longer separate between physical and digital realities even if they try. Consumers do not experience merged reality today, but expect that to happen with AR/VR technology. Examples of consumer expectations from market research are given. Finally, ramifications of merged reality are discussed, including subjectification of reality, motion sickness, the importance of consumer imagination and the expectations on use of time.
A Data Processing Method for Mountain Photovoltaic Power Plants Based on Time and Space Characteristics

The complex environment of mountain photovoltaic (PV) power plant brings great challenges to the operation and maintenance of the power plant. In order to better realize the intelligent operation and maintenance of mountain PV power plant, and we propose a method to analyze and aggregate the output curve of mountain PV power plant according to the temporal and spatial correlation. This data pre-method analyzed the correlation and difference between the series current data of the mountain PV power plant at different dates and locations. The data was reconstructed by multiple fitting and K-means clustering, and the typical day was filtered and the azimuth and inclination were divided. It can effectively solve the problem of inaccurate fault diagnosis caused by dirty actual output data of mountain PV power plants.
Learning Semantic Graph with Bayesian Networks for Action Recognition

Action recognition is widely concerned in the field of video analysis. There is evidence from Cognitive Science and Neuroscience that people actively encode activities into consistent hierarchical part structures. Inspired by this, this paper proposes a graph-based reasoning framework. Decoupling action between people and different objects through graphs, and optimize the graph structure through Bayesian networks. In this paper, the nodes in the graph are the spatiotemporal features of the object instead of the features in the suggestion box. This modeling method can contain the semantic information of the object. The edges between nodes in the graph represent the interaction between people and objects. We optimize the graph structure through Bayesian networks, use prior knowledge to provide common sense information. We evaluate our works on two standard video action recognition benchmarks JHMDB and HMDB datasets and achieve the good performances. Compared with the baseline, it has achieved an improvement of 6.7% and 9.0%, and has reached the level of advanced methods (JHMDB 84.3% and HMDB76.6%).
Research on Mechanism and Application of Coriolis Acceleration

Abstract Chongqing Bus falling into the river shocked the whole country. In order to find out whether the Coriolis illusion is one of the real causes of Chongqing Bus falling into the river, the mechanism of Coriolis acceleration is studied, and the corresponding analysis of the phenomenon caused by the Coriolis acceleration is carried out. The driver’s head in Chongqing Bus falling into the river is modelled and calculated. It is concluded that the effect of Coriolis acceleration can be ignored when the object’s motion speed is low, that is, the main cause of Chongqing Bus falling into the river is not Coriolis illusion.
A Bit of Connexivity Around the Field of Ordinary Conditionals

In this brief note we explore a couple of features of the semantics for indicative conditionals provided by Field. Those features strikingly resemble some controversial principles in connexive logic. We will show that although Field’s semantics has the technical means to stand to the mentioned features, more work is needed to make some of its outcomes less unintuitive.
An Automated Visualization Feature-Based Analysis Tool

Data visualization and data analysis tool are parts of the main elements in the study of data science. Data science always involves finding answers to the questions being asked within any type of domain. Data science is a multidisciplinary study that requires many relevant fields such as data visualization and statistical analytic to find insights from huge sets of structured data or unstructured data. This study aims to develop an automated visualization feature-based analysis tool for unstructured data that is collected through an online survey for a research project entitled the impacts of social distancing during the Covid-19 pandemic. This paper presents step by step process of the design and development, and the prototype of the analysis tool.
Efficient and Robust Distributed Matrix Computations via Convolutional Coding

Distributed matrix computations are well-recognized to suffer from the problem of stragglers (slow or failed worker nodes). The majority of prior work in this area has presented straggler mitigation strategies that are (i) either sub-optimal in terms of their straggler resilience, or (ii) suffer from numerical problems, i.e., there is a blow-up of round-off errors in the decoded result owing to the high condition numbers of the corresponding decoding matrices. This work introduces a novel solution framework, based on embedding the computations into the structure of a convolutional code, that removes these limitations. Our approach is provably optimal in terms of its straggler resilience, and has excellent numerical robustness which can be theoretically quantified by deriving a computable upper bound on the worst case condition number over all possible decoding matrices. All above claims are backed up by extensive experiments done on the AWS cloud platform.
A Power Transmission Line Fault Diagnosis Method Based on Multi-dimensional Information Model

Transmission lines are generally deployed in multi-dimensional scenarios including a sky-heaven-land sources, while transmission lines in different scenarios are vulnerable to varying degrees of failure, which causes great harm to the security of the system. Transmission lines are usually deployed in multi-dimensional scenarios including sky, heaven and land sources, while transmission lines in different scenarios are vulnerable to varying degrees of failure, which causes great harm to the security of the system. In order to ensure the high efficiency of top-level data storage and interaction, this paper proposes an “end-edge-cloud” data collaborative architecture, which ensures the reliable interaction between the user side and the power system, and makes use of the complementary and collaborative relationship between edge computing and cloud computing. Ensure the efficiency of user-end business processing.
Security of CORS on LocalStorage

HTML5 is the next generation of web application. Its LocalStorage solves some problems that can use cookies alone, but also brings new potential safety vulnerabilities because of using CORS. This paper discuss basic principle for using CORS in LocalStorage, compare several different cross-domain schemes, focus on existing security risks in CORS. A test case is designed for further analysis how to protect user privacy information, and put forward a practical scheme and some strategy on using CORS. Finally, a solution of using CORS is summed up, and also give some suggestions to security of CORS.