OpenRaiser/Intern-Atlas huggingface dataset:
IoT-Flock: An Open-source Framework for IoT Traffic Generation
Venue: arXiv cs.NI 2020
Year: 2020

Network traffic generation is one of the primary techniques that is used to design and analyze the performance of network security systems. However, due to the diversity of IoT networks in terms of devices, applications and protocols, the traditional network traffic generator tools are unable to generate the IoT specific protocols traffic. Hence, the traditional traffic generator tools cannot be used for designing and testing the performance of IoT-specific security solutions. In order to design an IoT-based traffic generation framework, two main challenges include IoT device modelling and generating the IoT normal and attack traffic simultaneously. Therefore, in this work, we propose an open-source framework for IoT traffic generation which supports the two widely used IoT application layer protocols, i.e., MQTT and CoAP. The proposed framework allows a user to create an IoT use case, add customized IoT devices into it and generate normal and malicious IoT traffic over a real-time network. Furthermore, we set up a real-time IoT smart home use case to manifest the applicability of the proposed framework for developing the security solutions for IoT smart home by emulating the real world IoT devices. The experimental results demonstrate that the proposed framework can be effectively used to develop better security solutions for IoT networks without physically deploying the real-time use case.
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
Venue: arXiv cs.CV 2020
Year: 2020

We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework. We aim to build a more accurate and thorough connection between image pixels and language semantics directly from image and sentence pairs instead of using region-based image features as the most recent vision and language tasks. Our Pixel-BERT which aligns semantic connection in pixel and text level solves the limitation of task-specific visual representation for vision and language tasks. It also relieves the cost of bounding box annotations and overcomes the unbalance between semantic labels in visual task and language semantic. To provide a better representation for down-stream tasks, we pre-train a universal end-to-end model with image and sentence pairs from Visual Genome dataset and MS-COCO dataset. We propose to use a random pixel sampling mechanism to enhance the robustness of visual representation and to apply the Masked Language Model and Image-Text Matching as pre-training tasks. Extensive experiments on downstream tasks with our pre-trained model show that our approach makes the most state-of-the-arts in downstream tasks, including Visual Question Answering (VQA), image-text retrieval, Natural Language for Visual Reasoning for Real (NLVR). Particularly, we boost the performance of a single model in VQA task by 2.17 points compared with SOTA under fair comparison.
CORSICA: Cross-Origin Web Service Identification
Venue: arXiv cs.CR 2020
Year: 2020

Vulnerabilities in private networks are difficult to detect for attackers outside of the network. While there are known methods for port scanning internal hosts that work by luring unwitting internal users to an external web page that hosts malicious JavaScript code, no such method for detailed and precise service identification is known. The reason is that the Same Origin Policy (SOP) prevents access to HTTP responses of other origins by default. We perform a structured analysis of loopholes in the SOP that can be used to identify web applications across network boundaries. For this, we analyze HTML5, CSS, and JavaScript features of standard-compliant web browsers that may leak sensitive information about cross-origin content. The results reveal several novel techniques, including leaking JavaScript function names or styles of cross-origin requests that are available in all common browsers. We implement and test these techniques in a tool called CORSICA. It can successfully identify 31 of 42 (74%) of web services running on different IoT devices as well as the version numbers of the four most widely used content management systems WordPress, Drupal, Joomla, and TYPO3. CORSICA can also determine the patch level on average down to three versions (WordPress), six versions (Drupal), two versions (Joomla), and four versions (TYPO3) with only ten requests on average. Furthermore, CORSICA is able to identify 48 WordPress plugins containing 65 vulnerabilities. Finally, we analyze mitigation strategies and show that the proposed but not yet implemented strategies Cross-Origin Resource Policy (CORP)} and Sec-Metadata would prevent our identification techniques.
Human-Guided Planner for Non-Prehensile Manipulation
Venue: arXiv cs.RO 2020
Year: 2020

We present a human-guided planner for non-prehensile manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning, however, the problem remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning approaches, but we investigate using them in conjunction with human-operator input. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.
Augmented Q Imitation Learning (AQIL)
Venue: arXiv cs.LG 2020
Year: 2020

The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback. Traditional deep reinforcement learning takes a significant time before the machine starts to converge to an optimal policy. This paper proposes Augmented Q-Imitation-Learning, a method by which deep reinforcement learning convergence can be accelerated by applying Q-imitation-learning as the initial training process in traditional Deep Q-learning.
The Covid19Impact Survey: Assessing the Pulse of the COVID-19 Pandemic in Spain via 24 questions
Venue: arXiv cs.CY 2020
Year: 2020

In this paper, we describe the results of analyzing a large-scale survey, called the Covid19Impact survey, to assess citizens feedback on four areas related to the COVID-19 pandemic in Spain: social contact behavior, financial impact, working situation and health status. A total of 24 questions cover the areas of demographics, their home situation, social contact behavior, personal economic impact, their workplace situation and their health. The survey was responded to by 156,614 participants between the evening of March 28th and April 2nd, 2020. Such a large response enables us to gain new insights, as well as an unprecedented glimpse at respondents personal experiences and concerns during the current COVID-19 pandemic. From the analysis, we draw several implications for the design of public policies related to the management of the COVID-19 pandemic.
Multi-Modal Video Forensic Platform for Investigating Post-Terrorist Attack Scenarios
Venue: arXiv cs.MM 2020
Year: 2020

The forensic investigation of a terrorist attack poses a significant challenge to the investigative authorities, as often several thousand hours of video footage must be viewed. Large scale Video Analytic Platforms (VAP) assist law enforcement agencies (LEA) in identifying suspects and securing evidence. Current platforms focus primarily on the integration of different computer vision methods and thus are restricted to a single modality. We present a video analytic platform that integrates visual and audio analytic modules and fuses information from surveillance cameras and video uploads from eyewitnesses. Videos are analyzed according their acoustic and visual content. Specifically, Audio Event Detection is applied to index the content according to attack-specific acoustic concepts. Audio similarity search is utilized to identify similar video sequences recorded from different perspectives. Visual object detection and tracking are used to index the content according to relevant concepts. Innovative user-interface concepts are introduced to harness the full potential of the heterogeneous results of the analytical modules, allowing investigators to more quickly follow-up on leads and eyewitness reports.
Analysis of Multi-Messages Retransmission Schemes
Venue: arXiv cs.IT 2020
Year: 2020

Hybrid Automatic ReQuest (HARQ) protocol enables reliable communications in wireless systems. Usually, several parallel streams are sent in successive timeslots following a time-sharing approach. Recently, multi-layer HARQ has been proposed by superposing packets within a timeslot. In this paper, we evaluate the potential of this multi-layer HARQ by playing with some design parameters. We show that a gain in throughput is only obtained at mid-Signal-to-Noise Ratio (SNR).
On Locating Paths in Compressed Tries
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we consider the problem of compressing a trie while supporting the powerful \emph{locate} queries: to return the pre-order identifiers of all nodes reached by a path labeled with a given query pattern. Our result builds on top of the XBWT tree transform of Ferragina et al. [FOCS 2005] and generalizes the \emph{r-index} locate machinery of Gagie et al. [SODA 2018, JACM 2020] based on the run-length encoded Burrows-Wheeler transform (BWT). Our first contribution is to propose a suitable generalization of the run-length BWT to tries. We show that this natural generalization enjoys several of the useful properties of its counterpart on strings: in particular, the transform natively supports counting occurrences of a query pattern on the trie's paths and its size $r$ captures the trie's repetitiveness and lower-bounds a natural notion of trie entropy. Our main contribution is a much deeper insight into the combinatorial structure of this object. In detail, we show that a data structure of $O(r\log n) + 2n + o(n)$ bits, where $n$ is the number of nodes, allows locating the $occ$ occurrences of a pattern of length $m$ in nearly-optimal $O(m\logσ+ occ)$ time, where $σ$ is the alphabet's size. Our solution consists in sampling $O(r)$ nodes that can be used as "anchor points" during the locate process. Once obtained the pre-order identifier of the first pattern occurrence (in co-lexicographic order), we show that a constant number of constant-time jumps between those anchor points lead to the identifier of the next pattern occurrence, thus enabling locating in optimal $O(1)$ time per occurrence.
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
Venue: arXiv cs.CV 2020
Year: 2020

These days, unsupervised super-resolution (SR) has been soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i.e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner. Unfortunately, since image translation itself is an extremely challenging task, the SR performance of these approaches are severely limited by the domain gap between generated synthetic LR images and real LR images. In this paper, we propose a novel domain-distance aware super-resolution (DASR) approach for unsupervised real-world image SR. The domain gap between training data (e.g. $\mathcal{Y}^g$) and testing data (e.g. $\mathcal{Y}^r$) is addressed with our \textbf{domain-gap aware training} and \textbf{domain-distance weighted supervision} strategies. Domain-gap aware training takes additional benefit from real data in the target domain while domain-distance weighted supervision brings forward the more rational use of labeled source domain data. The proposed method is validated on synthetic and real datasets and the experimental results show that DASR consistently outperforms state-of-the-art unsupervised SR approaches in generating SR outputs with more realistic and natural textures.
GraphChallenge.org Sparse Deep Neural Network Performance
Venue: arXiv cs.LG 2020
Year: 2020

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems. The sparse DNN challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment. In 2019 several sparse DNN challenge submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art sparse DNN execution time, $T_{\rm DNN}$, is a strong function of the number of DNN operations performed, $N_{\rm op}$. The sparse DNN challenge provides a clear picture of current sparse DNN systems and underscores the need for new innovations to achieve high performance on very large sparse DNNs.
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
Venue: arXiv cs.NE 2020
Year: 2020

The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically challenging domains such as autonomous driving or medical diagnostics. We address today's lack of transparency of neural networks and shed light on the roles of single neurons and groups of neurons within the network fulfilling a learned task. Inspired by research in the field of neuroscience, we characterize the learned representations by activation patterns and network ablations, revealing functional neuron populations that a) act jointly in response to specific stimuli or b) have similar impact on the network's performance after being ablated. We find that neither a neuron's magnitude or selectivity of activation, nor its impact on network performance are sufficient stand-alone indicators for its importance for the overall task. We argue that such indicators are essential for future advances in transfer learning and modern neuroscience.
Data-Driven Path Selection for Real-Time Video Streaming at the Network Edge
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we present a framework for the dynamic selection of the wireless channels used to deliver information-rich data streams to edge servers. The approach we propose is data-driven, where a predictor, whose output informs the decision making of the channel selector, is built from available data on the transformation imposed by the network on previously transmitted packets. The proposed technique is contextualized to real-time video streaming for immediate processing. The core of our framework is the notion of probes, that is, short bursts of packets transmitted over unused channels to acquire information while generating a controlled impact on other active links. Results indicate a high accuracy of the prediction output and a significant improvement in terms of received video quality when the prediction output is used to dynamically select the used channel for transmission.
Efficient UAV Physical Layer Security based on Deep Learning and Artificial Noise
Venue: arXiv cs.CR 2020
Year: 2020

Network-connected unmanned aerial vehicle (UAV) communications is a common solution to achieve high-rate image transmission. The broadcast nature of these wireless networks makes this communication vulnerable to eavesdropping. This paper considers the problem of compressed secret image transmission between two nodes, in the presence of a passive eavesdropper. In this paper, we use auto encoder/decoder convolutional neural networks, which by using deep learning algorithms, allow us to compress/decompress images. Also we use network physical layer features to generate high rate artificial noise to secure the data. Using features of the channel with applying artificial noises, reduce the channel capacity of the unauthorized users and prevent eavesdropper from detecting received data. Our simulation experiments show that for received data with SNR fewer than 5 in the authorized node, the MSE is less than 0.05.
Learning Pose-invariant 3D Object Reconstruction from Single-view Images
Venue: arXiv cs.CV 2020
Year: 2020

Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data. However, most work in this direction requires multi-view images for each object instance as training supervision, which oftentimes does not apply in practice. In this paper, we relax the common multi-view assumption and explore a more challenging yet more realistic setup of learning 3D shape from only single-view images. The major difficulty lies in insufficient constraints that can be provided by single view images, which leads to the problem of pose entanglement in learned shape space. As a result, reconstructed shapes vary along input pose and have poor accuracy. We address this problem by taking a novel domain adaptation perspective, and propose an effective adversarial domain confusion method to learn pose-disentangled compact shape space. Experiments on single-view reconstruction show effectiveness in solving pose entanglement, and the proposed method achieves on-par reconstruction accuracy with state-of-the-art with higher efficiency.
Computational Complexity of the Hylland-Zeckhauser Scheme for One-Sided Matching Markets
Venue: arXiv cs.GT 2020
Year: 2020

In 1979, Hylland and Zeckhauser \cite{hylland} gave a simple and general scheme for implementing a one-sided matching market using the power of a pricing mechanism. Their method has nice properties -- it is incentive compatible in the large and produces an allocation that is Pareto optimal -- and hence it provides an attractive, off-the-shelf method for running an application involving such a market. With matching markets becoming ever more prevalant and impactful, it is imperative to finally settle the computational complexity of this scheme. We present the following partial resolution: 1. A combinatorial, strongly polynomial time algorithm for the special case of $0/1$ utilities. 2. An example that has only irrational equilibria, hence proving that this problem is not in PPAD. Furthermore, its equilibria are disconnected, hence showing that the problem does not admit a convex programming formulation. 3. A proof of membership of the problem in the class FIXP. We leave open the (difficult) question of determining if the problem is FIXP-hard. Settling the status of the special case when utilities are in the set $\{0, {\frac 1 2}, 1 \}$ appears to be even more difficult.
Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles
Venue: arXiv cs.CV 2020
Year: 2020

Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the entire system as a whole. Because of this, they are limited to utilizing a task-specific set of features for all possible tasks of inference-time, which ignores the capability to leverage common task-invariant contextual knowledge for the task at hand. To address this problem, we propose an algorithm to jointly learn the task-specific and shared representations by adopting a multi-task learning network. Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly. The learned representations capture richer contextual information without additional task-specific network. Extensive experiments on the large-scale dataset HSD demonstrate the effectiveness and superiority of our network over state-of-the-art methods.
On Delay-limited Average Rate of HARQ-based Predictor Antenna Systems
Venue: arXiv cs.IT 2020
Year: 2020

Predictor antenna (PA) system is referred to as a system with two sets of antennas on the roof of a vehicle, where the PAs positioned in the front of the vehicle are used to predict the channel state observed by the receive antennas (RAs) that are aligned behind the PAs. In this work, we study the effect of spatial mismatch on the accuracy of channel state information estimation, and analyze the delay-constrained average rate of hybrid automatic repeat request (HARQ)-based PA systems. We consider variable-length incremental redundancy (INR) HARQ protocol, and derive a closed-form expression for the maximum average rate subject to a maximum delay constraint. Moreover, we study the effect of different parameters such as the vehicle speed, the antenna distance and the rate allocation on the system performance. The results indicate that, compared to the open-loop case, the delay-limited average rate is considerably improved with our proposed PA-HARQ scheme.
Testing the Usability and Accessibility of Smart TV Applications Using an Automated Model-based Approach
Venue: arXiv cs.SE 2020
Year: 2020

As the popularity of Smart Televisions (TVs) and interactive Smart TV applications (apps) has recently grown, the usability of these apps has become an important quality characteristic. Previous studies examined Smart TV apps from a usability perspective. However, these methods are mainly manual, and the potential of automated model-based testing methods for usability testing purposes has not yet been fully explored. In this paper, we propose an approach to test the usability of Smart TV apps based on the automated generation of a Smart TV user interaction model from an existing app by a specialized automated crawler. By means of this model, defined user tasks in the Smart TV app can be evaluated automatically in terms of their feasibility and estimated user effort, which reflects the usability of the analyzed app. This analysis can be applied in the context of regular users and users with various specific needs. The findings from this model-based automated analysis approach can be used to optimize the user interface of a Smart TV app to increase its usability, accessibility, and quality.