OpenRaiser/Intern-Atlas huggingface dataset:
Scale-aware Insertion of Virtual Objects in Monocular Videos
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a scale-aware method for inserting virtual objects with proper sizes into monocular videos. To tackle the scale ambiguity problem of geometry recovery from monocular videos, we estimate the global scale objects in a video with a Bayesian approach incorporating the size priors of objects, where the scene objects sizes should strictly conform to the same global scale and the possibilities of global scales are maximized according to the size distribution of object categories. To do so, we propose a dataset of sizes of object categories: Metric-Tree, a hierarchical representation of sizes of more than 900 object categories with the corresponding images. To handle the incompleteness of objects recovered from videos, we propose a novel scale estimation method that extracts plausible dimensions of objects for scale optimization. Experiments have shown that our method for scale estimation performs better than the state-of-the-art methods, and has considerable validity and robustness for different video scenes. Metric-Tree has been made available at: https://metric-tree.github.io
Generator Pyramid for High-Resolution Image Inpainting
Venue: arXiv cs.CV 2020
Year: 2020

Inpainting high-resolution images with large holes challenges existing deep learning based image inpainting methods. We present a novel framework -- PyramidFill for high-resolution image inpainting task, which explicitly disentangles content completion and texture synthesis. PyramidFill attempts to complete the content of unknown regions in a lower-resolution image, and synthesis the textures of unknown regions in a higher-resolution image, progressively. Thus, our model consists of a pyramid of fully convolutional GANs, wherein the content GAN is responsible for completing contents in the lowest-resolution masked image, and each texture GAN is responsible for synthesizing textures in a higher-resolution image. Since completing contents and synthesising textures demand different abilities from generators, we customize different architectures for the content GAN and texture GAN. Experiments on multiple datasets including CelebA-HQ, Places2 and a new natural scenery dataset (NSHQ) with different resolutions demonstrate that PyramidFill generates higher-quality inpainting results than the state-of-the-art methods. To better assess high-resolution image inpainting methods, we will release NSHQ, high-quality natural scenery images with high-resolution 1920$\times$1080.
A Variant of Gradient Descent Algorithm Based on Gradient Averaging
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we study an optimizer, Grad-Avg to optimize error functions. We establish the convergence of the sequence of iterates of Grad-Avg mathematically to a minimizer (under boundedness assumption). We apply Grad-Avg along with some of the popular optimizers on regression as well as classification tasks. In regression tasks, it is observed that the behaviour of Grad-Avg is almost identical with Stochastic Gradient Descent (SGD). We present a mathematical justification of this fact. In case of classification tasks, it is observed that the performance of Grad-Avg can be enhanced by suitably scaling the parameters. Experimental results demonstrate that Grad-Avg converges faster than the other state-of-the-art optimizers for the classification task on two benchmark datasets.
PeR-ViS: Person Retrieval in Video Surveillance using Semantic Description
Venue: arXiv cs.CV 2020
Year: 2020

A person is usually characterized by descriptors like age, gender, height, cloth type, pattern, color, etc. Such descriptors are known as attributes and/or soft-biometrics. They link the semantic gap between a person's description and retrieval in video surveillance. Retrieving a specific person with the query of semantic description has an important application in video surveillance. Using computer vision to fully automate the person retrieval task has been gathering interest within the research community. However, the Current, trend mainly focuses on retrieving persons with image-based queries, which have major limitations for practical usage. Instead of using an image query, in this paper, we study the problem of person retrieval in video surveillance with a semantic description. To solve this problem, we develop a deep learning-based cascade filtering approach (PeR-ViS), which uses Mask R-CNN [14] (person detection and instance segmentation) and DenseNet-161 [16] (soft-biometric classification). On the standard person retrieval dataset of SoftBioSearch [6], we achieve 0.566 Average IoU and 0.792 %w $IoU > 0.4$, surpassing the current state-of-the-art by a large margin. We hope our simple, reproducible, and effective approach will help ease future research in the domain of person retrieval in video surveillance. The source code and pretrained weights available at https://parshwa1999.github.io/PeR-ViS/.
Planning from Pixels using Inverse Dynamics Models
Venue: arXiv cs.LG 2020
Year: 2020

Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on task completion. These task-conditioned models adaptively focus modeling capacity on task-relevant dynamics, while simultaneously serving as an effective heuristic for planning with sparse rewards. We evaluate our method on challenging visual goal completion tasks and show a substantial increase in performance compared to prior model-free approaches.
Data Lakes for Digital Humanities
Venue: arXiv cs.DB 2020
Year: 2020

Traditional data in Digital Humanities projects bear various formats (structured, semi-structured, textual) and need substantial transformations (encoding and tagging, stemming, lemmatization, etc.) to be managed and analyzed. To fully master this process, we propose the use of data lakes as a solution to data siloing and big data variety problems. We describe data lake projects we currently run in close collaboration with researchers in humanities and social sciences and discuss the lessons learned running these projects.
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn a robust recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this paper, we address the cold-start challenge in the RL-based recommender systems by proposing a meta-level model-based reinforcement learning approach for fast user adaptation. In our approach, we learn to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with few interactions. To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent. Moreover, we model the interaction relationship between the user model and recommendation agent from an information-theoretic perspective. Empirical results show the effectiveness of the proposed method when adapting to new users with only a single interaction sequence. We further provide a theoretical analysis of the recommendation performance bound.
Is It a Plausible Colour? UCapsNet for Image Colourisation
Venue: arXiv cs.CV 2020
Year: 2020

Human beings can imagine the colours of a grayscale image with no particular effort thanks to their ability of semantic feature extraction. Can an autonomous system achieve that? Can it hallucinate plausible and vibrant colours? This is the colourisation problem. Different from existing works relying on convolutional neural network models pre-trained with supervision, we cast such colourisation problem as a self-supervised learning task. We tackle the problem with the introduction of a novel architecture based on Capsules trained following the adversarial learning paradigm. Capsule networks are able to extract a semantic representation of the entities in the image but loose details about their spatial information, which is important for colourising a grayscale image. Thus our UCapsNet structure comes with an encoding phase that extracts entities through capsules and spatial details through convolutional neural networks. A decoding phase merges the entity features with the spatial features to hallucinate a plausible colour version of the input datum. Results on the ImageNet benchmark show that our approach is able to generate more vibrant and plausible colours than exiting solutions and achieves superior performance than models pre-trained with supervision.
Economics of Mobile Data Trading Market
Venue: arXiv cs.GT 2020
Year: 2020

To exploit users' heterogeneous data demands, several mobile network operators worldwide have launched the mobile data trading markets, where users can trade mobile data quota with each other. In this paper, we aim to understand the importance of data trading market (DTM) by studying the users' operator selection and trading decisions, and analyzing the operator's profit maximizing strategy. We model the interactions between the mobile operator and the users as a three-stage Stackelberg game. In Stage I, the operator chooses the operation fee imposed on sellers to maximize its profit. In Stage II, each user chooses his operator. In Stage III, each DTM user chooses his trading decisions. We derive the closed-form expression of the unique Nash equilibrium (NE) in Stages II and III, where every user proposes the same price such that the total demand matches with the total supply. We further show that the Stage I's problem is convex and compute the optimal operation fee. Our analysis and numerical results show that an operator with a small initial market share can increase its profit by proposing a DTM, which is in line with the real-world situation in Hong Kong.
LSB Steganography Using Pixel Locator Sequence with AES
Venue: arXiv cs.CR 2020
Year: 2020

Image steganography is the art of hiding data into images. Secret data such as messages, audio, images can be hidden inside the cover image. This is mainly achieved by hiding the data into the LSB(Least Significant Bit) of the image pixels. To improve the security of steganography, this paper studied data encryption with AES(Advanced Encryption Standard) and LSB based data hiding technique with advanced user-defined encrypted data distribution in pixels other than the common linear computational method of storing data in a linear form. This data distribution file will contain the location of the data(in form of pixel numbers) to be encrypted/decrypted which is further encrypted with AES thus providing double encryption for data and its location stored over pixels. Steganography has many applications such as medical, military, copyright information, etc.
Data Processing and Annotation Schemes for FinCausal Shared Task
Venue: arXiv cs.CL 2020
Year: 2020

This document explains the annotation schemes used to label the data for the FinCausal Shared Task (Mariko et al., 2020). This task is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), to be held at The 28th International Conference on Computational Linguistics (COLING'2020), on December 12, 2020.
A Note on Data Biases in Generative Models
Venue: arXiv cs.CV 2020
Year: 2020

It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net .
Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability
Venue: arXiv cs.CL 2020
Year: 2020

Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5(HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistilBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved ~0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.
Interactive Tools for Reproducible Science -- Understanding, Supporting, and Motivating Reproducible Science Practices
Venue: arXiv cs.HC 2020
Year: 2020

Reproducibility should be a cornerstone of science as it enables validation and reuse. In recent years, the scientific community and the general public became increasingly aware of the reproducibility crisis, i.e. the wide-spread inability of researchers to reproduce published work, including their own. Scientific research is increasingly focused on the creation, observation, processing, and analysis of large data volumes. On the one hand, this transition towards computational and data-intensive science poses new challenges for research reproducibility and reuse. On the other hand, increased availability and advances in computation and web technologies offer new opportunities to address the reproducibility crisis. This thesis reports on user-centered design research conducted at CERN, a key laboratory in data-intensive particle physics. In this thesis, we build a wider understanding of researchers' interactions with tools that support research documentation, preservation, and sharing. From a Human-Computer Interaction (HCI) perspective the following aspects are fundamental: (1) Characterize and map requirements and practices around research preservation and reuse. (2) Understand the wider role and impact of research data management (RDM) tools in scientific workflows. (3) Design tools and interactions that promote, motivate, and acknowledge reproducible research practices. Research reported in this thesis represents the first systematic application of HCI methods in the study and design of interactive tools for reproducible science. We advocate the unique role of HCI in supporting, motivating, and transforming reproducible research practices through the design of tools that enable effective RDM. This thesis paves new ways for interaction with RDM tools that support and motivate reproducible science.
Accurate and Scalable Matching of Translators to Displaced Persons for Overcoming Language Barriers
Venue: arXiv cs.CY 2020
Year: 2020

Residents of developing countries are disproportionately susceptible to displacement as a result of humanitarian crises. During such crises, language barriers impede aid workers in providing services to those displaced. To build resilience, such services must be flexible and robust to a host of possible languages. \textit{Tarjimly} aims to overcome the barriers by providing a platform capable of matching bilingual volunteers to displaced persons or aid workers in need of translating. However, Tarjimly's large pool of translators comes with the challenge of selecting the right translator per request. In this paper, we describe a machine learning system that matches translator requests to volunteers at scale. We demonstrate that a simple logistic regression, operating on easily computable features, can accurately predict and rank translator response. In deployment, this lightweight system matches 82\% of requests with a median response time of 59 seconds, allowing aid workers to accelerate their services supporting displaced persons.
FinnSentiment -- A Finnish Social Media Corpus for Sentiment Polarity Annotation
Venue: arXiv cs.CL 2020
Year: 2020

Sentiment analysis and opinion mining is an important task with obvious application areas in social media, e.g. when indicating hate speech and fake news. In our survey of previous work, we note that there is no large-scale social media data set with sentiment polarity annotations for Finnish. This publications aims to remedy this shortcoming by introducing a 27,000 sentence data set annotated independently with sentiment polarity by three native annotators. We had the same three annotators for the whole data set, which provides a unique opportunity for further studies of annotator behaviour over time. We analyse their inter-annotator agreement and provide two baselines to validate the usefulness of the data set.
Advocating for Multiple Defense Strategies against Adversarial Examples
Venue: arXiv cs.LG 2020
Year: 2020

It has been empirically observed that defense mechanisms designed to protect neural networks against $\ell_\infty$ adversarial examples offer poor performance against $\ell_2$ adversarial examples and vice versa. In this paper we conduct a geometrical analysis that validates this observation. Then, we provide a number of empirical insights to illustrate the effect of this phenomenon in practice. Then, we review some of the existing defense mechanism that attempts to defend against multiple attacks by mixing defense strategies. Thanks to our numerical experiments, we discuss the relevance of this method and state open questions for the adversarial examples community.
Supporting Round-Trip Data Migration for Web APIs: A Henshin Solution
Venue: arXiv cs.SE 2020
Year: 2020

We present a solution to the Round-Trip Migration case of the Transformation Tool Contest 2020, based on the Henshin model transformation language. The task is to support four scenarios of transformations between two versions of the same data metamodel, a problem inspired by the application scenario of Web API migration, where such a round-trip migration methodology might mitigate drawbacks of the conventional "instant" migration style. Our solution relies on Henshin's visual syntax, which seems well-suited to capture the problem on an intuitive level, since the syntax is already similar to the scenario illustrations in the case description. We discuss the five evaluation criteria expressiveness, comprehensibility, bidirectionality, performance, and reusability.
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
Venue: arXiv cs.LG 2020
Year: 2020

To train sophisticated machine learning models one usually needs many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough on its own. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in $ε$-differential privacy) or omitting certain values (e.g., for $k$-anonymity). Other measures based on cryptographic methods can lead to very time-consuming computations, which is especially problematic for larger multi-omics data. We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework, enabling the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise. We evaluated our framework on drug resistance prediction for HIV-infected people and multi-omics dimensionality reduction and clustering problems in precision medicine. In terms of execution time, our framework significantly outperforms the best-fitting existing approaches without sacrificing the performance of the algorithm. Even though we only show the benefit for kernel-based algorithms, our framework can open up new research opportunities for further machine learning models that require the dot product of vectors from multiple sources.
Efficient Sealable Protection Keys for RISC-V
Venue: arXiv cs.CR 2020
Year: 2020

With the continuous increase in the number of software-based attacks, there has been a growing effort towards isolating sensitive data and trusted software components from untrusted third-party components. A hardware-assisted intra-process isolation mechanism enables software developers to partition a process into isolated components and in turn secure sensitive data from untrusted components. However, most of the existing hardware-assisted intra-process isolation mechanisms in modern processors, such as ARM and IBM Power, rely on costly kernel operations for switching between trusted and untrusted domains. Recently, Intel introduced a new hardware feature for intra-process memory isolation, called Memory Protection Keys (MPK), which enables a user-space process to switch the domains in an efficient way. While the efficiency of Intel MPK enables developers to leverage it for common use cases such as Code-Pointer Integrity, the limited number of unique domains (16) prohibits its use in cases such as OpenSSL where a large number of domains are required. Moreover, Intel MPK suffers from the protection key use-after-free vulnerability. To address these shortcomings, in this paper, we propose an efficient intra-process isolation technique for the RISC-V open ISA, called SealPK, which supports up to 1024 unique domains. SealPK prevents the protection key use-after-free problem by leveraging a lazy de-allocation approach. To further strengthen SealPK, we devise three novel sealing features to protect the allocated domains, their associated pages, and their permissions from modifications or tampering by an attacker. To demonstrate the feasibility of our design, we implement SealPK on a RISC-V Rocket processor, provide the OS support for it, and prototype our design on an FPGA. We demonstrate the efficiency of SealPK by leveraging it to implement an isolated shadow stack on our FPGA prototype.