Towards Fairness-Aware Federated Learning.

Recent advances in federated learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL and overlook the interests of the FL clients. This may result in unfair treatment of clients, which discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse fairness-aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey that helps readers gain insight into this interdisciplinary field. This article aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by the existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation, and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches and suggest promising future research directions toward FAFL.

Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches.

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

A dataset of human decision-making in teamwork management.

Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members' capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.

Towards AI-powered personalization in MOOC learning.

Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students' learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed.

Mitigating Herding in Hierarchical Crowdsourcing Networks.

Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers' current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

Blunt microdissection and rotational atherectomy: an effective combination for the resistant chronic total occlusion.

Percutaneous coronary intervention (PCI) of chronic total occlusions (CTO) has a lower success rate than nontotal occlusions. Like nontotal coronary occlusions, successful procedural outcomes of CTOs confers improvement in clinical outcomes. Reasons for unsuccessful PCI of CTOs may stem from various steps of the procedure including failure to cross the occlusion with a guidewire or a balloon, or failure to adequately dilate or modify the lesion. The Frontrunner blunt microdissection device is designed to improve the odds of guidewire passage through coronary occlusions, whereas the Rotablator rotational atherectomy device is intended for plaque modification, especially in resistant, elastic or calcific lesions. The Frontrunner and Rotablator can each facilitate the procedure at their respective junctures; the combined use of these technologies can enhance the overall PCI success rates, especially regarding difficult CTOs. This case presentation encounters two technical challenges: the inability of the balloon catheter to cross the CTO, and the inability to recross the occlusion with the less favorable RotaWire. In this case, the combined use of the Frontrunner and Rotablator devices was necessary to achieve overall procedural success. When tackling difficult CTOs, a comprehensive strategy that encompasses multiple devices and technologies can enable ultimate procedural success.

Aortic valvular regurgitation: prevalence and clinical characteristics in a predominantly obese adult population not taking anorexigens.

BACKGROUND: We recently reported the prevalence of aortic regurgitation (AR) by Doppler echocardiography (echo) in obese subjects to be higher than in some previous reports. OBJECTIVE: To describe the prevalence of AR in an obese population not taking anorexigens as a function of demographic characteristics, cardiovascular risk factors, and other potential predictors. METHODS: In 539 adult subjects, cardiovascular status was evaluated by medical history, physical examination, and Doppler echocardiograms performed according to a standardized imaging protocol. Echocardiographic readers were blinded as to each subject's medical and medication histories. Associations of AR with demographic and comorbid factors were examined. RESULTS: Subjects had a mean (+/- SD) body mass index (BMI) of 35 +/- 7 kg/m2, and were predominantly white (87.6%), females (74%), with a mean age of 47 +/- 12 years. AR by Food and Drug Administration criteria (> or =mild) was present in 4.1% of the subjects. Covariates significantly associated with AR were increasing age (P < 0.001), presence of a history of hypertension (P = 0.001), left ventricular (LV) internal dimensions (P < 0.005), and tricuspid and mitral regurgitation grade (P < 0.001). CONCLUSIONS: Clinical and Doppler echo evaluation of a large, predominantly obese, adult population revealed that AR was more prevalent than in some previous reports and was highly correlated with increased age, presence of a history of hypertension, LV internal dimensions, tricuspid and mitral regurgitation.

Treatment of post-stenotic saphenous vein graft aneurysm: special considerations with the polytetrafluoroethylene-covered stent.

The use of the polytetrafluoroethylene-covered stents is expanding and its full potential in percutaneous coronary intervention is still being defined. The synthetic covered stent has been used in the treatment of coronary as well as saphenous vein graft disease. In the treatment of relatively large aneurysms of saphenous vein grafts, however, the technical and anatomic aspects may be unusual and challenging. We present a case of an ostial saphenous vein graft lesion with a saccular post-stenotic aneurysm treated with a polytetrafluoroethylene-covered stent. Special consideration is made of the procedural technique and the unusual anatomic challenge.

Rheolytic thrombectomy with distal filter embolic protection as adjunctive therapies to high-risk saphenous vein graft intervention.

Percutaneous intervention of saphenous vein graft (SVG), especially those with heavy atherothrombotic load, presents high risk for distal embolization and no-reflow. Using the distal filters alone may occasionally be disadvantageous because of the large debris burden and the inability to assess the underlying culprit lesions and vessel size accurately. We present a case of intervention of an occluded SVG using a combination of rheolytic thrombectomy and distal filter embolic protection as a pretreatment before stenting. This strategy has the potential to reduce further the risk of no-reflow and to provide visualization for proper assessment of the underlying anatomy especially in clot-laden vessels.