Balancing Privacy Protection and Interpretability in Federated Learning

Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information. Nevertheless, recent studies have illustrated that FL still suffers from information leakage as adversaries try to recover the training data by analyzing shared parameters from local clients. To deal with this issue, differential privacy (DP) is adopted to add noise to the gradients of local models before aggregation. It, however, results in the poor performance of gradient-based interpretability methods, since some weights capturing the salient region in feature map will be perturbed. To overcome this problem, we propose a simple yet effective adaptive differential privacy (ADP) mechanism that selectively adds noisy perturbations to the gradients of client models in FL. We also theoretically analyze the impact of gradient perturbation on the model interpretability. Finally, extensive experiments on both IID and Non-IID data demonstrate that the proposed ADP can achieve a good trade-off between privacy and interpretability in FL

Familiarity-based Collaborative Team Recognition in Academic Social Networks

Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose MOTO (faMiliarity-based cOllaborative Team recOgnition algorithm) to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale data set. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution

Heterogeneous graph learning for explainable recommendation over academic networks

With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions. We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths. With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach. © 2021 ACM

On Using Contact Expectation for Routing in Delay Tolerant Networks

Abstract—Conventional routing algorithms rely on the ex-istence of persistent end-to-end paths for the delivery of a message to its destination via a predesigned path. However, in a delay tolerant network (DTN), nodes are intermittently connected, and thus the network topology is dynamic in nature, which makes the routing become one of the most challenging problems. A promising solution is to predict the nodes ’ future contacts based on their contact histories. In this paper, we first propose an expected encounter based routing protocol (EER) which distributes multiple replicas of a message proportionally between two encounters according to their expected encounter values. In case of single replica of a message, EER makes the routing decision by comparing the minimum expected meeting delay to the destination. We further propose a community based routing protocol (CR) which takes advantages of the high contact frequency property of the community. The simulations demonstrate the effectiveness of our proposed routing protocols under different network parameters

A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have explored the invisibility of backdoor triggers to improve attack stealthiness. However, most of them only consider the invisibility in the spatial domain without explicitly accounting for the generation of invisible triggers in the frequency domain, making the generated poisoned images be easily detected by recent defense methods. To address this issue, in this paper, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Discrete Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, the proposed DUBA adopts a novel attack strategy, in which the model is trained with weak triggers and attacked with strong triggers to further enhance the attack performance and stealthiness. We extensively evaluate DUBA against popular image classifiers on four datasets. The results demonstrate that it significantly outperforms the state-of-the-art backdoor attacks in terms of the attack success rate and stealthinessComment: 10 pages, 7 figures. Submit to ACM MM 202

Ketamine Inhibits Lung Fluid Clearance through Reducing Alveolar Sodium Transport

Ketamine is a broadly used anaesthetic for analgosedation. Accumulating clinical evidence shows that ketamine causes pulmonary edema with unknown mechanisms. We measured the effects of ketamine on alveolar fluid clearance in human lung lobes ex vivo. Our results showed that intratracheal instillation of ketamine markedly decreased the reabsorption of 5% bovine serum albumin instillate. In the presence of amiloride (a specific ENaC blocker), fluid resolution was not further decreased, suggesting that ketamine could decrease amiloride-sensitive fraction of AFC associated with ENaC. Moreover, we measured the regulation of amiloride-sensitive currents by ketamine in A549 cells using whole-cell patch clamp mode. Our results suggested that ketamine decreased amiloride-sensitive Na+ currents (ENaC activity) in a dose-dependent fashion. These data demonstrate that reduction in lung ENaC activity and lung fluid clearance following administration of ketamine may be the crucial step of the pathogenesis of resultant pulmonary edema

MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules. © 2004-2012 IEEE

Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

Co-methylated Genes in Different Adipose Depots of Pig are Associated with Metabolic, Inflammatory and Immune Processes

It is well established that the metabolic risk factors of obesity and its comorbidities are more attributed to adipose tissue distribution rather than total adipose mass. Since emerging evidence suggests that epigenetic regulation plays an important role in the aetiology of obesity, we conducted a genome-wide methylation analysis on eight different adipose depots of three pig breeds living within comparable environments but displaying distinct fat level using methylated DNA immunoprecipitation sequencing. We aimed to investigate the systematic association between anatomical location-specific DNA methylation status of different adipose depots and obesity-related phenotypes. We show here that compared to subcutaneous adipose tissues which primarily modulate metabolic indicators, visceral adipose tissues and intermuscular adipose tissue, which are the metabolic risk factors of obesity, are primarily associated with impaired inflammatory and immune responses. This study presents epigenetic evidence for functionally relevant methylation differences between different adipose depots

Liquid gating elastomeric porous system with dynamically controllable gas/liquid transport

【Abstract】The development of membrane technology is central to fields ranging from resource harvesting to medicine, but the existing designs are unable to handle the complex sorting of multiphase substances required for many systems. Especially, the dynamic multiphase transport and separation under a steady-state applied pressure have great benefits for membrane science, but have not been realized at present. Moreover, the incorporation of precisely dynamic control with avoidance of contamination of membranes remains elusive. We show a versatile strategy for creating elastomeric microporous membrane-based systems that can finely control and dynamically modulate the sorting of a wide range of gasesandliquids underasteady-stateapplied pressure,nearlyeliminate fouling,and can be easily applied over many size scales, pressures, and environments. Experiments and theoretical calculation demonstrate the stability of our system and the tunability of the critical pressure. Dynamic transport of gas and liquid can be achieved through our gating interfacial design and the controllable pores’ deformation without changing the applied pressure. Therefore, we believe that this system will bring new opportunities for many applications, such as gas-involved chemical reactions, fuel cells, multiphase separation, multiphase flow, multiphase microreactors, colloidal particle synthesis, and sizing nano/microparticles.This work was supported by the National Natural Science Foundation of China (grant no. 21673197), the Young Overseas High-level Talents Introduction Plan, the 111 Project (grant no. B16029). 研究工作得到国家自然科学基金委（项目批准号：21673197）和厦门大学校长基金（项目批准号：20720170050）等资助与支持