Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality

Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes

Estimating poverty maps from aggregated mobile communication networks

Governments and other organisations often rely on data collected by household surveys and censuses to provide estimates of household poverty and identify areas in most need of regeneration and development investment. However, due to the high cost associated with manual data collection and processing, many developing countries conduct such surveys very infrequently, if at all, and only at a coarse level of spatial granularity. Consequently, it becomes difficult for governments and NGOs to determine where and when to intervene. This thesis addresses this problem by examining the feasibility of deriving up to date and high resolution proxy measurements of poverty from an alternative source of data, namely, Call Detail Records (CDRs), which can be used by organisations to help in decision making. Specifically, we contribute the following: 1. A detailed spatial analysis of economic wealth in two sub-Saharan countries, Senegal and Cote d’Ivoire from which we derive two baseline poverty esti- ˆ mators grounded on concrete usage scenarios. 2. We establish a link between communication patterns and wealth through a simulation-based analysis of information diffusion. We further examine the influence of contextual factors, including data quality issues and economic volatility, on the strength of this relationship. 3. An approach to building wealth prediction models based on features of aggregated CDRs. Features include static and simulation based measures of information access, activity based metrics and econometric inspired metrics. We further perform a comparative analysis of the results of several models in relation to the baseline predictors. We conclude that it is possible to produce proxy poverty or wealth indicators from aggregated CDRs that provide a good level of accuracy, particularly where geographical coverage of the mobile phone network is sufficient. The final outcome of this thesis is a method for developing aggregated CDR-based poverty or wealth models that can be readily implemented anywhere in which there is a need for more up to date and/or finer resolution poverty estimates

Algorithms for Social Good: A Study of Fairness and Bias in Automated Data-Driven Decision-Making Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Satellite Imagery Approach to Estimating Migratory Flows in Guatemala Using Convolutional Neural Networks

Being able to predict migratory flows is important in ensuring political, social, and economic stability. In the wake of violence, unrest, natural disasters, and social pressures, millions of mi- grants have fled Central America in search of a better life. However, due to the infrequent nature and high cost of census data, there is a need for a more remote and up to date approaches. Con- volutional Neural Networks offer a computer vision based approach that is cheaper and with significantly less lag. In this study, we seek to evaluate the effectiveness of different convolu- tional neural networks in predicting migratory patterns in Guatemala. Using a combination of open source satellite images and census data, we implement a variety of network architec- tures that seek to predict migration both through regression and classification techniques. We find that while regression and classification models do not prove to be an effective tool, there is an opportunity for additional research into the spatial nature of migratory prediction. Our preliminary results affirm the need for continued research and advancement in deep learning algorithms to predict migratory flows

An Integrated Fuzzy Inference Based Monitoring, Diagnostic, and Prognostic System

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable grab bag of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ~45 hours and 95% CIs of ~162 hours

SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

Perceptual video quality assessment models are either frame-based or video-based, i.e., they apply spatiotemporal filtering or motion estimation to capture temporal video distortions. Despite their good performance on video quality databases, video-based approaches are time-consuming and harder to efficiently deploy. To balance between high performance and computational efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF) framework, which integrates multiple quality-aware features to predict video quality. Nevertheless, this fusion framework does not fully exploit temporal video quality measurements which are relevant to temporal video distortions. To this end, we propose two improvements to the VMAF framework: SpatioTemporal VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video features which are fed into a single or multiple regression models. To train our models, we designed a large subjective database and evaluated the proposed models against state-of-the-art approaches. The compared algorithms will be made available as part of the open source package in https://github.com/Netflix/vmaf

Campus Safety Data Gathering, Classification, and Ranking Based on Clery-Act Reports

Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions. In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate a safety rank based on these reports. We first provide a survey of campus safety and security measures. We utilize our survey results to provide an automated data-gathering method for capturing standard campus safety data from Clery-act reports. We then utilize the collected information to classify existing campuses based on their safety conditions. Our research model is also capable to predict the safety rank of campuses based on their Clery-Act report by comparing it to existing Clery-Act reports of other campuses and reported rank on public resources. Our research on this thesis uses a number of languages, tools, and technologies such as Python, shell scripts, text conversion, data mining, spreadsheets, and others. We provide a detailed description of our research work on this topic, explain our research methodology, and finally describe our findings and results. This research contributes to the automated campus safety data generation, classification, and ranking

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines