OpenRaiser/Intern-Atlas huggingface dataset:
Overt visual attention on rendered 3D objects
Venue: arXiv cs.HC 2019
Year: 2019

This work covers multiple aspects of overt visual attention on 3D renders: measurement, projection, visualization, and application to studying the influence of material appearance on looking behaviour. In the scope of this work, we ran an eye-tracking experiment in which the observers are presented with animations of rotating 3D objects. The objects were rendered to simulate different metallic appearance, particularly smooth (glossy), rough (matte), and coated gold. The eye-tracking results illustrate how material appearance itself influences the observer's attention, while all the other parameters remain unchanged. In order to make visualization of the attention maps more natural and also make the analysis more accurate, we develop a novel technique of projection of gaze fixations on the 3D surface of the figure itself, instead of the conventional 2D plane of the screen. The proposed methodology will be useful for further studies of attention and saliency in the computer graphics domain.
Bayesian Tensorized Neural Networks with Automatic Rank Selection
Venue: arXiv cs.LG 2019
Year: 2019

Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms. However, directly applying tensor compression in the training process is a challenging task due to the difficulty of choosing a proper tensor rank. In order to achieve this goal, this paper proposes a Bayesian tensorized neural network. Our Bayesian method performs automatic model compression via an adaptive tensor rank determination. We also present approaches for posterior density calculation and maximum a posteriori (MAP) estimation for the end-to-end training of our tensorized neural network. We provide experimental validation on a fully connected neural network, a CNN and a residual neural network where our work produces $7.4\times$ to $137\times$ more compact neural networks directly from the training.
PTrix: Efficient Hardware-Assisted Fuzzing for COTS Binary
Venue: arXiv cs.SE 2019
Year: 2019

Despite its effectiveness in uncovering software defects, American Fuzzy Lop (AFL), one of the best grey-box fuzzers, is inefficient when fuzz-testing source-unavailable programs. AFL's binary-only fuzzing mode, QEMU-AFL, is typically 2-5X slower than its source-available fuzzing mode. The slowdown is largely caused by the heavy dynamic instrumentation. Recent fuzzing techniques use Intel Processor Tracing (PT), a light-weight tracing feature supported by recent Intel CPUs, to remove the need of dynamic instrumentation. However, we found that these PT-based fuzzing techniques are even slower than QEMU-AFL when fuzzing real-world programs, making them less effective than QEMU-AFL. This poor performance is caused by the slow extraction of code coverage information from highly compressed PT traces. In this work, we present the design and implementation of PTrix, which fully unleashes the benefits of PT for fuzzing via three novel techniques. First, PTrix introduces a scheme to highly parallel the processing of PT trace and target program execution. Second, it directly takes decoded PT trace as feedback for fuzzing, avoiding the expensive reconstruction of code coverage information. Third, PTrix maintains the new feedback with stronger feedback than edge-based code coverage, which helps reach new code space and defects that AFL may not. We evaluated PTrix by comparing its performance with the state-of-the-art fuzzers. Our results show that, given the same amount of time, PTrix achieves a significantly higher fuzzing speed and reaches into code regions missed by the other fuzzers. In addition, PTrix identifies 35 new vulnerabilities in a set of previously well-fuzzed binaries, showing its ability to complement existing fuzzers.
SAASQUAL: A Quality Model For Evaluating SaaS on The Cloud Computing Environment
Venue: arXiv cs.SE 2019
Year: 2019

Cloud computing is a Technology that has come out in the last decade and that is transforming the IT industry in huge. The Cloud computing is playing a vital role as a backbone component of the Internet of Things (IoT). In a Cloud Computing scenario, cloud services are accessible via Internet. Cloud computing is providing ondemand resources like Infrastructure, platform, and software as it is do not pay to possess the software itself but rather to use it. Pay for use concept is very attractive, hence many organizations are adopting the SaaS model drastically. Even though, each customer is unique and leads to unique variation in the requirements of the software. the SaaS is generally press into service and it yields advantages to service providers and service customers. More and more SaaS services are emerging, how to select qualified service is key problem for customers. Present quality models are not sufficient to evaluate SaaS selection on the cloud due to its tremendous increasing in the use. A quality model can be used to represent, evaluate, and differentiate the quality of the SaaS providers. In this paper, a new quality model proposed and named SAASQUAL for cloud software services.This model is based on different attributes of quality software, quality service and metrics that measure software quality and service quality in order to evaluate potential software as a service on the cloud.
Exploring Temporal Information for Improved Video Understanding
Venue: arXiv cs.CV 2019
Year: 2019

In this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and semantic segmentation. For action recognition, I have proposed a framework, termed hidden two-stream networks, to learn an optimal motion representation that does not require the computation of optical flow. My framework alleviates several challenges faced in video classification, such as learning motion representations, real-time inference, multi-framerate handling, generalizability to unseen actions, etc. For semantic segmentation, I have introduced a general framework that uses video prediction models to synthesize new training samples. By scaling up the training dataset, my trained models are more accurate and robust than previous models even without modifications to the network architectures or objective functions. I believe videos have much more potential to be mined, and temporal information is one of the most important cues for machines to perceive the visual world better.
A Taxonomy to Assess and Tailor Risk-based Testing in Recent Testing Standards
Venue: arXiv cs.SE 2019
Year: 2019

This article provides a taxonomy for risk-based testing that serves as a tool to define, tailor, or assess risk-based testing approaches in general and to instantiate risk-based testing approaches for the current testing standards ISO/IEC/IEEE 29119, ETSI EG and OWASP Security Testing Guide in particular. We demonstrate the usefulness of the taxonomy by applying it to the aforementioned standards as well as to the risk-based testing approaches SmartTesting, RACOMAT, PRISMA and risk-based test case prioritization using fuzzy expert systems. In this setting, the taxonomy is used to systematically identify deviations between the standards' requirements and the individual testing approaches so that we are able to position and compare the testing approaches and discuss their potential for practical application.
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
Venue: arXiv cs.LG 2019
Year: 2019

Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long-term histories are also used to consider periodicity and seasonality patterns as time series data. In this study, we propose an efficient architecture, Temporal-Guided Network (TGNet), which utilizes graph networks and temporal-guided embedding. Graph networks extract invariant features to permutations of adjacent regions instead of convolutional layers. Temporal-guided embedding explicitly learns temporal contexts from training data and is substituted for the input of long-term histories from days/weeks ago. TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding. Finally, our model achieves competitive performances with other baselines on three spatiotemporal demand dataset from real-world, but the number of trainable parameters is about 20 times smaller than a state-of-the-art baseline. We also show that temporal-guided embedding learns temporal contexts as intended and TGNet has robust forecasting performances even to atypical event situations.
Hashing based Answer Selection
Venue: arXiv cs.CL 2019
Year: 2019

Answer selection is an important subtask of question answering (QA), where deep models usually achieve better performance. Most deep models adopt question-answer interaction mechanisms, such as attention, to get vector representations for answers. When these interaction based deep models are deployed for online prediction, the representations of all answers need to be recalculated for each question. This procedure is time-consuming for deep models with complex encoders like BERT which usually have better accuracy than simple encoders. One possible solution is to store the matrix representation (encoder output) of each answer in memory to avoid recalculation. But this will bring large memory cost. In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. HAS adopts a hashing strategy to learn a binary matrix representation for each answer, which can dramatically reduce the memory cost for storing the matrix representations of answers. Hence, HAS can adopt complex encoders like BERT in the model, but the online prediction of HAS is still fast with a low memory cost. Experimental results on three popular answer selection datasets show that HAS can outperform existing models to achieve state-of-the-art performance.
Justifications of Welfare Guarantees under Normalized Utilities
Venue: arXiv cs.GT 2019
Year: 2019

It is standard in computational social choice to analyse welfare considerations under the assumptions of normalized utilities. In this note, we summarize some common reasons for this approach. We then mention another justification which is ignored but has solid normative appeal. The central concept used in the `new' justification can also be used more widely as a social objective.
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Venue: arXiv cs.LG 2019
Year: 2019

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.
Nonparametric Online Learning Using Lipschitz Regularized Deep Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural networks are considered to be state of the art models in many offline machine learning tasks. However, their performance and generalization abilities in online learning tasks are much less understood. Therefore, we focus on online learning and tackle the challenging problem where the underlying process is stationary and ergodic and thus removing the i.i.d. assumption and allowing observations to depend on each other arbitrarily. We prove the generalization abilities of Lipschitz regularized deep neural networks and show that by using those networks, a convergence to the best possible prediction strategy is guaranteed.
Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution
Venue: arXiv cs.CL 2019
Year: 2019

We provide the first computational treatment of fused-heads constructions (FH), focusing on the numeric fused-heads (NFH). FHs constructions are noun phrases (NPs) in which the head noun is missing and is said to be `fused' with its dependent modifier. This missing information is implicit and is important for sentence understanding. The missing references are easily filled in by humans but pose a challenge for computational models. We formulate the handling of FH as a two stages process: identification of the FH construction and resolution of the missing head. We explore the NFH phenomena in large corpora of English text and create (1) a dataset and a highly accurate method for NFH identification; (2) a 10k examples (1M tokens) crowd-sourced dataset of NFH resolution; and (3) a neural baseline for the NFH resolution task. We release our code and dataset, in hope to foster further research into this challenging problem.
Adaptive Learning Material Recommendation in Online Language Education
Venue: arXiv cs.IR 2019
Year: 2019

Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials. This makes it hard to recommend appropriate content that matches the student's prior knowledge. In this paper, we propose a refined hierarchical knowledge structure to model vocabulary knowledge, which enables us to automatically organize the authentic and up-to-date learning materials collected from the internet. Based on this knowledge structure, we then introduce a hybrid approach to recommend learning materials that adapts to a student's language level. We evaluate our work with an online Japanese learning tool and the results suggest adding adaptivity into material recommendation significantly increases student engagement.
Engineering Kernelization for Maximum Cut
Venue: arXiv cs.DS 2019
Year: 2019

Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules. For the fundamental Max Cut problem, kernelization algorithms are theoretically highly efficient for various parameterizations. However, the efficacy of these reduction rules in practice---to aid solving highly challenging benchmark instances to optimality---remains entirely unexplored. We engineer a new suite of efficient data reduction rules that subsume most of the previously published rules, and demonstrate their significant impact on benchmark data sets, including synthetic instances, and data sets from the VLSI and image segmentation application domains. Our experiments reveal that current state-of-the-art solvers can be sped up by up to multiple orders of magnitude when combined with our data reduction rules. On social and biological networks in particular, kernelization enables us to solve four instances that were previously unsolved in a ten-hour time limit with state-of-the-art solvers; three of these instances are now solved in less than two seconds.
Non-Determinism in Neural Networks for Adversarial Robustness
Venue: arXiv cs.LG 2019
Year: 2019

Recent breakthroughs in the field of deep learning have led to advancements in a broad spectrum of tasks in computer vision, audio processing, natural language processing and other areas. In most instances where these tasks are deployed in real-world scenarios, the models used in them have been shown to be susceptible to adversarial attacks, making it imperative for us to address the challenge of their adversarial robustness. Existing techniques for adversarial robustness fall into three broad categories: defensive distillation techniques, adversarial training techniques, and randomized or non-deterministic model based techniques. In this paper, we propose a novel neural network paradigm that falls under the category of randomized models for adversarial robustness, but differs from all existing techniques under this category in that it models each parameter of the network as a statistical distribution with learnable parameters. We show experimentally that this framework is highly robust to a variety of white-box and black-box adversarial attacks, while preserving the task-specific performance of the traditional neural network model.
Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability
Venue: arXiv cs.LG 2019
Year: 2019

Data-driven approaches which aim to identify and predict player engagement are becoming increasingly popular in games industry contexts. This is due to the growing practice of tracking and storing large volumes of in-game telemetries coupled with a desire to tailor the gaming experience to the end-user's needs. These approaches are particularly useful not just for companies adopting Game-as-a-Service (GaaS) models (e.g. for re-engagement strategies) but also for those working under persistent content-delivery regimes (e.g. for better audience targeting). A major challenge for the latter is to build engagement models of the user which are data-efficient, holistic and can generalize across multiple game titles and genres with minimal adjustments. This work leverages a theoretical framework rooted in engagement and behavioural science research for building a model able to estimate engagement-related behaviours employing only a minimal set of game-agnostic metrics. Through a series of experiments we show how, by modelling early user-game interactions, this approach can make joint estimates of long-term survival time and churn probability across several single-player games in a range of genres. The model proposed is very suitable for industry applications since it relies on a minimal set of metrics and observations, scales well with the number of users and is explicitly designed to work across a diverse range of titles.
Levenshtein Transformer
Venue: arXiv cs.CL 2019
Year: 2019

Modern neural sequence generation models are built to either generate tokens step-by-step from scratch or (iteratively) modify a sequence of tokens bounded by a fixed length. In this work, we develop Levenshtein Transformer, a new partially autoregressive model devised for more flexible and amenable sequence generation. Unlike previous approaches, the atomic operations of our model are insertion and deletion. The combination of them facilitates not only generation but also sequence refinement allowing dynamic length changes. We also propose a set of new training techniques dedicated at them, effectively exploiting one as the other's learning signal thanks to their complementary nature. Experiments applying the proposed model achieve comparable performance but much-improved efficiency on both generation (e.g. machine translation, text summarization) and refinement tasks (e.g. automatic post-editing). We further confirm the flexibility of our model by showing a Levenshtein Transformer trained by machine translation can straightforwardly be used for automatic post-editing.
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
Venue: arXiv cs.SI 2019
Year: 2019

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness.
A Benchmark of Data Loss Bugs for Android Apps
Venue: arXiv cs.SE 2019
Year: 2019

Android apps must be able to deal with both stop events, which require immediately stopping the execution of the app without losing state information, and start events, which require resuming the execution of the app at the same point it was stopped. Support to these kinds of events must be explicitly implemented by developers who unfortunately often fail to implement the proper logic for saving and restoring the state of an app. As a consequence apps can lose data when moved to background and then back to foreground (e.g., to answer a call) or when the screen is simply rotated. These faults can be the cause of annoying usability issues and unexpected crashes. This paper presents a public benchmark of 110 data loss faults in Android apps that we systematically collected to facilitate research and experimentation with these problems. The benchmark is available on GitLab and includes the faulty apps, the fixed apps (when available), the test cases to automatically reproduce the problems, and additional information that may help researchers in their tasks.
Enhancing Block-Wise Transfer with Network Coding in CoAP
Venue: arXiv cs.NI 2019
Year: 2019

CoAP (Constrained Application Protocol) with block-wise transfer (BWT) option is a known protocol choice for large data transfer in general lossy IoT network environments. Lossy transmission environments on the other hand lead to CoAP resending multiple blocks, which creates overheads. To tackle this problem, we design a BWT with network coding (NC), with the goal to reducing the number of unnecessary retransmissions. The results show the reduction in the number of block retransmissions for different values of blocksize, implying the reduced transfer time. For the maximum blocksize of 1024 bytes and total probability loss of 0.5, CoAP with NC can resend up to 5 times less blocks.