SCINOBO: a novel system classifying scholarly communication in a dynamically constructed hierarchical Field-of-Science taxonomy

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments

Towards Efficient Decentralized Federated Learning

We focus on the problem of efficiently deploying a federated learning training task in a decentralized setting with multiple aggregators. To that end, we introduce a number of improvements and modifications to the recently proposed IPLS protocol. In particular, we relax its assumption for direct communication across participants, using instead indirect communication over a decentralized storage system, effectively turning it into a partially asynchronous protocol. Moreover, we secure it against malicious aggregators (that drop or alter data) by relying on homomorphic cryptographic commitments for efficient verification of aggregation. We implement the modified IPLS protocol and report on its performance and potential bottlenecks. Finally, we identify important next steps for this line of research

Restoring tibiofemoral alignment during ACL reconstruction results in better knee biomechanics

"Published online: 24 October 2017"PURPOSE: Anterior cruciate ligament (ACL) reconstruction (ACLR) aims to restore normal knee joint function, stability and biomechanics and in the long term avoid joint degeneration. The purpose of this study is to present the anatomic single bundle (SB) ACLR that emphasizes intraoperative correction of tibiofemoral subluxation that occurs after ACL injury. It was hypothesized that this technique leads to optimal outcomes and better restoration of pathological tibiofemoral joint movement that results from ACL deficiency (ACLD). METHODS: Thirteen men with unilateral ACLD were prospectively evaluated before and at a mean follow-up of 14.9 (SD = 1.8) months after anatomic SB ACLR with bone patellar tendon bone autograft. The anatomic ACLR replicated the native ACL attachment site anatomy and graft orientation. Emphasis was placed on intraoperative correction of tibiofemoral subluxation by reducing anterior tibial translation (ATT) and internal tibial rotation. Function was measured with IKDC, Lysholm and the Tegner activity scale, ATT was measured with the KT-1000 arthrometer and tibial rotation (TR) kinematics were measured with 3Dmotion analysis during a high-demand pivoting task. RESULTS: The results showed significantly higher TR of the ACL-deficient knee when compared to the intact knee prior to surgery (12.2° ± 3.7° and 10.7° ± 2.6° respectively, P = 0.014). Postoperatively, the ACLR knee showed significantly lower TR as compared to the ACL-deficient knee (9.6°±3.1°, P = 0.001) but no difference as compared to the control knee (n.s.). All functional scores were significantly improved and ATT was restored within normal values (P < 0.001). CONCLUSIONS: Intraoperative correction of tibiofemoral subluxation that results after ACL injury is an important step during anatomic SB ACLR. The intraoperative correction of tibiofemoral subluxation along with the replication of native ACL anatomy results in restoration of rotational kinematics of ACLD patients to normal levels that are comparable to the control knee. These results indicate that the reestablishment of tibiofemoral alignment during ACLR may be an important step that facilitates normal knee kinematics postoperatively. LEVEL OF EVIDENCE: Level II, prospective cohort study.The authors gratefully acknowledge the funding support from the Hellenic Association of Orthopaedic Surgery and Traumatology (HAOST-EEXOT)info:eu-repo/semantics/publishedVersio