Collaborative Distance Research Articles

Local Preserving Graphs Using Intra-Class Competitive Representation for Dimensionality Reduction of Hyperspectral Image

As a key technique in hyperspectral image pre-processing, dimensionality reduction has received a lot of attention. However, most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures. In this paper, we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information. These two methods explore local information into the collaborative graph through competing constraints, thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm. By classifying four benchmark hyperspectral data, the proposed methods are proved to be superior to several advanced algorithms, even under small-sample-size conditions.

CLARINET: efficient learning of dynamic network models from literature.

Creating or extending computational models of complex systems, such as intra- and intercellular biological networks, is a time and labor-intensive task, often limited by the knowledge and experience of modelers. Automating this process would enable rapid, consistent, comprehensive and robust analysis and understanding of complex systems. In this work, we present CLARINET (CLARIfying NETworks), a novel methodology and a tool for automatically expanding models using the information extracted from the literature by machine reading. CLARINET creates collaboration graphs from the extracted events and uses several novel metrics for evaluating these events individually, in pairs, and in groups. These metrics are based on the frequency of occurrence and co-occurrence of events in literature, and their connectivity to the baseline model. We tested how well CLARINET can reproduce manually built and curated models, when provided with varying amount of information in the baseline model and in the machine reading output. Our results show that CLARINET can recover all relevant interactions that are present in the reading output and it automatically reconstructs manually built models with average recall of 80% and average precision of 70%. CLARINET is highly scalable, its average runtime is at the order of ten seconds when processing several thousand interactions, outperforming other similar methods. The data underlying this article are available in Bitbucket at https://bitbucket.org/biodesignlab/clarinet/src/master/. Supplementary data are available at Bioinformatics Advances online.

Spatial‐spectral feature extraction of hyperspectral images using tensor‐based collaborative graph analysis

Although the collaborative graph-based discriminant analysis (CGDA) method has shown promising performance for the feature extraction of the hyperspectral image (HSI), both the intrinsic local subspace structures and spatial structural information are ignored in CGDA. To address these problems, a novel spatial-spectral feature extraction method, i.e. tensor-based collaborative graph analysis, is proposed in this letter. Specifically, the spectral similarity is utilized to calculate the Tikhonov matrix, which can constrain the testing samples to be represented by similar training samples in the collaborative representation model. To fully exploit the 3D spatial-spectral structural information, the collaborative representation model is extended to tensor space by using the third-order tensor representation of HSI, in which samples are constructed by small local patches around the central pixels. Depending on the above techniques, the quality of the collaborative graph constructed by the coefficient matrix can be significantly improved to obtain the discriminative low-dimensional features. Experiments on three HSI data sets demonstrate the superiority of the proposed method.

E-Learning Based on Cloud Computing

Modern technological paradigms of learning give educators an ability to support the development of highly professional human resources. For this reason, teachers of higher educational institutions find advanced technologies more and more useful. Rapid introduction of cloud computing into pedagogical practice creates a professional need to study the technological capabilities of cloud-based learning services to select the best educational solutions among available variants. In view of this, the current article provides a theoretical overview of e-learning cloud architecture layers and models of its deployment in the education system. In order to test the adaptability of learning management systems (LMSs) to a collaborative distance learning model, research among educators of I.M. Sechenov First Moscow State Medical University (Russia), Prydniprovska State Academy of Civil Engineering and Architecture (Ukraine) and Wuxi Institute of Technology (China) was carried out using the Blackboard Learn LMS as a case study. This study involved 100 individuals who tested the Blackboard Learn teaching capabilities. The conducted investigation allowed outlining the main advantages of the use of computer-based e-learning applications in the process of organizing and supporting the educational process. Among them are convenient means of processing educational content, effective educational process organization, effective tools for knowledge monitoring, and effective security and confidentiality system. The present work also shed light on the benefits of introducing cloud-based e-learning in higher educational institutions, as producers of an integrated educational product. The study confirmed that the introduction of cloud computing in education results in the advancement of the training content and can considerably improve students’ academic results due to updated learning technologies, concepts, and tools.

How does distant collaboration influence R&D quality?

ABSTRACT Geographical distance plays a key role in R&D collaboration for technological development. The relationship between geographical collaboration distance and technological development has been a key topic in innovation studies. However, how geographical distance in relation to R&D collaboration influences technological development quality has not been explored holistically or systematically. Thus, this study used patent-based measures to explore how spatial distance between inventors influences R&D output quality by employing three technology-related proxies, i.e. originality, generality, and innovation. It is found that geographic distance between collaborators has a significant and positive effect on originality, generality, and innovation. This paper contributes to understanding the underlying impact on technological development and provides researchers with a more thorough understanding of the spatial distance trajectory of technological innovation.

Does Tangible design and print hold value in the contemporary digital age?

In the following study I have used a mix of trivial, journalistic and academic resources from relevant sources to draw a conclusion on the question posed; does tangible design and print hold value in the contemporary digital age? Explorations into the benefits of tangible design, how digital and material media is consumed, and the implications for design practice drove me to consider a range of elements to this query. These included reminiscence and nostalgia, the benefits of Lo-fi as an aesthetic choice, the freedoms that independent print media such as Zines can provide the designer and the unique sense of community that this can foster, how tangible design can be interactive in order to appeal to multiple senses and therefore create a deeper and more memorable connection for the consumer, and the unique set of benefits that come with stickers being such a tangible design media such as the appeal of free advertising and consumer freedoms of context choice. I also consider in this exploration the benefits of digital vs tangible when collaborating on a design piece. I drew the conclusion that tangible collaboration holds higher value due to more personal and time effective physical collaboration between multiple designers, however it must be said that digital collaboration brings forth its own set of benefits, such as allowing for long distance collaboration which may not otherwise be possible. When exploring how digital and tangible can be used not only for design but as tools to advertise design work and their comparable effectivity, I considered tangible design benefits such as stickers doubling as free advertising, and digital benefits such as being able to pay to reach larger audiences from broader scopes. The benefits considered by the stated explorations lead me to conclude that tangible print does still hold value in the contemporary digital age, which is proven by its still rife popularity.  

Use of Benford's law on academic publishing networks

Benford's law, also known as the first-digit law, has been widely used to test for anomalies in various data ranging from accounting fraud detection, stock prices, and house prices to electricity bills, population numbers, and death rates. Scientific collaboration graphs have been studied extensively as data availability increased. Most research was oriented towards analysing patterns and typologies of citation graphs and co-authorship graphs. Most countries group publications into categories in an attempt to objectively measure research output. However, the scientific community is complex and heterogeneous. Additionally, scientific fields may have different publishing cultures, which make creating a unified metric for evaluating research output problematic. In complex systems like these, it is important to regularly observe potential anomalies and examine them more carefully in an attempt to either improve the evaluation model or find potential loopholes and misuses. In this paper, we examine the potential application of Benford's law on the official research database of Slovenia. We provide evidence that metrics such as number of papers per researcher conform to Benford's distribution, while the number of authors per paper does not. Additionally, we observe some anomalies and provide potential reasoning behind them.

Implementation of Project-Based Model in Collaborative Distance Learning System for Second Grade Elementary School

The COVID-19 pandemic has had a major impact on the education sector, especially for schools, in the form of a change in the learning process, which currently must be done in a different space and distance. In areas with a harmless zone and inadequate for online learning, the schools have practiced two ways of learning: face-to-face and non-face-to-face. The scientific model should still be implemented eventhough in distance learning situation. This study aimed to examine the use of a project-based learning model for elementary school students to develop a collaborative distance learning system during the COVID-19 pandemic. The research method employed classroom action research from the John Elliot model. The first cycle of learning was carried out online, and the second cycle of learning was conducted face-to-face at school. The research was done in two cycles because students had limited facilities to support online learning. The data collection technique was through observation. The research subjects involved were second-grade elementary school students of SD Negeri 2 Kurung Kambing in Pandeglang Regency. This study’s results proved that the project-based model influenced increasing psychomotoric competency achievement in the SBdP (Cultural Arts and Crafts) subject with teaching materials mimicking daily and natural movements in the "Planting Corn" dance. This learning also involved students more actively and trained them to think critically and behave creatively.

Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.

On Some Properties of Collaboration Graphs of Scientists in Math-Net.Ru

A study of two graphs of scientific cooperation based on co-authorship and citation according to the all-Russian mathematical portal was conducted Math-Net.Ru. A citation-based scientific collaboration graph is a directed graph without loops and multiple edges, whose vertices are the authors of publications, and arcs connect them when there is at least one publication of the first author that cites the publication of the second author. A co-authorship graph is an undirected graph in which the vertices are the authors, and the edges record the co-authorship of two authors in at least one article. The customary study of the main characteristics of both graphs is carried out: diameter and average distance, connectivity components and clustering. In both graphs, we observe a similar connectivity structure – the presence of a giant component and a large number of small components. The similarity and difference of scientific cooperation through co-authorship and citation is noted.

Measurement and Evaluation of Collaborative Development Level of Higher Education

With the aid of grey correlation theory and technique for order of preference by similarity to ideal solution (TOPSIS), this paper sets up a multi-dimensional evaluation index system (EIS) for the comprehensive development level (CDL) of China’s higher education, and quantifies the state of collaborative development in 2005-2017 with an improved distance collaboration model. The results show that: Being the premises and bases of higher education, the supply subsystem develops much slower than the other subsystems (i.e. participation, output, and environment), and clearly pulls the development of the other subsystems. From 2012 onwards, the subsystems conformed to basically the same trend for the degree of collaboration: the degree of collaboration tended to be stable. There was even a slight dent in overall degree of collaboration between 2012 and 2016. Hence, the overall degree of collaborative development was far slower than the overall degree of development. After more than a decade of development, the degree of development, degree of collaboration, and degree of collaborative development reached basically the same level in 2017. The empirical results shed new lights on the focal points of higher education development in China.

Children’s Literature Archives and Special Collections Websites: A Webometric Content Analysis

ABSTRACT This study examined the websites representing fourteen English-language children’s literature archives and special collections in the United States to find key similarities and major differences among the websites’ provision of information to distance users. The study took place during the COVID-19 pandemic and aimed to assess websites’ usefulness for researchers whose access had been severely restricted by travel bans and safety issues. Although the circumstances of the study are unusual, the push toward remote learning, distance collaboration, and world-wide research indicates that this study will be of value and interest beyond the bounds of pandemic-related travel restrictions.

Research on collaborative distance English teaching system based on mobile cloud computing

Aiming at the problems of low success rate and poor robustness of traditional distance English teaching system, this paper proposes a new collaborative distance English teaching system based on mobile cloud computing. The hardware of the system adopts USB-to-JTAG device to download files and adopts reverse principle to upload English teaching resources. The four-wire signal is purified and denoised by phase-locked loop. When the system fails, reset circuit is used to deal with it in time. The system software adopts the structure of the Yangtze River basin, establishes the virtual teaching place and video voice teaching method, and realises the remote synchronous teaching. The experimental results show that the success rate and robustness of distance cooperative English teaching system based on mobile cloud computing are 98.2% and 94.5%, which are significantly higher than traditional methods. This method has good reliability and is more suitable for English teaching system.

Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection

Multi-view feature selection aims at obtaining a subset of informative features from heterogeneous feature domains. Recent graph based approaches mostly learn view-specific feature selection matrices by virtue of prepared single-view graphs, and weight the view-wise objectives to discriminate them. However, the majority of them encounter that (i) the dimensions of vectors for evaluating features in different views are inconsistent and (ii) the similarities attained by using features in the original high-dimensional space are inferior. As a result, the joint evaluation of heterogeneous features using view-specific selection matrices are inaccurate and immensely depend on ill-defined similarity relations. To overcome them, we propose the Multilevel projections with Adaptive neighbor graph model for unsupervised Multi-view Feature Selection (MAMFS). Our formulation learns the collaborative graph adapting to the adaptive k-nearest neighbors in subspaces, wherein the multilevel projections are involved including the weighted view-specific projections that discriminate different views and the joint projection that collaborates different views. Benefited from this, both of view-within information and the view-across information are jointly explored and made use of. Extensive simulations are conducted to validate the superiority of the proposed method compared to the state-of-the-art competitors.

Practical experience in organizing distance learning in secondary schools

The COVID-19 pandemic has re-emphasized distance learning. It became a powerful impetus for the transition from theoretical considerations to the practical implementation of such training in the school system (Strategy New Growth Model - New Social Policy, 2020). This article describes the experience of organizing such training for children in the 4th quarter of the 2019/2020 academic year. It considers all stages of implementation of distance learning, such as solution of legal issues regulating the activities of an educational institution; effective construction of the educational process, considering the important relationship "student-teacher"; the right choice of information technology that meets the capabilities and limited skills of students, and has a user-friendly interface. The experience of distance collaboration between a teacher, a student and his parents is also presented, which made it possible to build effective teaching and achieve a high-quality education.

Practical experience in organizing distance learning in secondary schools

The COVID-19 pandemic has re-emphasized distance learning. It became a powerful impetus for the transition from theoretical considerations to the practical implementation of such training in the school system (Strategy New Growth Model - New Social Policy, 2020). This article describes the experience of organizing such training for children in the 4th quarter of the 2019/2020 academic year. It considers all stages of implementation of distance learning, such as solution of legal issues regulating the activities of an educational institution; effective construction of the educational process, considering the important relationship "student-teacher"; the right choice of information technology that meets the capabilities and limited skills of students, and has a user-friendly interface. The experience of distance collaboration between a teacher, a student and his parents is also presented, which made it possible to build effective teaching and achieve a high-quality education.

Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction

Due to the widespread applications in real-world scenarios, metro ridership prediction is a crucial but challenging task in intelligent transportation systems. However, conventional methods either ignore the topological information of metro systems or directly learn on physical topology, and cannot fully explore the patterns of ridership evolution. To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system, while a similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation. These complementary graphs are incorporated into a Graph Convolution Gated Recurrent Unit (GC-GRU) for spatial-temporal representation learning. Further, a Fully-Connected Gated Recurrent Unit (FC-GRU) is also applied to capture the global evolution tendency. Finally, we develop a Seq2Seq model with GC-GRU and FC-GRU to forecast the future metro ridership sequentially. Extensive experiments on two large-scale benchmarks (e.g., Shanghai Metro and Hangzhou Metro) well demonstrate the superiority of our PVCGN for station-level metro ridership prediction. Moreover, we apply the proposed PVCGN to address the online origin-destination (OD) ridership prediction and the experiment results show the universality of our method. Our code and benchmarks are available at <uri>https://github.com/HCPLab-SYSU/PVCGN</uri>.

Exploring the changing geographical pattern of international scientific collaborations through the prism of cities.

Science is becoming increasingly international in terms of breaking down walls in its pursuit of high impact. Despite geographical location and distance still being major barriers for scientific collaboration, little is known about whether high-impact collaborations are similarly constrained by geography compared to collaborations of average impact. To address this question, we analyze Web of Science (WoS) data on international collaboration between global leader cities in science production. We report an increasing intensity of international city-city collaboration and find that average distance of collaboration of the strongest connections has slightly increased, but distance decay has remained stable over the last three decades. However, high-impact collaborations span large distances by following similar distance decay. This finding suggests that a larger geographical reach of research collaboration should be aimed for to support high-impact science. The creation of the European Research Area (ERA) represents an effective action that has deepened intracontinental research collaborations and the position of the European Union (EU) in global science. Yet, our results provide new evidence that global scientific leaders are not sufficiently collaborative in carrying out their big science projects.

Developing a perceptual distance monitor for client-contractor collaborations

Purpose This study aims to propose a tool for conceptualizing and operationalizing perceptual distance in client-contractor collaborations: the perceptual distance monitor (PDM). This paper explains how this monitor was developed and used to examine the impact of perceptual distance on project outcomes. Design/methodology/approach This paper developed the PDM by gathering quantitative survey data from client and contractor representatives. Structural equation modeling tested the predictive validity of perceptual distance on project outcomes. Findings The PDM enables a valid and reliable assessment of the perceptual distance between client and contractor in projects. Moreover, the PDM shows that project outcomes suffer if parties have different perceptions of project objectives, project managers’ competences and the level of trust in the collaboration. These findings confirm the predictive validity of the PDM. Research limitations/implications The study builds on survey data representing dyadic perceptions from 38 measurements in collaborative projects. This paper may not have identified all the effects of perceptual distance on project outcomes, as the analyses were conducted at the project level. This research underlines the importance of gathering dyadic data for studies in interorganizational settings. Practical implications Perceptual distance can be expected between clients and contractors, and higher perceptual distance is generally associated with lower project outcomes. Using the PDM, project managers can concretize, discuss and monitor this perceptual distance over time. The PDM provides project managers with a useful tool to prevent the escalation of conflicts and project failure. Originality/value Based on agency theory and social identity theory, this study provides a unique and validated conceptualization and operationalization of perceptual distance between client and contractor in interorganizational collaborations and supply chains.

The implementation of a digital service approach to fostering team autonomy, distant collaboration, and knowledge scaling in large enterprises

BACKGROUND: Autonomous acting of individuals and as teams are key elements of agile, distributed, and partly or entirely distant working environments. The availability of relevant processes, methods, tools, and guidelines is key to leveraging team autonomy. OBJECTIVE: This article presents the design and implementation of a digital self-service kit (SSK) approach featuring high scalability, as well as a quality assurance and continuous improvement mechanism. As consumers, the teams within an organization can use these SSK’s anytime and on-demand without any constraints in location, time, or quota. As producers (of knowledge and experience), they can also assume active roles in the extension and continuous improvement of the SSK’s. METHODS: This has been achieved in open community networks where feedback is actively leveraged and constantly integrated in the SSK’s design. Such open Communities of Practices (CoP) ensure that all interested parties can contribute to the adequateness of both the content and the provision of the SSK’s in both local and distant corporate settings. Both the design and implementation have been done and evaluated in a large-scale international corporate environment where high cultural diversity, as well as distant collaboration are of key importance. RESULTS: The results presented in this article include a generic digital self-service approach to distance learning and coaching of teams in the particular context of the agile transformation of large corporate organizations. Key elements include a strong and systematic expert team involvement in the process of the setup and design of such digital SSK’s, as well as a well-explained and understood kit structure for efficient and effective utilization and re-contextualization of the contained knowledge into team-specific project contexts. This contributes to team autonomy as a major prerequisite for the agile transformation, as well as knowledge scaling across the organisation. CONCLUSIONS: The key insights gained from this experiment confirm the high relevance and effectivity of the approach especially during periods where distant collaborations are essential (e.g. during a pandemic crisis).