5,761 research outputs found

    VirtualIdentity : privacy preserving user profiling

    Get PDF
    User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling - which are the intellectual property of the company - to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy preserving manner

    Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

    Full text link
    Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications. Model personalization methods have been studied to overcome this problem. However, existing approaches are mainly under the prerequisite of fully labeled data, which is unrealistic in practice due to the requirement of expertise. The primary issue caused by partial-labeled condition is that, clients with deficient labeled data can suffer from unfair performance gain because they lack adequate insights of local distribution to customize the global model. To tackle this problem, 1) we propose a novel personalized semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents), thus to enhance their perception of local data; 2) based on this paradigm, we design an uncertainty-based data-relation metric to ensure that selected helpers can provide trustworthy pseudo labels instead of misleading the local training; 3) to mitigate the network overload introduced by helper searching, we further develop a helper selection protocol to achieve efficient communication with negligible performance sacrifice. Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partial labeled data, especially in highly heterogeneous setting.Comment: 11 page

    Pain Level Detection From Facial Image Captured by Smartphone

    Get PDF
    Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high

    Social Responsibility Practices Regarding Facilities Granted to Employees and Consumer Protection in Selected European Companies

    Get PDF
    CSR practices are marked by two essential attributes: their voluntary nature and their extraordinary diversity. In addition, it is difficult to draw the fine line between mere compliance with the law, and those initiatives that exceed the legal requirements, giving social partners a guarantee that companies have actually internalized their social and environmental responsibilities. This article aims to restructure this vast field, following two axes: the facilities granted to employees, on one hand, and consumer protection, on the other hand. In terms of methodology, a sample of the largest 13 listed European companies was selected from as many sectors. The content of annual and sustainability reports issued in 2009, was screened for narrative aspects related to social responsibility, and the material was organized according to the matters voluntarily described by companies. In the area of employment rights, several issues were identified: employee representation on the boards of directors, equal opportunities, professional development, and ethical issues. On the topic of consumer rights, suggestive examples were given on several aspects: healthy products, counseling, after-sales service and customer identity protection. The importance of this research goes beyond these case studies; its originality lies in providing a coherent picture of an area that is perceived as very unstructured. As can be seen from the examples included in this paper, most of the socially responsible initiatives that firms exhibit do not have a negative impact on a firm’s profitability, while actually being integrated into products and services as efficiency improvements. Instead, responsible actions can have a major positive impact whenever employees and consumers perceive the company as a fair and generous player who derives profits from the sale of products and services, not in violation of the social partners’ rights.corporate social responsibility, facilities granted to employee, consumer protection, European companies

    Federated Learning in Computer Vision

    Get PDF
    Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with huge datasets often with critical privacy issues, therefore many federated learning approaches have been presented to exploit its distributed and privacy-preserving nature. Firstly, this paper introduces the different FL settings used in computer vision and the main challenges that need to be tackled. Then, it provides a comprehensive overview of the different strategies used for FL in vision applications and presents several different approaches for image classification, object detection, semantic segmentation and for focused settings in face recognition and medical imaging. For the various approaches the considered FL setting, the employed data and methodologies and the achieved results are thoroughly discussed

    First impressions: A survey on vision-based apparent personality trait analysis

    Get PDF
    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

    FLAIR: Federated Learning Annotated Image Repository

    Full text link
    Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suitable for federated learning. FLAIR has 429,078 images from 51,414 Flickr users and captures many of the intricacies typically encountered in federated learning, such as heterogeneous user data and a long-tailed label distribution. We implement multiple baselines in different learning setups for different tasks on this dataset. We believe FLAIR can serve as a challenging benchmark for advancing the state-of-the art in federated learning. Dataset access and the code for the benchmark are available at \url{https://github.com/apple/ml-flair}
    • …
    corecore