OpenRaiser/Intern-Atlas huggingface dataset:
Inverse Graphics: Unsupervised Learning of 3D Shapes from Single Images
Venue: arXiv cs.CV 2019
Year: 2019

Using generative models for Inverse Graphics is an active area of research. However, most works focus on developing models for supervised and semi-supervised methods. In this paper, we study the problem of unsupervised learning of 3D geometry from single images. Our approach is to use a generative model that produces 2-D images as projections of a latent 3D voxel grid, which we train either as a variational auto-encoder or using adversarial methods. Our contributions are as follows: First, we show how to recover 3D shape and pose from general datasets such as MNIST, and MNIST Fashion in good quality. Second, we compare the shapes learned using adversarial and variational methods. Adversarial approach gives denser 3D shapes. Third, we explore the idea of modelling the pose of an object as uniform distribution to recover 3D shape from a single image. Our experiment with the CelebA dataset \cite{liu2015faceattributes} proves that we can recover complete 3D shape from a single image when the object is symmetric along one, or more axis whilst results obtained using ModelNet40 \cite{wu20153d} show the potential side-effects, in which the model learns 3D shapes such that it can render the same image from any viewpoint. Forth, we present a general end-to-end approach to learning 3D shapes from single images in a completely unsupervised fashion by modelling the factors of variation such as azimuth as independent latent variables. Our method makes no assumptions about the dataset, and can work with synthetic as well as real images (i.e. unsupervised in true sense). We present our results, by training the model using the $μ$-VAE objective \cite{ucar2019bridging} and a dataset combining all images from MNIST, MNIST Fashion, CelebA and six categories of ModelNet40. The model is able to learn 3D shapes and the pose in qood quality and leverages information learned across all datasets.
Simultaneous Mapping and Target Driven Navigation
Venue: arXiv cs.CV 2019
Year: 2019

This work presents a modular architecture for simultaneous mapping and target driven navigation in indoors environments. The semantic and appearance stored in 2.5D map is distilled from RGB images, semantic segmentation and outputs of object detectors by convolutional neural networks. Given this representation, the mapping module learns to localize the agent and register consecutive observations in the map. The navigation task is then formulated as a problem of learning a policy for reaching semantic targets using current observations and the up-to-date map. We demonstrate that the use of semantic information improves localization accuracy and the ability of storing spatial semantic map aids the target driven navigation policy. The two modules are evaluated separately and jointly on Active Vision Dataset and Matterport3D environments, demonstrating improved performance on both localization and navigation tasks.
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network
Venue: arXiv cs.LG 2019
Year: 2019

Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cyber-security attacks on internet-connected and autonomous vehicles are becoming a new threat. In this paper, a deep learning model is proposed, which can identify drivers from their driving behaviors based on vehicle telematics data. The proposed Long-Short-Term-Memory (LSTM) model predicts the identity of the driver based on the individual's unique driving patterns learned from the vehicle telematics data. Given the telematics is time-series data, the problem is formulated as a time series prediction task to exploit the embedded sequential information. The performance of the proposed approach is evaluated on three naturalistic driving datasets, which gives high accuracy prediction results. The robustness of the model on noisy and anomalous data that is usually caused by sensor defects or environmental factors is also investigated. Results show that the proposed model prediction accuracy remains satisfactory and outperforms the other approaches despite the extent of anomalies and noise-induced in the data.
Thick-Net: Parallel Network Structure for Sequential Modeling
Venue: arXiv cs.LG 2019
Year: 2019

Recurrent neural networks have been widely used in sequence learning tasks. In previous studies, the performance of the model has always been improved by either wider or deeper structures. However, the former becomes more prone to overfitting, while the latter is difficult to optimize. In this paper, we propose a simple new model named Thick-Net, by expanding the network from another dimension: thickness. Multiple parallel values are obtained via more sets of parameters in each hidden state, and the maximum value is selected as the final output among parallel intermediate outputs. Notably, Thick-Net can efficiently avoid overfitting, and is easier to optimize than the vanilla structures due to the large dropout affiliated with it. Our model is evaluated on four sequential tasks including adding problem, permuted sequential MNIST, text classification and language modeling. The results of these tasks demonstrate that our model can not only improve accuracy with faster convergence but also facilitate a better generalization ability.
"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning
Venue: arXiv cs.CY 2019
Year: 2019

Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus on real world implementation and the associated challenges to accuracy, fairness, accountability, and transparency that come from actual, situated use. Serious questions remain under examined regarding how to ethically build models, interpret and explain model output, recognize and account for biases, and minimize disruptions to professional expertise and work cultures. We address this gap in the literature and provide a detailed case study covering the development, implementation, and evaluation of Sepsis Watch, a machine learning-driven tool that assists hospital clinicians in the early diagnosis and treatment of sepsis. We, the team that developed and evaluated the tool, discuss our conceptualization of the tool not as a model deployed in the world but instead as a socio-technical system requiring integration into existing social and professional contexts. Rather than focusing on model interpretability to ensure a fair and accountable machine learning, we point toward four key values and practices that should be considered when developing machine learning to support clinical decision-making: rigorously define the problem in context, build relationships with stakeholders, respect professional discretion, and create ongoing feedback loops with stakeholders. Our work has significant implications for future research regarding mechanisms of institutional accountability and considerations for designing machine learning systems. Our work underscores the limits of model interpretability as a solution to ensure transparency, accuracy, and accountability in practice. Instead, our work demonstrates other means and goals to achieve FATML values in design and in practice.
In Search of Credible News
Venue: arXiv cs.CL 2019
Year: 2019

We study the problem of finding fake online news. This is an important problem as news of questionable credibility have recently been proliferating in social media at an alarming scale. As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources. We then propose a language-independent approach for automatically distinguishing credible from fake news, based on a rich feature set. In particular, we use linguistic (n-gram), credibility-related (capitalization, punctuation, pronoun use, sentiment polarity), and semantic (embeddings and DBPedia data) features. Our experiments on three different testsets show that our model can distinguish credible from fake news with very high accuracy.
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets
Venue: arXiv cs.CV 2019
Year: 2019

When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting. The identity preservation problem, where the model loses the detailed information of the target leading to a defective output, is the most common failure mode. The problem has several potential sources such as the identity of the driver leaking due to the identity mismatch, or dealing with unseen large poses. To overcome such problems, we introduce components that address the mentioned problem: image attention block, target feature alignment, and landmark transformer. Through attending and warping the relevant features, the proposed architecture, called MarioNETte, produces high-quality reenactments of unseen identities in a few-shot setting. In addition, the landmark transformer dramatically alleviates the identity preservation problem by isolating the expression geometry through landmark disentanglement. Comprehensive experiments are performed to verify that the proposed framework can generate highly realistic faces, outperforming all other baselines, even under a significant mismatch of facial characteristics between the target and the driver.
Dense Fusion Classmate Network for Land Cover Classification
Venue: arXiv cs.CV 2019
Year: 2019

Recently, FCNs based methods have made great progress in semantic segmentation. Different with ordinary scenes, satellite image owns specific characteristics, which elements always extend to large scope and no regular or clear boundaries. Therefore, effective mid-level structure information extremely missing, precise pixel-level classification becomes tough issues. In this paper, a Dense Fusion Classmate Network (DFCNet) is proposed to adopt in land cover classification.
Defending with Shared Resources on a Network
Venue: arXiv cs.GT 2019
Year: 2019

In this paper we consider a defending problem on a network. In the model, the defender holds a total defending resource of R, which can be distributed to the nodes of the network. The defending resource allocated to a node can be shared by its neighbors. There is a weight associated with every edge that represents the efficiency defending resources are shared between neighboring nodes. We consider the setting when each attack can affect not only the target node, but its neighbors as well. Assuming that nodes in the network have different treasures to defend and different defending requirements, the defender aims at allocating the defending resource to the nodes to minimize the loss due to attack. We give polynomial time exact algorithms for two important special cases of the network defending problem. For the case when an attack can only affect the target node, we present an LP-based exact algorithm. For the case when defending resources cannot be shared, we present a max-flow-based exact algorithm. We show that the general problem is NP-hard, and we give a 2-approximation algorithm based on LP-rounding. Moreover, by giving a matching lower bound of 2 on the integrality gap on the LP relaxation, we show that our rounding is tight.
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Venue: arXiv cs.DC 2019
Year: 2019

Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks. However, there exists a discrepancy between theory and practice: while theoretical analysis of most existing compression methods assumes compression is applied to the gradients of the entire model, many practical implementations operate individually on the gradients of each layer of the model. In this paper, we prove that layer-wise compression is, in theory, better, because the convergence rate is upper bounded by that of entire-model compression for a wide range of biased and unbiased compression methods. However, despite the theoretical bound, our experimental study of six well-known methods shows that convergence, in practice, may or may not be better, depending on the actual trained model and compression ratio. Our findings suggest that it would be advantageous for deep learning frameworks to include support for both layer-wise and entire-model compression.
The gift of the gab: Are rental scammers skilled at the art of persuasion?
Venue: arXiv cs.CY 2019
Year: 2019

Rental scams are a type of advance fee fraud, in which the scammer tries to get a victim to pay a deposit to rent an apartment of which the scammer pretends to be the landlord. We specifically focused on fraudulent long-term rentals advertised in the UK on Craigslist. After a victim responds to the scammer's advertisement, the scammer attempts to persuade them to transfer money without having seen the property. We were interested in which persuasion techniques scammers use, and in assessing their skill at the art of persuasion. During a period of three weeks, we scraped 2112 letting advertisements, identified the fraudulent advertisements and had 44 conversations of around 4 or 5 emails each with the scammers. Our analysis indicates that Cialdini`s marketing-based social persuasion strategies, such as liking, appeal to authority, and the need for commitment and consistency are extensively implemented by rental scammers. Of Stajano and Wilson's scam-based persuasion strategies, an appeal to sympathy (i.e., kindness) and need for greed were commonly used. We identified two further social persuasion strategies: establishing credibility and removing objections. At a superficial level, rental scammers seem skilled at their job, because they mimic genuine landlords and use a range of effective persuasion techniques. However, when examining their emails more closely, we see they often use pre-scripted emails, their mimicry is often incompetent, and they have a lack of language skills and cultural knowledge that may tip people off. They appear to be the criminal equivalent of a boilerhouse sales operation, a modus operandi that has not previously been studied by cybercrime researchers.
Python vs. R: A Text Mining Approach for analyzing the Research Trends in Scopus Database
Venue: arXiv cs.CY 2019
Year: 2019

In the contemporary world, with the incubation of advanced technologies and tremendous outbursts of research works, analyzing big data to incorporate research strategies becomes more helpful using the tools and techniques presented in the current research scenario. This paper indeed tries to tackle the most prominent challenges relating to big data analysis by utilizing a text mining approach to analyze research data published in the field of production management as a case to begin with. The study has been conducted by considering research data of International Journal of Production Research (IJPR) indexed in Scopus between 1961-2017 by dividing the analysis incurred into 3 fragments being 1961-1990, 1991-2010 and finally 2011-2017 as a case to highlight the focus of journal. This has indeed provided multi-faceted benefits such as increasing the effectiveness of the procured data with well-established comparisons between R and Python Programming along with providing detailed research trends on the research work incubated. The results of the study highlighted some most prominent topics in the existing IJPR literature such as system's optimization, supplier selection, process design, etc. providing well-established details relating to ongoing research works. The study also compared both languages suiting to a particular field of study for better comprehension and vastness of the research topics. The current research work is one of the part of a copyright work with registration number SW-10310/2018 titled Program for Analyzing Key Trends in Research Data-set. It has been designed in Python for carrying out detailed content analysis based on the available research database in bib format as in the current context it has been applied for IJPR journal and can be replicated on articles of any domain found using keyword search.
Exploring the added value of blockchain technology for the healthcare domain
Venue: arXiv cs.CY 2019
Year: 2019

In this report, the University Medical Center Groningen (UMCG) has written down lessons learned on how blockchain technology can have an impact on the healthcare domain. By looking at two use-cases, the hospital challenged several teams, participating in an open innovation program and blockchain hackathon, to find a solution that showed the added value of the technology for patient care and scientific research. Besides this practical perspective, the report also considers literature discussing the current state of blockchain technology in regard to developments in the healthcare domain (touching on patient empowerment, data management, regulations, and interoperability between healthcare systems).
Towards an Open and Scalable Music Metadata Layer
Venue: arXiv cs.CY 2019
Year: 2019

One of the significant issues in the music supply chain today is the lack of consistent, complete and authoritative information or metadata regarding the creation of a given musical work. In many cases multiple entities in the music supply chain have each created their own version of the metadata for a musical work, often by manually re-entering the same information or through scraping data from other sites. In such cases, the effort to synchronize or to correct the information becomes manually laborious and error-prone. Furthermore, confidential information regarding the legal ownership of the musical work is often commingled in the same metadata, making the entire database proprietary and thus closed. In this paper we explore an alternative model for creation metadata following the open access paradigm found in other industries, such as in book publishing, library systems and in the automotive parts supply chain. The vision is to create a new music metadata layer for creation metadata that is open, scalable and provides an authoritative source of information that is available to all entities in the music supply chain globally.
Zoea -- Composable Inductive Programming Without Limits
Venue: arXiv cs.PL 2019
Year: 2019

Automatic generation of software from some form of specification has been a long standing goal of computer science research. To date successful results have been reported for the production of relatively small programs. This paper presents Zoea which is a simple programming language that allows software to be generated from a specification format that closely resembles a set of automated functional tests. Zoea incorporates a number of advances that enable it to generate software that is large enough to have commercial value. Zoea also allows programs to be composed to form still larger programs. As a result Zoea can be used to produce software of any size and complexity. An overview of the core Zoea language is provided together with a high level description of the symbolic AI based Zoea compiler.
A Model-View-ViewModel (MVVM) Application Framework for Hearing Impairment Diagnosis
Venue: arXiv cs.CY 2019
Year: 2019

Around 466 million people worldwide (over 5% of the world's population) have disabling hearing loss, and out of these 34 million are children. Estimates suggest that by 2050, over 900 million people worldwide will have disabling hearing loss. The annual global cost of unaddressed hearing loss amounts to US$ 750 billion. Early detection of hearing loss can reduce its impact on an individual's life in addition to saving a huge cost. This paper presents the design, implementation, and evaluation of an open-source application framework for hearing impairment diagnosis. The framework is built using the Model-View-ViewModel (MVVM) pattern which separates the development of graphical user interface (GUI) from the development of business and back-end logic. Some of the benefits of the MVVM pattern include reusable components, independent development of GUI and business or back-end logic, flexibility to modify GUI without having to change business or back-end logic, ease of unit testing, and reduced maintenance overhead. The proposed framework along with the open-source code makes it possible to easily extend the application functionality thus enabling other researchers and practitioners to develop their own versions of hearing loss diagnosis applications. The proposed software was evaluated by an otolaryngologist and found to be very beneficial in assisting a clinician to reach a hearing impairment diagnosis conclusion more methodically, swiftly and accurately.
Solar Event Tracking with Deep Regression Networks: A Proof of Concept Evaluation
Venue: arXiv cs.CV 2019
Year: 2019

With the advent of deep learning for computer vision tasks, the need for accurately labeled data in large volumes is vital for any application. The increasingly available large amounts of solar image data generated by the Solar Dynamic Observatory (SDO) mission make this domain particularly interesting for the development and testing of deep learning systems. The currently available labeled solar data is generated by the SDO mission's Feature Finding Team's (FFT) specialized detection modules. The major drawback of these modules is that detection and labeling is performed with a cadence of every 4 to 12 hours, depending on the module. Since SDO image data products are created every 10 seconds, there is a considerable gap between labeled observations and the continuous data stream. In order to address this shortcoming, we trained a deep regression network to track the movement of two solar phenomena: Active Region and Coronal Hole events. To the best of our knowledge, this is the first attempt of solar event tracking using a deep learning approach. Since it is impossible to fully evaluate the performance of the suggested event tracks with the original data (only partial ground truth is available), we demonstrate with several metrics the effectiveness of our approach. With the purpose of generating continuously labeled solar image data, we present this feasibility analysis showing the great promise of deep regression networks for this task.
Improved Compressed String Dictionaries
Venue: arXiv cs.DS 2019
Year: 2019

We introduce a new family of compressed data structures to efficiently store and query large string dictionaries in main memory. Our main technique is a combination of hierarchical Front-coding with ideas from longest-common-prefix computation in suffix arrays. Our data structures yield relevant space-time tradeoffs in real-world dictionaries. We focus on two domains where string dictionaries are extensively used and efficient compression is required: URL collections, a key element in Web graphs and applications such as Web mining; and collections of URIs and literals, the basic components of RDF datasets. Our experiments show that our data structures achieve better compression than the state-of-the-art alternatives while providing very competitive query times.