Early-stage reciprocity in sustainable scientific collaboration

Scientific collaboration is of significant importance in tackling grand challenges and breeding innovations. Despite the increasing interest in investigating and promoting scientific collaborations, we know little about the collaboration sustainability as well as mechanisms behind it. In this paper, we set out to study the relationships between early-stage reciprocity and collaboration sustainability. By proposing and defining h-index reciprocity, we give a comprehensive statistical analysis on how reciprocity influences scientific collaboration sustainability, and find that scholars are not altruism and the key to sustainable collaboration is fairness. The unfair h-index reciprocity has an obvious negative impact on collaboration sustainability. The bigger the reciprocity difference, the less sustainable in collaboration. This work facilitates understanding sustainable collaborations and thus will benefit both individual scholar in optimizing collaboration strategies and the whole academic society in improving teamwork efficiency. © 2020 Elsevier Ltd.The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP-78. This work is partially supported by China Postdoctoral Science Foundation ( 2019M651115 )

Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM

Web strategies for the curation and discovery of openeducational resources

For those receiving funding from the UK HEFCE-funded Open Educational Resource Programme (2009 – 2012), the sustainability of project outputs was one of a number of essential goals. Our approach for the hosting and distribution of health and life science open educational resources (OER) was based on the utilisation of the WordPress.org blogging platform and search engine optimisation (SEO) techniques to curate content and widen discovery. This paper outlines the approaches taken and tools used at the time, and reflects upon the effectiveness of web strategies several years post-funding. The paper concludes that using WordPress.org as a platform for sharing and curating OER, and the adoption of a pragmatic approach to SEO, offers cheap and simple ways for small scale open education projects to be effective and sustainable

DINE : a framework for deep incomplete network embedding

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines. © 2019, Springer Nature Switzerland AG.E

Prediksi Bidang Penelitian dan Rekomendasi Dosen Pembimbing Skripsi Berdasarkan Konten Latar Belakang pada Naskah Proposal Menggunakan Metode Multi-Class Support Vector Machine dan Weighted Product

Pada Fakultas Ilmu Komputer Universitas Brawijaya (FILKOM UB), pengerjaan skripsi dimulai dengan melakukan pembuatan praproposal yang berisi latar belakang dan bidang skripsi. Dalam pengerjaan skripsi, mahasiswa butuh pendampingan oleh dosen pembimbing. Dosen pembimbing berfungsi sebagai motivator, pendamping serta pemberi arahan bagi mahasiswa yang sedang mengerjakan skripsi. Dosen pembimbing menjadi krusial dalam pengerjaan skripsi seorang mahasiswa. Oleh karena hal tersebut, pemilihan dosen pembimbing yang memiliki bidang keahlian yang sesuai dengan topik skripsi sangat penting. Pada FILKOM UB, dosen dengan bidang keahlian yang serupa dikumpulkan dalam sebuah kelompok jabatan fungsional dosen (KJFD). Mahasiswa FILKOM UB dapat berdiskusi dengan ketua program studi atau koordinator KJFD untuk mendapatkan rekomendasi dosen yang memiliki bidang keahlian sesuai topik skripsi. Topik skripsi dapat ditentukan dari latar belakang sebuah proposal skripsi. Penelitian ini bertujuan untuk mengetahui tingkat akurasi prediksi KJFD menggunakan algoritme Multi-class Support Vector Machine dan tingkat akurasi rekomendasi dosen pembimbing menggunakan algoritme Weighted Product. Prediksi KJFD dilakukan berdasarkan latar belakang pada naskah skripsi. Rekomendasi dosen diberikan berdasarkan kesesuaian bidang KJFD dosen dengan topik dan beberapa data dosen yang didapatkan dari unit Pengelola Sistem Informasi, Infrastruktur TI dan Kehumasan Fakultas Ilmu Komputer (PSIK FILKOM) seperti  jurusan dosen, sisa kuota bimbingan, tingkatan gelar, dan beban kerja. Hasil pengujian menghasilkan akurasi prediksi bidang skripsi memiliki nilai precision tertinggi sebesar 0,93 dan akurasi rekomendasi dosen pembimbing memiliki nilai precision@k tertinggi sebesar 0,1678 saat nilai k berjumlah 4. Hasil pengujian akurasi tersebut menampilkan bahwa prediksi bidang skripsi dapat dilakukan dengan sangat baik menggunakan Multi-class Support Vector Machine. Sementara rekomendasi dosen pembimbing dapat dilakukan secara optimal dengan jumlah dosen yang direkomendasikan sebanyak 4 dosen.AbstractIn Fakultas Ilmu Komputer Universitas Brawijaya (FILKOM UB), thesis work started by making preproposal which contains the background and thesis  field/topic. In the working of thesis, undergraduate student needs to be accompanied by a supervisor. Supervisor serve as motivator, companion, and guider for undergraduate students who are doing their thesis research. Supervisor roles become crucial in the working of thesis. Therefore, the selection of supervisor who have areas of expertise that matching with thesis topic is very important. In FILKOM UB, supervisor with similar expertise gathered in a lecturer functional group (KJFD). Students of FILKOM UB may discuss with the head of programme or KJFD coordinator to get a supervisor reccomendation who suitable with the topic of thesis. The topic of thesis can be determined by the introduction contents in  proposal manuscript. This research aims to discover the accuracy of KJFD prediction using Multi-class Support Vector Machine and the accuracy of supervisor reccomendation using Weighted Product. KJFD prediction formulated based on introduction contents in proposal manuscript. Supervisor recommendation done based on the coherency of supervisor’s expertise with the thesis’s topic and also based on some supervisor data that was obtained from unit Pengelola Sistem Informasi, Infrastruktur TI dan Kehumasan Fakultas Ilmu Komputer (PSIK FILKOM). The data that was obtained from unit PSIK FILKOM are supervisor’s majors, remaining quota for supervising, degree level, and work load. Testing result shows the accuracy of thesis’s topic having precision value of 0,93 and accuracy of the supervisor predicition having precision@k value of 0,1678 with k value of 4. The accuracy test result shows that thesis topics prediction can be done very well using Multi-class Support Vector Machine. While supervisor reccomendation can be done optimally when the number of recommended supervisor is 4

DINE: A Framework for Deep Incomplete Network Embedding

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines.Comment: 12 pages, 3 figure

IT infrastructure & microservices authentication

Mestrado IPB-ESTGBIOma - Integrated solutions in BIOeconomy for the Mobilization of the Agrifood chain project is structured in 6 PPS (Products, Processes, and Services) out of which, a part of PPS2 is covered in this work. This work resulted in the second deliverable of PPS2 which is defined as PPS2.A1.E2 - IT infrastructure design and graphical interface conceptual design. BIOma project is in the early stage and this deliverable is a design task of the project. For defining the system architecture, requirements, UML diagrams, physical architecture, and logical architecture have been proposed. The system architecture is based on microservices due to its advantages like scalability and maintainability for bigger projects like BIOma where several sensors are used for big data analysis. Special attention has been devoted to the research and study for the authentication and authorization of users and devices in a microservices architecture. The proposed authentication solution is a result of research made for microservices authentication where it was concluded that using a separate microservice for user authentication is the best solution. FIWARE is an open-source initiative defining a universal set of standards for context data management that facilitates the development of Smart solutions for different domains like Smart Cities, Smart Industry, Smart Agrifood, and Smart Energy. FIWARE’s PEP (Policy Enforcement Point) proxy solution has been proposed in this work for the better management of user’s identities, and client-side certificates have been proposed for authentication of IoT (Internet of Things) devices. The communication between microservices is done through AMQP (Advanced Message Queuing Protocol), and between IoT devices and microservices is done through MQTT (Message Queuing Telemetry Transport) protocol

Sistem Rekomendasi Dosen Pembimbing Berdasarkan Latar Belakang Menggunakan Metode Multi-Class Support Vector Machine Dan Weighted Product

Pada Fakultas Ilmu Komputer Universitas Brawijaya, pengerjaan skripsi dimulai dengan melakukan pembuatan praproposal yang berisi latar belakang dan bidang skripsi. Dalam pengerjaan skripsi, mahasiswa butuh pendampingan oleh dosen pembimbing. Dosen pembimbing berfungsi sebagai motivator, pendamping serta pemberi arahan bagi mahasiswa yang sedang mengerjakan skripsi. Dosen pembimbing menjadi krusial dalam pengerjaan skripsi seorang mahasiswa. Oleh karena hal tersebut, pemilihan dosen pembimbing yang memiliki bidang keahlian yang sesuai dengan topik skripsi sangat penting. Topik skripsi ditentukan dari latar belakang sebuah proposal skripsi. Untuk menjawab permasalahan dibutuhkan sebuah sistem yang mampu menentukan topik skripsi dan memprediksi dosen pembimbing yang sesuai berdasarkan latar belakang proposal skripsi. Rekomendasi dosen diberikan berdasarkan kesesuaian bidang dosen dengan topik dan beberapa data dosen yang didapatkan dari unit Pengelola Sistem Informasi, Infrastruktur TI dan Kehumasan Fakultas Ilmu Komputer (PSIK FILKOM) seperti jurusan dosen, sisa kuota bimbingan, tingkatan gelar, dan beban kerja. Hasil pengujian akurasi menghasilkan akurasi klasifikasi bidang skripsi sebesar 93,75% dan akurasi prediksi dosen pembimbing sebesar 57,14%. Hasil pengujian unit menunjukkan bahwa sistem 100% valid dan sesuai dengan kebutuhan. Hasil pengujian kompatibilitas menunjukkan bahwa sistem dapat digunakan di berbagai web browser, sistem operasi, dan platform. Hasil pengujian performa menunjukkan bahwa 85,71% bagian dari sistem telah responsive dan dapat digunakan dengan baik

Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes

Knowing driving factors and understanding researcher behaviors from the dynamics of collaborations over time offer some insights, i.e. help funding agencies in designing research grant policies. We present longitudinal network analysis on the observed collaborations through co-authorship over 15 years. Since co-authors possibly influence researchers to have interest changes, by focusing on researchers who could become the influencer, we propose a stochastic actor-oriented model of bipartite (two-mode) author-topic networks from article metadata. Information of scientific fields or topics of article contents, which could represent the interests of researchers, are often unavailable in the metadata. Topic absence issue differentiates this work with other studies on collaboration dynamics from article metadata of title-abstract and author properties. Therefore, our works also include procedures to extract and map clustered keywords as topic substitution of research interests. Then, the next step is to generate panel-waves of co-author networks and bipartite author-topic networks for the longitudinal analysis. The proposed model is used to find the driving factors of co-authoring collaboration with the focus on researcher behaviors in interest changes. This paper investigates the dynamics in an academic social network setting using selected metadata of publicly-available crawled articles in interrelated domains of "natural language processing" and "information extraction". Based on the evidence of network evolution, researchers have a conformed tendency to co-author behaviors in publishing articles and exploring topics. Our results indicate the processes of selection and influence in forming co-author ties contribute some levels of social pressure to researchers. Our findings also discussed on how the co-author pressure accelerates the changes of interests and behaviors of the researchers