QuoTe: Quality-oriented Testing for Deep Learning Systems

Recently, there has been significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is DL testing—that is, given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, which are commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model quality after testing. In this work, we address this gap by proposing a novel testing framework called QuoTe (i.e., Quality-oriented Testing). A key part of QuoTe is a quantitative measurement on (1) the value of each test case in enhancing the model property of interest (often via retraining) and (2) the convergence quality of the model property improvement. QuoTe utilizes the proposed metric to automatically select or generate valuable test cases for improving model quality. The proposed metric is also a lightweight yet strong indicator of how well the improvement converged. Extensive experiments on both image and tabular datasets with a variety of model architectures confirm the effectiveness and efficiency of QuoTe in improving DL model quality—that is, robustness and fairness. As a generic quality-oriented testing framework, future adaptations can be made to other domains (e.g., text) as well as other model properties

VeriFi:Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model's performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning

Backdoor Attacks on Crowd Counting

Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to backdoor attacks, a major security threat to deep learning. A backdoor attack implants a backdoor trigger into a target model via data poisoning so as to control the model's predictions at test time. Different from image classification models on which most of existing backdoor attacks have been developed and tested, crowd counting models are regression models that output multi-dimensional density maps, thus requiring different techniques to manipulate. In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or small density estimations. Experimental results demonstrate the effectiveness of our DMBA attacks on five classic crowd counting models and four types of datasets. We also provide an in-depth analysis of the unique challenges of backdooring crowd counting models and reveal two key elements of effective attacks: 1) full and dense triggers and 2) manipulation of the ground truth counts or density maps. Our work could help evaluate the vulnerability of crowd counting models to potential backdoor attacks.Comment: To appear in ACMMM 2022. 10pages, 6 figures and 2 table

On-Chip Light Polarization Management by Mapping the Polarization Information to Phase Shift

This work was partially supported by the National Natural Science Foundation of China (NSFC) under Grants 61120106012, 62001010, and 61775005. Q.D. acknowledges the Research Foundation - Flanders (FWO) for supporting his postdoctoral research under Grant 12ZR720N. J.Q. acknowledges the funding support from the State Key Laboratory of Advanced Optical Communication Systems Networks, Peking University, and Open Fund of IPOC (BUPT)

GUDMAP - An Online GenitoUrinary Resource

The GenitoUrinary Development Molecular Anatomy Project (GUDMAP) is a consortium of laboratories working to provide the scientific and medical community with gene expression data and tools to facilitate research (see "www.gudmap.org":http://www.gudmap.org). The data provided by GUDMAP includes large _in situ_ hybridization screens (wholemount and section) and expression microarray analysis of components of the developing mouse urogenital system (including laser-captured material and FACS-isolated cells from transgenic reporter mice). In addition, a high-resolution anatomy ontology has been developed by members of the GUDMAP consortium to describe the subcompartments of the developing murine genitourinary tract. 

The GUDMAP Database Development Team and Editorial Office - both based in Edinburgh - function to ensure submission, curation, storage and presentation of the data submitted by the GUDMAP consortium. Our collective aim is twofold: 1) to simplify the process of submission so that data is publically available as soon as it is produced; and 2) to organize this information in a database and ensure that the online interface is continuously available and easy to use. Thus far, we have developed a range of tools that help both the submitter and the end user. These include: an online annotation tool that simplifies _in situ_ data submission through an ontology-based graphical user interface; a database interface that allows users to browse and query expression data, and to filter data by organ system; a heat-map display of microarray data and analyses. Furthermore, the Edinburgh team has developed a GUDMAP Disease Database that queries associations between genes, genitourinary diseases, and renal/urinary and reproductive phenotypes. In collaboration with GUDMAP consortium members at the CCHMC (Cincinnati Children's Hospital Medical Center), the Disease Database is being extended to include mammalian phenotypes mapped to OMIM entries. 

By virtue of its impressive dataset and its ease of use we hope that the GUDMAP Website will continue to serve as a powerful resource for biologists, clinicians and bioinformaticians with an interest in the urogenital system

Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters.</jats:p