OpenRaiser/Intern-Atlas huggingface dataset:
AnimGAN: A Spatiotemporally-Conditioned Generative Adversarial Network for Character Animation
Venue: arXiv cs.CV 2020
Year: 2020

Producing realistic character animations is one of the essential tasks in human-AI interactions. Considered as a sequence of poses of a humanoid, the task can be considered as a sequence generation problem with spatiotemporal smoothness and realism constraints. Additionally, we wish to control the behavior of AI agents by giving them what to do and, more specifically, how to do it. We proposed a spatiotemporally-conditioned GAN that generates a sequence that is similar to a given sequence in terms of semantics and spatiotemporal dynamics. Using LSTM-based generator and graph ConvNet discriminator, this system is trained end-to-end on a large gathered dataset of gestures, expressions, and actions. Experiments showed that compared to traditional conditional GAN, our method creates plausible, realistic, and semantically relevant humanoid animation sequences that match user expectations.
Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations
Venue: arXiv cs.SE 2020
Year: 2020

This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzing. Pythia uses a statistical model to learn common usage patterns of a target REST API from structurally valid seed inputs. It then generates learning-based mutations by injecting a small amount of noise deviating from common usage patterns while still maintaining syntactic validity. Pythia's mutation strategy helps generate grammatically valid test cases and coverage-guided feedback helps prioritize the test cases that are more likely to find bugs. We present experimental evaluation on three production-scale, open-source cloud services showing that Pythia outperforms prior approaches both in code coverage and new bugs found. Using Pythia, we found 29 new bugs which we are in the process of reporting to the respective service owners.
All downhill from the PhD? The typical impact trajectory of US academic careers
Venue: arXiv cs.DL 2020
Year: 2020

Within academia, mature researchers tend to be more senior, but do they also tend to write higher impact articles? This article assesses long-term publishing (16+ years) United States (US) researchers, contrasting them with shorter-term publishing researchers (1, 6 or 10 years). A long-term US researcher is operationalised as having a first Scopus-indexed journal article in exactly 2001 and one in 2016-2019, with US main affiliations in their first and last articles. Researchers publishing in large teams (11+ authors) were excluded. The average field and year normalised citation impact of long- and shorter-term US researchers' journal articles decreases over time relative to the national average, with especially large falls to the last articles published that may be at least partly due to a decline in self-citations. In many cases researchers start by publishing above US average citation impact research and end by publishing below US average citation impact research. Thus, research managers should not assume that senior researchers will usually write the highest impact papers.
Self-supervised Robust Object Detectors from Partially Labelled Datasets
Venue: arXiv cs.CV 2020
Year: 2020

In the object detection task, merging various datasets from similar contexts but with different sets of Objects of Interest (OoI) is an inexpensive way (in terms of labor cost) for crafting a large-scale dataset covering a wide range of objects. Moreover, merging datasets allows us to train one integrated object detector, instead of training several ones, which in turn resulting in the reduction of computational and time costs. However, merging the datasets from similar contexts causes samples with partial labeling as each constituent dataset is originally annotated for its own set of OoI and ignores to annotate those objects that are become interested after merging the datasets. With the goal of training \emph{one integrated robust object detector with high generalization performance}, we propose a training framework to overcome missing-label challenge of the merged datasets. More specifically, we propose a computationally efficient self-supervised framework to create on-the-fly pseudo-labels for the unlabeled positive instances in the merged dataset in order to train the object detector jointly on both ground truth and pseudo labels. We evaluate our proposed framework for training Yolo on a simulated merged dataset with missing rate $\approx\!48\%$ using VOC2012 and VOC2007. We empirically show that generalization performance of Yolo trained on both ground truth and the pseudo-labels created by our method is on average $4\%$ higher than the ones trained only with the ground truth labels of the merged dataset.
ChirpOTLE: A Framework for Practical LoRaWAN Security Evaluation
Venue: arXiv cs.CR 2020
Year: 2020

Low-power wide-area networks (LPWANs) are becoming an integral part of the Internet of Things. As a consequence, businesses, administration, and, subsequently, society itself depend on the reliability and availability of these communication networks. Released in 2015, LoRaWAN gained popularity and attracted the focus of security research, revealing a number of vulnerabilities. This lead to the revised LoRaWAN 1.1 specification in late 2017. Most of previous work focused on simulation and theoretical approaches. Interoperability and the variety of implementations complicate the risk assessment for a specific LoRaWAN network. In this paper, we address these issues by introducing ChirpOTLE, a LoRa and LoRaWAN security evaluation framework suitable for rapid iteration and testing of attacks in testbeds and assessing the security of real-world networks.We demonstrate the potential of our framework by verifying the applicability of a novel denial-of-service attack targeting the adaptive data rate mechanism in a testbed using common off-the-shelf hardware. Furthermore, we show the feasibility of the Class B beacon spoofing attack, which has not been demonstrated in practice before.
Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently. In this paper, we revisit this problem and point out the ignored issue about image alignment information. We show that the performance of a simple Siamese network is highly dependent on the alignment setting and the comparison of previous works can be unfair if they have different assumptions. Instead of focusing on the feature extraction under the alignment assumption, we show that improvements in metric learning techniques significantly boost the performance regardless of the alignment. Without leveraging the alignment information, our pipeline outperforms previous works on both panorama and cropped datasets. Furthermore, we conduct visualization to help understand the learned model and the effect of alignment information using Grad-CAM. With our discovery on the approximate rotation-invariant activation maps, we propose a novel method to estimate the orientation/alignment between a pair of cross-view images with unknown alignment information. It achieves state-of-the-art results on the CVUSA dataset.
Benchmarking and Performance Modelling of MapReduce Communication Pattern
Venue: arXiv cs.DC 2020
Year: 2020

Understanding and predicting the performance of big data applications running in the cloud or on-premises could help minimise the overall cost of operations and provide opportunities in efforts to identify performance bottlenecks. The complexity of the low-level internals of big data frameworks and the ubiquity of application and workload configuration parameters makes it challenging and expensive to come up with comprehensive performance modelling solutions. In this paper, instead of focusing on a wide range of configurable parameters, we studied the low-level internals of the MapReduce communication pattern and used a minimal set of performance drivers to develop a set of phase level parametric models for approximating the execution time of a given application on a given cluster. Models can be used to infer the performance of unseen applications and approximate their performance when an arbitrary dataset is used as input. Our approach is validated by running empirical experiments in two setups. On average the error rate in both setups is plus or minus 10% from the measured values.
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
Venue: arXiv cs.CV 2020
Year: 2020

Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. Our fully-convolutional neural network directly regresses the angle of each bounding box using a periodic loss function, which accounts for angle periodicities. We have also created a new dataset with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets. Code and dataset are available at http://vip.bu.edu/rapid .
A Note on the Concrete Hardness of the Shortest Independent Vectors Problem in Lattices
Venue: arXiv cs.CC 2020
Year: 2020

Blömer and Seifert showed that $\mathsf{SIVP}_2$ is NP-hard to approximate by giving a reduction from $\mathsf{CVP}_2$ to $\mathsf{SIVP}_2$ for constant approximation factors as long as the $\mathsf{CVP}$ instance has a certain property. In order to formally define this requirement on the $\mathsf{CVP}$ instance, we introduce a new computational problem called the Gap Closest Vector Problem with Bounded Minima. We adapt the proof of Blömer and Seifert to show a reduction from the Gap Closest Vector Problem with Bounded Minima to $\mathsf{SIVP}$ for any $\ell_p$ norm for some constant approximation factor greater than $1$. In a recent result, Bennett, Golovnev and Stephens-Davidowitz showed that under Gap-ETH, there is no $2^{o(n)}$-time algorithm for approximating $\mathsf{CVP}_p$ up to some constant factor $γ\geq 1$ for any $1 \leq p \leq \infty$. We observe that the reduction in their paper can be viewed as a reduction from $\mathsf{Gap3SAT}$ to the Gap Closest Vector Problem with Bounded Minima. This, together with the above mentioned reduction, implies that, under Gap-ETH, there is no $2^{o(n)}$-time algorithm for approximating $\mathsf{SIVP}_p$ up to some constant factor $γ\geq 1$ for any $1 \leq p \leq \infty$.
A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation
Venue: arXiv cs.CL 2020
Year: 2020

The answer-agnostic question generation is a significant and challenging task, which aims to automatically generate questions for a given sentence but without an answer. In this paper, we propose two new strategies to deal with this task: question type prediction and copy loss mechanism. The question type module is to predict the types of questions that should be asked, which allows our model to generate multiple types of questions for the same source sentence. The new copy loss enhances the original copy mechanism to make sure that every important word in the source sentence has been copied when generating questions. Our integrated model outperforms the state-of-the-art approach in answer-agnostic question generation, achieving a BLEU-4 score of 13.9 on SQuAD. Human evaluation further validates the high quality of our generated questions. We will make our code public available for further research.
DeepSQLi: Deep Semantic Learning for Testing SQL Injection
Venue: arXiv cs.SE 2020
Year: 2020

Security is unarguably the most serious concern for Web applications, to which SQL injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi vulnerabilities is of ultimate importance, yet is unfortunately far from trivial to implement. This is because the existence of a huge, or potentially infinite, number of variants and semantic possibilities of SQL leading to SQLi attacks on various Web applications. In this paper, we propose a deep natural language processing based tool, dubbed DeepSQLi, to generate test cases for detecting SQLi vulnerabilities. Through adopting deep learning based neural language model and sequence of words prediction, DeepSQLi is equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs (or a test case) into a new test case, which is semantically related and potentially more sophisticated. Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art SQLi testing automation tool, on six real-world Web applications that are of different scales, characteristics and domains. Empirical results demonstrate the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such that more SQLi vulnerabilities can be identified by using a less number of test cases, whilst running much faster.
A Note on Self-Dual Generalized Reed-Solomon Codes
Venue: arXiv cs.IT 2020
Year: 2020

A linear code is called an MDS self-dual code if it is both an MDS code and a self-dual code with respect to the Euclidean inner product. The parameters of such codes are completely determined by the code length. In this paper, we consider new constructions of MDS self-dual codes via generalized Reed-Solomon (GRS) codes and their extended codes. The critical idea of our constructions is to choose suitable evaluation points such that the corresponding (extended) GRS codes are self-dual. The evaluation set of our constructions is consists of a subgroup of finite fields and its cosets in a bigger subgroup. Four new families of MDS self-dual codes are obtained and they have better parameters than previous results in certain region. Moreover, by the Mobius action over finite fields, we give a systematic way to construct self-dual GRS codes with different evaluation points provided any known self-dual GRS codes. Specially, we prove that all the self-dual extended GRS codes over $\mathbb{F}_{q}$ with length $n< q+1$ can be constructed from GRS codes with the same parameters.
Domain Specific, Semi-Supervised Transfer Learning for Medical Imaging
Venue: arXiv cs.CV 2020
Year: 2020

Limited availability of annotated medical imaging data poses a challenge for deep learning algorithms. Although transfer learning minimizes this hurdle in general, knowledge transfer across disparate domains is shown to be less effective. On the other hand, smaller architectures were found to be more compelling in learning better features. Consequently, we propose a lightweight architecture that uses mixed asymmetric kernels (MAKNet) to reduce the number of parameters significantly. Additionally, we train the proposed architecture using semi-supervised learning to provide pseudo-labels for a large medical dataset to assist with transfer learning. The proposed MAKNet provides better classification performance with $60 - 70\%$ less parameters than popular architectures. Experimental results also highlight the importance of domain-specific knowledge for effective transfer learning.
Req2Lib: A Semantic Neural Model for Software Library Recommendation
Venue: arXiv cs.SE 2020
Year: 2020

Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.
Vision-based control of a knuckle boom crane with online cable length estimation
Venue: arXiv cs.RO 2020
Year: 2020

A vision-based controller for a knuckle boom crane is presented. The controller is used to control the motion of the crane tip and at the same time compensate for payload oscillations. The oscillations of the payload are measured with three cameras that are fixed to the crane king and are used to track two spherical markers fixed to the payload cable. Based on color and size information, each camera identifies the image points corresponding to the markers. The payload angles are then determined using linear triangulation of the image points. An extended Kalman filter is used for estimation of payload angles and angular velocity. The length of the payload cable is also estimated using a least squares technique with projection. The crane is controlled by a linear cascade controller where the inner control loop is designed to damp out the pendulum oscillation, and the crane tip is controlled by the outer loop. The control variable of the controller is the commanded crane tip acceleration, which is converted to a velocity command using a velocity loop. The performance of the control system is studied experimentally using a scaled laboratory version of a knuckle boom crane.
Learning visual servo policies via planner cloning
Venue: arXiv cs.RO 2020
Year: 2020

Learning control policies for visual servoing in novel environments is an important problem. However, standard model-free policy learning methods are slow. This paper explores planner cloning: using behavior cloning to learn policies that mimic the behavior of a full-state motion planner in simulation. We propose Penalized Q Cloning (PQC), a new behavior cloning algorithm. We show that it outperforms several baselines and ablations on some challenging problems involving visual servoing in novel environments while avoiding obstacles. Finally, we demonstrate that these policies can be transferred effectively onto a real robotic platform, achieving approximately an 87% success rate both in simulation and on a real robot.
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Université), LIG (Université Grenoble Alpes), and LIUM (Le Mans Université). Attention-based encoder-decoder models, trained end-to-end, were used for our submissions to the offline speech translation track. Our contributions focused on data augmentation and ensembling of multiple models. In the simultaneous speech translation track, we build on Transformer-based wait-k models for the text-to-text subtask. For speech-to-text simultaneous translation, we attach a wait-k MT system to a hybrid ASR system. We propose an algorithm to control the latency of the ASR+MT cascade and achieve a good latency-quality trade-off on both subtasks.
Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation
Venue: arXiv cs.HC 2020
Year: 2020

Recent WiFi-based fall detection systems have drawn much attention due to their advantages over other sensory systems. Various implementations have achieved impressive progress in performance, thanks to machine learning and deep learning techniques. However, many of such high accuracy systems have low reliability as they fail to achieve robustness in unseen environments. To address that, this paper investigates a method of generalization through adversarial data augmentation. Our results show a slight improvement in deep learning-systems in unseen domains, though the performance is not significant.
Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting
Venue: arXiv cs.CV 2020
Year: 2020

Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images. These challenges are typically addressed using multi-column structures in existing methods. However, such an approach does not provide consistent improvement and transferability due to limited ability in capturing multi-scale features, sensitiveness to large density shift, and difficulty in training multi-branch models. To overcome these limitations, a Single-column Scale-invariant Network (ScSiNet) is presented in this paper, which extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT). Furthermore, in order to enlarge the diversity of densities, a randomly integrated loss is presented for training our single-branch method. Extensive experiments on public datasets demonstrate that the proposed method consistently outperforms state-of-the-art approaches in counting accuracy and achieves remarkable transferability and scale-invariant property.
Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contact-tracing apps
Venue: arXiv cs.HC 2020
Year: 2020

Contact-tracing apps have potential benefits in helping health authorities to act swiftly to halt the spread of COVID-19. However, their effectiveness is heavily dependent on their installation rate, which may be influenced by people's perceptions of the utility of these apps and any potential privacy risks due to the collection and releasing of sensitive user data (e.g., user identity and location). In this paper, we present a survey study that examined people's willingness to install six different contact-tracing apps after informing them of the risks and benefits of each design option (with a U.S.-only sample on Amazon Mechanical Turk, $N=208$). The six app designs covered two major design dimensions (centralized vs decentralized, basic contact tracing vs. also providing hotspot information), grounded in our analysis of existing contact-tracing app proposals. Contrary to assumptions of some prior work, we found that the majority of people in our sample preferred to install apps that use a centralized server for contact tracing, as they are more willing to allow a centralized authority to access the identity of app users rather than allowing tech-savvy users to infer the identity of diagnosed users. We also found that the majority of our sample preferred to install apps that share diagnosed users' recent locations in public places to show hotspots of infection. Our results suggest that apps using a centralized architecture with strong security protection to do basic contact tracing and providing users with other useful information such as hotspots of infection in public places may achieve a high adoption rate in the U.S.