OpenRaiser/Intern-Atlas huggingface dataset:
An Exploration of Verbatim Content Republishing by News Producers
Venue: arXiv cs.CY 2018
Year: 2018

In today's news ecosystem, news sources emerge frequently and can vary widely in intent. This intent can range from benign to malicious, with many tactics being used to achieve their goals. One lesser studied tactic is content republishing, which can be used to make specific stories seem more important, create uncertainty around an event, or create a perception of credibility for unreliable news sources. In this paper, we take a first step in understanding this tactic by exploring verbatim content copying across 92 news producers of various characteristics. We find that content copying occurs more frequently between like-audience sources (eg. alternative news, mainstream news, etc.), but there consistently exists sparse connections between these communities. We also find that despite articles being verbatim, the headlines are often changed. Specifically, we find that mainstream sources change more structural features, while alternative sources change many more content features, often changing the emotional tone and bias of the titles. We conclude that content republishing networks can help identify and label the intent of brand-new news sources using the tight-knit community they belong to. In addition, it is possible to use the network to find important content producers in each community, producers that are used to amplify messages of other sources, and producers that distort the messages of other sources.
Data Analytics Service Composition and Deployment on Edge Devices
Venue: arXiv cs.SE 2018
Year: 2018

Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.
Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity
Venue: arXiv cs.CY 2018
Year: 2018

The proliferation of Internet of Things (IoT) devices for consumer "smart" homes raises concerns about user privacy. We present a survey method based on the Contextual Integrity (CI) privacy framework that can quickly and efficiently discover privacy norms at scale. We apply the method to discover privacy norms in the smart home context, surveying 1,731 American adults on Amazon Mechanical Turk. For $2,800 and in less than six hours, we measured the acceptability of 3,840 information flows representing a combinatorial space of smart home devices sending consumer information to first and third-party recipients under various conditions. Our results provide actionable recommendations for IoT device manufacturers, including design best practices and instructions for adopting our method for further research.
Coded Iterative Computing using Substitute Decoding
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a new coded computing technique called "substitute decoding" for general iterative distributed computation tasks. In the first part of the paper, we use PageRank as a simple example to show that substitute decoding can make the computation of power iterations solving PageRank on sparse matrices robust to erasures in distributed systems. For these sparse matrices, codes with dense generator matrices can significantly increase storage costs and codes with low-density generator matrices (LDGM) are preferred. Surprisingly, we show through both theoretical analysis and simulations that when substitute decoding is used, coded iterative computing with extremely low-density codes (2 or 3 non-zeros in each row of the generator matrix) can achieve almost the same convergence rate as noiseless techniques, despite the poor error-correction ability of LDGM codes. In the second part of the paper, we discuss applications of substitute decoding beyond solving linear systems and PageRank. These applications include (1) computing eigenvectors, (2) computing the truncated singular value decomposition (SVD), and (3) gradient descent. These examples show that the substitute decoding algorithm is useful in a wide range of applications.
Graph-based Ontology Summarization: A Survey
Venue: arXiv cs.IR 2018
Year: 2018

Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Venue: arXiv cs.CL 2018
Year: 2018

Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and dissimilar (out of domain) instances to those seen in training. This requires learning an underlying task, while not learning irrelevant signals and biases specific to individual domains. We propose a novel method to optimise both in- and out-of-domain accuracy based on joint learning of a structured neural model with domain-specific and domain-general components, coupled with adversarial training for domain. Evaluating on multi-domain language identification and multi-domain sentiment analysis, we show substantial improvements over standard domain adaptation techniques, and domain-adversarial training.
Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model
Venue: arXiv cs.CR 2018
Year: 2018

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. An adversary such as an untrusted location-based server can monitor the queried locations by a user to infer critical information such as the user's home address, health conditions, shopping habits, etc. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only consider a limited amount of side information known by an adversary which may face more serious challenges in practice. In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm. Then, we develop an attack model based on the Viterbi algorithm which can significantly threaten the location privacy of the users. Next, in order to protect the users from Viterbi attack, we propose an algorithm called robust dummy generation (RDG) which can resist against the Viterbi attack while maintaining a high performance in terms of the privacy metrics introduced in the paper. All the algorithms are applied and analyzed on a real-life dataset.
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification aims to match a person's identity across multiple camera streams. Deep neural networks have been successfully applied to the challenging person re-identification task. One remarkable bottleneck is that the existing deep models are data hungry and require large amounts of labeled training data. Acquiring manual annotations for pedestrian identity matchings in large-scale surveillance camera installations is a highly cumbersome task. Here, we propose the first semi-supervised approach that performs pseudo-labeling by considering complex relationships between unlabeled and labeled training samples in the feature space. Our approach first approximates the actual data manifold by learning a generative model via adversarial training. Given the trained model, data augmentation can be performed by generating new synthetic data samples which are unlabeled. An open research problem is how to effectively use this additional data for improved feature learning. To this end, this work proposes a novel Feature Affinity based Pseudo-Labeling (FAPL) approach with two possible label encodings under a unified setting. Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks. FAPL trains with the joint supervision of cross-entropy loss together with a center regularization term, which not only ensures discriminative feature representation learning but also simultaneously predicts pseudo-labels for unlabeled data. Our extensive experiments on two standard large-scale datasets, Market-1501 and DukeMTMC-reID, demonstrate significant performance boosts over closely related competitors and outperforms state-of-the-art person re-identification techniques in most cases.
A Note on QR-Based Model Reduction: Algorithm, Software, and Gravitational Wave Applications
Venue: arXiv cs.DC 2018
Year: 2018

While the proper orthogonal decomposition (POD) is optimal under certain norms it's also expensive to compute. For large matrix sizes, it is well known that the QR decomposition provides a tractable alternative. Under the assumption that it is rank--revealing QR (RRQR), the approximation error incurred is similar to the POD error and, furthermore, we show the existence of an RRQR with exactly same error estimate as POD. To numerically realize an RRQR decomposition, we will discuss the (iterative) modified Gram Schmidt with pivoting (MGS) and reduced basis method by employing a greedy strategy. We show that these two, seemingly different approaches from linear algebra and approximation theory communities are in fact equivalent. Finally, we describe an MPI/OpenMP parallel code that implements one of the QR-based model reduction algorithms we analyze. This code was developed with model reduction in mind, and includes functionality for tasks that go beyond what is required for standard QR decompositions. We document the code's scalability and show it to be capable of tackling large problems. In particular, we apply our code to a model reduction problem motivated by gravitational waves emitted from binary black hole mergers and demonstrate excellent weak scalability on the supercomputer Blue Waters up to 32,768 cores and for complex, dense matrices as large as 10,000-by-3,276,800 (about half a terabyte in size).
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning
Venue: arXiv cs.RO 2018
Year: 2018

Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural architecture for learning multi-modal navigation policies. FollowNet maps natural language instructions as well as visual and depth inputs to locomotion primitives. FollowNet processes instructions using an attention mechanism conditioned on its visual and depth input to focus on the relevant parts of the command while performing the navigation task. Deep reinforcement learning (RL) a sparse reward learns simultaneously the state representation, the attention function, and control policies. We evaluate our agent on a dataset of complex natural language directions that guide the agent through a rich and realistic dataset of simulated homes. We show that the FollowNet agent learns to execute previously unseen instructions described with a similar vocabulary, and successfully navigates along paths not encountered during training. The agent shows 30% improvement over a baseline model without the attention mechanism, with 52% success rate at novel instructions.
Zero-Shot Object Detection by Hybrid Region Embedding
Venue: arXiv cs.CV 2018
Year: 2018

Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images. In this study, we define a more difficult scenario, namely zero-shot object detection (ZSD) where no visual training data is available for some of the target object classes. We present a novel approach to tackle this ZSD problem, where a convex combination of embeddings are used in conjunction with a detection framework. For evaluation of ZSD methods, we propose a simple dataset constructed from Fashion-MNIST images and also a custom zero-shot split for the Pascal VOC detection challenge. The experimental results suggest that our method yields promising results for ZSD.
Color naming reflects both perceptual structure and communicative need
Venue: arXiv cs.CL 2018
Year: 2018

Gibson et al. (2017) argued that color naming is shaped by patterns of communicative need. In support of this claim, they showed that color naming systems across languages support more precise communication about warm colors than cool colors, and that the objects we talk about tend to be warm-colored rather than cool-colored. Here, we present new analyses that alter this picture. We show that greater communicative precision for warm than for cool colors, and greater communicative need, may both be explained by perceptual structure. However, using an information-theoretic analysis, we also show that color naming across languages bears signs of communicative need beyond what would be predicted by perceptual structure alone. We conclude that color naming is shaped both by perceptual structure, as has traditionally been argued, and by patterns of communicative need, as argued by Gibson et al. - although for reasons other than those they advanced.
Robust curvelet domain watermarking technique that preserves cleanness of high quality images
Venue: arXiv cs.MM 2018
Year: 2018

Watermarking inserts invisible data into content to protect copyright. The embedded information provides proof of authorship and facilitates tracking illegal distribution, etc. Current robust watermarking techniques have been proposed to preserve inserted copyright information from various attacks, such as content modification and watermark removal attack. However, since the watermark is inserted in the form of noise, there is an inevitable effect of reducing content visual quality. In general, more robust watermarking techniques tend to have larger effect on the quality, and content creators and users are often reluctant to insert watermarks. Thus, there is a demand for a watermark that maintains maximum image quality, even if the watermark performance is slightly inferior. Therefore, we propose a watermarking technique that maximizes invisibility while maintaining sufficient robustness and data capacity enough to be applied for real situations. The proposed method minimizes watermarking energy by adopting curvelet domain multi-directional decomposition to maximize invisibility, and maximizes robustness against signal processing attack by watermarking pattern suitable for curvelet transformation. The method is also robust against geometric attack by employing watermark detection method utilizing curvelet characteristics. The proposed method showed very good results of 57.65 dB peak signal-to-noise ratio in fidelity tests, and mean opinion score showed that images treated with the proposed method were hardly distinguishable from the originals. The proposed technique also showed good robustness against signal processing and geometric attacks compared with existing techniques.
Using AI to Hack IA: A New Stealthy Spyware Against Voice Assistance Functions in Smart Phones
Venue: arXiv cs.CR 2018
Year: 2018

Intelligent Personal Assistant (IA), also known as Voice Assistant (VA), has become increasingly popular as a human-computer interaction mechanism. Most smartphones have built-in voice assistants that are granted high privilege, which is able to access system resources and private information. Thus, once the voice assistants are exploited by attackers, they become the stepping stones for the attackers to hack into the smartphones. Prior work shows that the voice assistant can be activated by inter-component communication mechanism, through an official Android API. However, this attack method is only effective on Google Assistant, which is the official voice assistant developed by Google. Voice assistants in other operating systems, even custom Android systems, cannot be activated by this mechanism. Prior work also shows that the attacking voice commands can be inaudible, but it requires additional instruments to launch the attack, making it unrealistic for real-world attack. We propose an attacking framework, which records the activation voice of the user, and launch the attack by playing the activation voice and attack commands via the built-in speaker. An intelligent stealthy module is designed to decide on the suitable occasion to launch the attack, preventing the attack being noticed by the user. We demonstrate proof-of-concept attacks on Google Assistant, showing the feasibility and stealthiness of the proposed attack scheme. We suggest to revise the activation logic of voice assistant to be resilient to the speaker based attack.
Artificial Intelligence Inspired Self-Deployment of Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose a self-deployment approach for finding the optimal placement of extenders in which both the wireless back-haul and front-haul throughput of the extender are optimized. We present an artificial intelligence (AI) case based reasoning (CBR) framework that enables autonomous self-deployment in which the network can learn the environment by means of sensing and perception. New actions, i.e. extender positions, are created by problem-specific optimization and semi-supervised learning algorithms that balance exploration and exploitation of the search space. An IEEE 802.11 standard compliant simulations are performed to evaluate the framework on a large scale and compare its performance against existing conventional coverage maximization approaches. Experimental evaluation is also performed in an enterprise environment to demonstrate the competence of the proposed AI-framework in perceiving such a dense scenario and reason the extender deployment that achieves user quality of service (QoS). Throughput fairness and ubiquitous QoS satisfaction are achieved which provide a leap to apply AI-driven self-deployment in wireless networks.
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning methods have shown impressive results for a variety of medical problems over the last few years. However, datasets tend to be small due to time-consuming annotation. As datasets with different patients are often very heterogeneous generalization to new patients can be difficult. This is complicated further if large differences in image acquisition can occur, which is common during intravascular optical coherence tomography for coronary plaque imaging. We address this problem with an adversarial training strategy where we force a part of a deep neural network to learn features that are independent of patient- or acquisitionspecific characteristics. We compare our regularization method to typical data augmentation strategies and show that our approach improves performance for a small medical dataset.
An Analysis Scheme for Investigation of Effects of Various Parameters on Signals in Acoustic-Resolution Photoacoustic Microscopy of Mice Brain: a Simulation Study
Venue: arXiv cs.CE 2018
Year: 2018

Photoacoustic spectral analysis is a novel tool for studying various parameters affecting signals in Photoacoustic microscopy. But only observing frequency components of photoacoustic signals doesn't make enough data for a desirable analysis. Thus a hybrid time-domain and frequency-domain analysis scheme has been proposed to investigate effects of various parameters like depth of microscopy, laser focal spot size and contrast agent concentration on Photoacoustic signals.
Integrated Bounds for Disintegrated Storage
Venue: arXiv cs.DC 2018
Year: 2018

We point out a somewhat surprising similarity between non-authenticated Byzantine storage, coded storage, and certain emulations of shared registers from smaller ones. A common characteristic in all of these is the inability of reads to safely return a value obtained in a single atomic access to shared storage. We collectively refer to such systems as disintegrated storage, and show integrated space lower bounds for asynchronous regular wait-free emulations in all of them. In a nutshell, if readers are invisible, then the storage cost of such systems is inherently exponential in the size of written values; otherwise, it is at least linear in the number of readers. Our bounds are asymptotically tight to known algorithms, and thus justify their high costs.
Efficient and Deterministic Record & Replay for Actor Languages
Venue: arXiv cs.PL 2018
Year: 2018

With the ubiquity of parallel commodity hardware, developers turn to high-level concurrency models such as the actor model to lower the complexity of concurrent software. However, debugging concurrent software is hard, especially for concurrency models with a limited set of supporting tools. Such tools often deal only with the underlying threads and locks, which is at the wrong abstraction level and may even introduce additional complexity. To improve on this situation, we present a low-overhead record & replay approach for actor languages. It allows one to debug concurrency issues deterministically based on a previously recorded trace. Our evaluation shows that the average run-time overhead for tracing on benchmarks from the Savina suite is 10% (min. 0%, max. 20%). For Acme-Air, a modern web application, we see a maximum increase of 1% in latency for HTTP requests and about 1.4 MB/s of trace data. These results are a first step towards deterministic replay debugging of actor systems in production.