NoMoATS: Towards Automatic Detection of Mobile Tracking

AbstractToday’s mobile apps employ third-party advertising and tracking (A&T) libraries, which may pose a threat to privacy. State-of-the-art detects and blocks outgoing A&T HTTP/S requests by using manually curated filter lists (e.g. EasyList), and recently, using machine learning approaches. The major bottleneck of both filter lists and classifiers is that they rely on experts and the community to inspect traffic and manually create filter list rules that can then be used to block traffic or label ground truth datasets. We propose NoMoATS – a system that removes this bottleneck by reducing the daunting task of manually creating filter rules, to the much easier and scalable task of labeling A&T libraries. Our system leverages stack trace analysis to automatically label which network requests are generated by A&T libraries. Using NoMoATS, we collect and label a new mobile traffic dataset. We use this dataset to train decision tree classifiers, which can be applied in real-time on the mobile device and achieve an average F-score of 93%. We show that both our automatic labeling and our classifiers discover thousands of requests destined to hundreds of different hosts, previously undetected by popular filter lists. To the best of our knowledge, our system is the first to (1) automatically label which mobile network requests are engaged in A&T, while requiring to only manually label libraries to their purpose and (2) apply on-device machine learning classifiers that operate at the granularity of URLs, can inspect connections across all apps, and detect not only ads, but also tracking.

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A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data ◽

10.3390/data6080087 ◽

2021 ◽

Vol 6 (8) ◽

pp. 87

Author(s):

Sara Ferreira ◽

Mário Antunes ◽

Manuel E. Correia

Keyword(s):

Machine Learning ◽

Digital Forensics ◽

State Of The Art ◽

Forensic Analysis ◽

Third Party ◽

Support Vector ◽

Multimedia Content ◽

Digital Forensic ◽

Video Frames ◽

Forensic Tools

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

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Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

Natural Language Engineering ◽

10.1017/s1351324920000352 ◽

2020 ◽

pp. 1-21 ◽

Cited By ~ 2

Author(s):

Clément Dalloux ◽

Vincent Claveau ◽

Natalia Grabar ◽

Lucas Emanuel Silva Oliveira ◽

Claudia Maria Cabral Moro ◽

...

Keyword(s):

Machine Learning ◽

Information Extraction ◽

State Of The Art ◽

Automatic Detection ◽

Brazilian Portuguese ◽

Supervised Machine Learning ◽

Biomedical Domain ◽

Learning Approaches ◽

Cross Domain ◽

Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

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Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map

Journal of Personalized Medicine ◽

10.3390/jpm11080785 ◽

2021 ◽

Vol 11 (8) ◽

pp. 785

Author(s):

Quentin Miagoux ◽

Vidisha Singh ◽

Dereck de Mézquita ◽

Valerie Chaudru ◽

Mohamed Elati ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Machine Learning ◽

State Of The Art ◽

Biological Information ◽

Response To Treatment ◽

Patient Specific ◽

Learning Approaches ◽

Data Types ◽

Disease Heterogeneity ◽

Combining Data

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

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Learning fair models and representations

Intelligenza Artificiale ◽

10.3233/ia-190034 ◽

2020 ◽

Vol 14 (1) ◽

pp. 151-178

Author(s):

Luca Oneto

Keyword(s):

Machine Learning ◽

Social Services ◽

Ethical Issues ◽

State Of The Art ◽

Online Advertising ◽

Radical Change ◽

Data Representation ◽

Learning Approaches ◽

Central Question ◽

Disparate Treatment

Machine learning based systems and products are reaching society at large in many aspects of everyday life, including financial lending, online advertising, pretrial and immigration detention, child maltreatment screening, health care, social services, and education. This phenomenon has been accompanied by an increase in concern about the ethical issues that may rise from the adoption of these technologies. In response to this concern, a new area of machine learning has recently emerged that studies how to address disparate treatment caused by algorithmic errors and bias in the data. The central question is how to ensure that the learned model does not treat subgroups in the population unfairly. While the design of solutions to this issue requires an interdisciplinary effort, fundamental progress can only be achieved through a radical change in the machine learning paradigm. In this work, we will describe the state of the art on algorithmic fairness using statistical learning theory, machine learning, and deep learning approaches that are able to learn fair models and data representation.

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From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00025 ◽

2018 ◽

Vol 6 ◽

pp. 343-356 ◽

Cited By ~ 2

Author(s):

Egoitz Laparra ◽

Dongfang Xu ◽

Steven Bethard

Keyword(s):

Neural Network ◽

Machine Learning ◽

Comparative Analysis ◽

State Of The Art ◽

Learning Approaches ◽

Semantic Parsing ◽

Time Intervals ◽

Semantic Composition ◽

Previous State ◽

New Scoring

This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.

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Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Mathematics ◽

10.3390/math8112075 ◽

2020 ◽

Vol 8 (11) ◽

pp. 2075

Author(s):

Óscar Apolinario-Arzube ◽

José Antonio García-Díaz ◽

José Medina-Moreira ◽

Harry Luna-Aveiga ◽

Rafael Valencia-García

Keyword(s):

Machine Learning ◽

Deep Learning ◽

User Interfaces ◽

State Of The Art ◽

Learning Approaches ◽

Word Embeddings ◽

Linguistic Features ◽

Intended Meaning ◽

Language User ◽

Learning Architectures

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

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Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

10.1101/053033 ◽

2016 ◽

Cited By ~ 12

Author(s):

Michael P. Pound ◽

Alexandra J. Burgess ◽

Michael H. Wilson ◽

Jonathan A. Atkinson ◽

Marcus Griffiths ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Analysis ◽

Paradigm Shift ◽

State Of The Art ◽

Plant Phenotyping ◽

Learning Approaches ◽

Challenging Problem ◽

Feature Identification ◽

Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

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EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i6.12967 ◽

2016 ◽

Vol 6 (6) ◽

pp. 3131 ◽

Cited By ~ 4

Author(s):

Sanam Narejo ◽

Eros Pasero ◽

Farzana Kulsoom

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Deep Belief Network ◽

Machine Learning Algorithms ◽

Learning Approaches ◽

Alternative Communication ◽

Eeg Signals ◽

Communication Interface ◽

Belief Network ◽

State Classification

<p>A Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information. During EEG acquisition, artifacts are induced due to involuntary eye movements or eye blink, casting adverse effects on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy. One of the designed SAE models outperforms the performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study, may provide a contribution towards the state of the art performance on the problem of EEG based eye state classification.</p>

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Partial Label Learning with Self-Guided Retraining

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33013542 ◽

2019 ◽

Vol 33 ◽

pp. 3542-3549 ◽

Cited By ~ 10

Author(s):

Lei Feng ◽

Bo An

Keyword(s):

Real World ◽

Optimization Problem ◽

State Of The Art ◽

Ground Truth ◽

Learning Approaches ◽

High Confidence ◽

Infinity Norm ◽

Real World Datasets ◽

Partial Label Learning ◽

Optimization Efficiency

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

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Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

European Journal of Personality ◽

10.1002/per.2290 ◽

2020 ◽

Vol 34 (5) ◽

pp. 826-844 ◽

Cited By ~ 1

Author(s):

Louis Tay ◽

Sang Eun Woo ◽

Louis Hickman ◽

Rachel M. Saef

Keyword(s):

Machine Learning ◽

Social Media ◽

Text Mining ◽

Personality Assessment ◽

Ground Truth ◽

Psychometric Validation ◽

Learning Approaches ◽

Text Data ◽

Personality Psychology ◽

Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

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