Knowledge-Driven Data Ecosystems Toward Data Transparency

A data ecosystem (DE) offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that DEs face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven DE architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Last, we discuss and rate the potential of the proposed architecture in the fulfillmentof these requirements.

Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.

Gifted School Activities With DropTalk, Parent-Teacher Notebook, and SmileNote for Students With Disabilities

Three programs, DropTalk, Parent-Teacher Notebook, and SmileNote, were developed by teachers at schools for special needs education to help students with various disabilities, in collaboration with businesses supportive of students with disabilities. DropTalk was developed to help students with nonverbal communication by using Pictogram and text overlaid with voice/sound. A digital-based Parent-Teacher Notebook was developed to share the valuable data on each student between their home and school. The shared data are effectively used to build up individual support plans. SmileNote was developed to help students with nonverbal communication disabilities present their wills, hopes, and desires to the classmates and others. In this chapter, the aims and valuable functions in three software applications are described in detail, and self-made contents created with the software and gifted school activities conducted at several schools for special needs education are depicted.

Review and outlook of accelerator-related codes and their interplay with the experiments software

Powerful flexible computer codes are essential for the design and optimisation of accelerator and experiments. We briefly review what already exists and what is needed in terms of accelerator codes. For the FCC-ee, it will be important to include the effects of beamstrahlung and beam–beam interaction as well as machine imperfections and sources of beam-induced backgrounds relevant for the experiments and consider the possibility of beam polarisation. The experiment software Key4hep, which aims to provide a common software stack for future experiments, is described, and the possibility of extending this concept to machine codes is discussed. We analyse how to interface and connect the accelerator and experiment codes in an efficient and flexible way for optimisation of the FCC-ee interaction region design and discuss the possibility of using shared data formats as an interface.

Comparative Perspectives on Geriatrics-Surgery Co-Management Program by Specialty and Staff Role

Co-management programs between geriatrics and surgical specialties have gained popularity in the last few years. Little is known about how these programs are perceived across surgical specialties and staff roles. We conducted a mixed methods study to assess perspectives on a geriatrics-surgery co-management program (GSCP) at a hospital where geriatricians co-manage patients 65 or older admitted to Orthopedic Trauma, General Trauma, and Neurosurgery. We used semi-structured interviews (n=13) and online surveys (n=45) to explore program value, facilitators, use, understanding, and impact by specialty and staff roles (physicians, advanced practice providers, nurses, case managers, social workers). Interview transcripts were analyzed using qualitative thematic analysis, and survey data were analyzed using Kruskal-Wallis, ANOVA, and Fisher’s exact tests. Interviews revealed three themes: 1) GSCP is valued because of geriatricians’ expertise in older adults, relationship with patients and families, and skill in addressing social determinants of health; 2) GSCP facilitators include consistent availability of geriatricians, clear communication, and collaboration via shared data-driven goals; and 3) GSCP use varies by surgical specialty and role depending on expertise and patient complexity. Survey data analysis affirmed interview themes and showed significant differences (p-values<0.05) between perspectives of surgical specialties and roles on GSCP use, understanding, impact, and which specialty should manage specific clinical issues. Findings suggest that while there are similarities across surgical specialties and roles regarding the value of, and facilitators for, a GSCP, specialties and roles differ in use, understanding, and perceived program impact on care. These findings suggest strategies for optimizing this intervention across groups.

Persistent Cohesion with Advanced Ring Signatures for Shared Data in Cloud

Motivated by the exponential growth and the huge success of cloud data services bring the cloud common place for data to be not only stored in the cloud, but also shared across multiple users. Our scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. Unfortunately, the integrity of cloud data is subject to skepticism due to the existence of hardware/software failures and human errors. Several mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. However, public auditing on the integrity of shared data with these existing mechanisms will inevitably reveal confidential information—identity privacy—to public verifiers. In this paper, we propose a novel privacy-preserving mechanism that supports public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute verification metadata needed to audit the correctness of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from public verifiers, who are able to efficiently verify shared data integrity without retrieving the entire file. In addition, our mechanism is able to perform multiple auditing tasks simultaneously instead of verifying them one by one. Our experimental results demonstrate the effectiveness and efficiency of our mechanism when auditing shared data integrity. Keywords: Public auditing, privacy-preserving, shared data, cloud computing

The UNESCO FRIEND-Water program: accelerates, shares and transfers knowledge and innovation in hydrology across the world in the frame of the Intergovernmental Hydrological Program (IHP)

The FRIEND-Water program (FWP) is the oldest and the most transverse program (i.e. Flagship Initiative) within the Hydrological Intergovernmental Program (IHP) from UNESCO. FRIEND means “Flow Regimes from International and Experimental Network Data”. The FWP is dedicated to allow large communities of hydrologists and associated disciplines to collaborate across borders on common shared data and scientific topics which are addressed through large world regions. The program has evolved in its 35 years of existence. There are 8 large regions in the world which gather tenths of hundreds of researchers, and organize events following several research topics defined according to each region priorities. The FWP is chaired by a scientific committee gathering regional coordinators and thematic experts. Each region gives its research priorities which evolve according to the projections given by the member States during the IHP council every year. The future activities of the FWP are defined by the upcoming IHP IX program and with the support of the newly created Montpellier UNESCO Category II Center ICIREWARD, acting as FWP Secretariat.

Privacy preserving defect prediction using generalization and entropy-based data reduction

The software engineering community produces data that can be analyzed to enhance the quality of future software products, and data regarding software defects can be used by data scientists to create defect predictors. However, sharing such data raises privacy concerns, since sensitive software features are usually considered as business assets that should be protected in accordance with the law. Early research efforts on protecting the privacy of software data found that applying conventional data anonymization to mask sensitive attributes of software features degrades the quality of the shared data. In addition, data produced by such approaches is not immune to attacks such as inference and background knowledge attacks. This research proposes a new approach to share protected release of software defects data that can still be used in data science algorithms. We created a generalization (clustering)-based approach to anonymize sensitive software attributes. Tomek link and AllNN data reduction approaches were used to discard noisy records that may affect the usefulness of the shared data. The proposed approach considers diversity of sensitive attributes as an important factor to avoid inference and background knowledge attacks on the anonymized data, therefore data discarded is removed from both defective and non-defective records. We conducted experiments conducted on several benchmark software defect datasets, using both data quality and privacy measures to evaluate the proposed approach. Our findings showed that the proposed approach outperforms existing well-known techniques using accuracy and privacy measures.