OpenRaiser/Intern-Atlas huggingface dataset:
$2$-Layer $k$-Planar Graphs: Density, Crossing Lemma, Relationships, and Pathwidth
Venue: arXiv cs.DM 2020
Year: 2020

The $2$-layer drawing model is a well-established paradigm to visualize bipartite graphs. Several beyond-planar graph classes have been studied under this model. Surprisingly, however, the fundamental class of $k$-planar graphs has been considered only for $k=1$ in this context. We provide several contributions that address this gap in the literature. First, we show tight density bounds for the classes of $2$-layer $k$-planar graphs with $k\in\{2,3,4,5\}$. Based on these results, we provide a Crossing Lemma for $2$-layer $k$-planar graphs, which then implies a general density bound for $2$-layer $k$-planar graphs. We prove this bound to be almost optimal with a corresponding lower bound construction. Finally, we study relationships between $k$-planarity and $h$-quasiplanarity in the $2$-layer model and show that $2$-layer $k$-planar graphs have pathwidth at most $k+1$.
An Edge-powered Approach to Assisted Driving
Venue: arXiv cs.NI 2020
Year: 2020

Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.
IoT Network Security: Requirements, Threats, and Countermeasures
Venue: arXiv cs.CR 2020
Year: 2020

IoT devices are increasingly utilized in critical infrastructure, enterprises, and households. There are several sophisticated cyber-attacks that have been reported and many networks have proven vulnerable to both active and passive attacks by leaking private information, allowing unauthorized access, and being open to denial of service attacks. This paper aims firstly, to assist network operators to understand the need for an IoT network security solution, and then secondly, to survey IoT network attack vectors, cyber threats, and countermeasures with a focus on improving the robustness of existing security solutions. Our first contribution highlights viewpoints on IoT security from the perspective of stakeholders such as manufacturers, service providers, consumers, and authorities. We discuss the differences between IoT and IT systems, the need for IoT security solutions, and we highlight the key components required for IoT network security system architecture. For our second contribution, we survey the types of IoT attacks by grouping them based on their impact. We discuss various attack techniques, threats, and shortfalls of existing countermeasures with an intention to enable future research into improving IoT network security.
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
Venue: arXiv cs.CV 2020
Year: 2020

Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of motion fields applies model-based interpolation that makes use of edge information extracted from the target image. For depth completion, data-driven learning approaches are widespread. Our work is inspired by latest trends in depth completion that tackle the problem of dense guidance for sparse information. We extend these ideas and create a generic cross-domain architecture that can be applied for a multitude of interpolation problems like optical flow, scene flow, or depth completion. In our experiments, we show that our proposed concept of Sparse Spatial Guided Propagation (SSGP) achieves improvements to robustness, accuracy, or speed compared to specialized algorithms.
BlindSignedID: Mitigating Denial-of-Service Attacks on Digital Contact Tracing
Venue: arXiv cs.CR 2020
Year: 2020

Due to the recent outbreak of COVID-19, many governments suspended outdoor activities and imposed social distancing policies to prevent the transmission of SARS-CoV-2. These measures have had severe impact on the economy and peoples' daily lives. An alternative to widespread lockdowns is effective contact tracing during an outbreak's early stage. However, mathematical models suggest that epidemic control for SARS-CoV-2 transmission with manual contact tracing is implausible. To reduce the effort of contact tracing, many digital contact tracing projects (e.g., PEPP-PT, DP-3T, TCN, BlueTrace, Google/Apple Exposure Notification, and East/West Coast PACT) are being developed to supplement manual contact tracing. However, digital contact tracing has drawn scrutiny from privacy advocates, since governments or other parties may attempt to use contact tracing protocols for mass surveillance. As a result, many digital contact tracing projects build privacy-preserving mechanisms to limit the amount of privacy-sensitive information leaked by the protocol. In this paper, we examine how these architectures resist certain classes of attacks, specifically DoS attacks, and present BlindSignedIDs, a privacy-preserving digital contact tracing mechanism, which are verifiable ephemeral identifiers to limit the effectiveness of MAC-compliant DoS attacks. In our evaluations, we showed BlindSignedID can effectively deny bogus EphIDs, mitigating DoS attacks on the local storage beyond 90% of stored EphIDs. Our example DoS attacks showed that using 4 attackers can cause the gigabyte level DoS attacks within normal working hours and days.
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Venue: arXiv cs.LG 2020
Year: 2020

Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limited screen size of mobile devices, positions of items have a significant influence on clicks: 1) Higher positions lead to more clicks for one commodity. 2) The 'pseudo-exposure' issue: Only a few recommended items are shown at first glance and users need to slide the screen to browse other items. Therefore, some recommended items ranked behind are not viewed by users and it is not proper to treat this kind of items as negative samples. While many works model the online recommendation as contextual bandit problems, they rarely take the influence of positions into consideration and thus the estimation of the reward function may be biased. In this paper, we aim at addressing these two issues to improve the performance of online mobile recommendation. Our contributions are four-fold. First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set. Second, we propose a novel contextual combinatorial bandit method called UBM-LinUCB to address two issues related to positions by adopting the User Browsing Model (UBM), a click model for web search. Third, we provide a formal regret analysis and prove that our algorithm achieves sublinear regret independent of the number of items. Finally, we evaluate our algorithm on two real-world datasets by a novel unbiased estimator. An online experiment is also implemented in Taobao, one of the most popular e-commerce platforms in the world. Results on two CTR metrics show that our algorithm outperforms the other contextual bandit algorithms.
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Venue: arXiv cs.LG 2020
Year: 2020

With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items with different properties such as huge discounts. A web page often consists of different independent modules. The ranking policies of these modules are decided by different teams and optimized individually without cooperation, which might result in competition between modules. Thus, the global policy of the whole page could be sub-optimal. In this paper, we propose a novel multi-agent cooperative reinforcement learning approach with the restriction that different modules cannot communicate. Our contributions are three-fold. Firstly, inspired by a solution concept in game theory named correlated equilibrium, we design a signal network to promote cooperation of all modules by generating signals (vectors) for different modules. Secondly, an entropy-regularized version of the signal network is proposed to coordinate agents' exploration of the optimal global policy. Furthermore, experiments based on real-world e-commerce data demonstrate that our algorithm obtains superior performance over baselines.
Balancing freedoms, rights and responsibilities during COVID in US: a study of anti- and pro-restriction discourse
Venue: arXiv cs.CY 2020
Year: 2020

Countries across the world have instituted unprecedented restrictions on freedom of movement, privacy and individual rights to control the spread of COVID-19. These measures tend to have been derived from communally orientated East Asian cultures. The way that culturally relevant concepts of rights and freedoms underpin COVID restrictions in democratic and individually orientated countries remains unknown. This data memo addresses this issue through analysis of pro- and anti-restriction discourse on social media in the US. It finds that anti-social and economic restriction discourse more frequently articulates rights and freedoms, based on ideas of inviolable rights to freedom of movement or freedom of economic activity or a cost-benefit analysis that places economic activity over public health. Pro-social and economic restriction discourse less frequently mentions rights and freedoms, instead supporting restrictions as following state and medical advice and out of deference and respect to medical professionals. Discourse is highly polarised and divisive and articulated largely through established political identity positions. It is suggested that more attention is paid to discussions of balancing rights and freedoms in COVID control restrictions. To convince opposers of restrictions, supporters of restrictions should base arguments around communal rights and positive freedoms. It is also important to critically evaluate whether and how these perspectives need to be adapted to be appropriate and resonant in democratic and individualistic countries.
An Improved Person Re-identification Method by light-weight convolutional neural network
Venue: arXiv cs.CV 2020
Year: 2020

Person Re-identification is defined as a recognizing process where the person is observed by non-overlapping cameras at different places. In the last decade, the rise in the applications and importance of Person Re-identification for surveillance systems popularized this subject in different areas of computer vision. Person Re-identification is faced with challenges such as low resolution, varying poses, illumination, background clutter, and occlusion, which could affect the result of recognizing process. The present paper aims to improve Person Re-identification using transfer learning and application of verification loss function within the framework of Siamese network. The Siamese network receives image pairs as inputs and extract their features via a pre-trained model. EfficientNet was employed to obtain discriminative features and reduce the demands for data. The advantages of verification loss were used in the network learning. Experiments showed that the proposed model performs better than state-of-the-art methods on the CUHK01 dataset. For example, rank5 accuracies are 95.2% (+5.7) for the CUHK01 datasets. It also achieved an acceptable percentage in Rank 1. Because of the small size of the pre-trained model parameters, learning speeds up and there will be a need for less hardware and data.
Metamodel Quality Requirements and Evaluation (MQuaRE)
Venue: arXiv cs.SE 2020
Year: 2020

Models are the primary artifacts of model-driven software engineering (MDSD) [1], and a terminal model is a representation that conforms to a given software metamodel [2, 3]. As the quality of a software metamodel directly impacts the quality of terminal models, software metamodel quality is an essential aspect of MDSD. However, the literature reports a few proposals for metamodel quality evaluation, but most lack a general solution for the quality issue. Some efforts focus on quality measures [4], a quality evaluation model [5], or a quality evaluation model with structural measures borrowed from OO design [6, 7, 8]. Thus, we support there is a need for a more thorough solution for metamodel quality evaluation, with potential benefits to MDSD in general. This document describes a metamodel quality evaluation framework called MQuaRE (Metamodel Quality Requirements and Evaluation). MQuaRE is an integrated framework composed of metamodel quality requirements, a metamodel quality model, metamodel quality measures, and an evaluation process, with a great contribution of the ISO/IEC 25000 series [9] for software product quality evaluation.
RespVAD: Voice Activity Detection via Video-Extracted Respiration Patterns
Venue: arXiv cs.LG 2020
Year: 2020

Voice Activity Detection (VAD) refers to the task of identification of regions of human speech in digital signals such as audio and video. While VAD is a necessary first step in many speech processing systems, it poses challenges when there are high levels of ambient noise during the audio recording. To improve the performance of VAD in such conditions, several methods utilizing the visual information extracted from the region surrounding the mouth/lip region of the speakers' video recording have been proposed. Even though these provide advantages over audio-only methods, they depend on faithful extraction of lip/mouth regions. Motivated by these, a new paradigm for VAD based on the fact that respiration forms the primary source of energy for speech production is proposed. Specifically, an audio-independent VAD technique using the respiration pattern extracted from the speakers' video is developed. The Respiration Pattern is first extracted from the video focusing on the abdominal-thoracic region of a speaker using an optical flow based method. Subsequently, voice activity is detected from the respiration pattern signal using neural sequence-to-sequence prediction models. The efficacy of the proposed method is demonstrated through experiments on a challenging dataset recorded in real acoustic environments and compared with four previous methods based on audio and visual cues.
Top2Vec: Distributed Representations of Topics
Venue: arXiv cs.CL 2020
Year: 2020

Topic modeling is used for discovering latent semantic structure, usually referred to as topics, in a large collection of documents. The most widely used methods are Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis. Despite their popularity they have several weaknesses. In order to achieve optimal results they often require the number of topics to be known, custom stop-word lists, stemming, and lemmatization. Additionally these methods rely on bag-of-words representation of documents which ignore the ordering and semantics of words. Distributed representations of documents and words have gained popularity due to their ability to capture semantics of words and documents. We present $\texttt{top2vec}$, which leverages joint document and word semantic embedding to find $\textit{topic vectors}$. This model does not require stop-word lists, stemming or lemmatization, and it automatically finds the number of topics. The resulting topic vectors are jointly embedded with the document and word vectors with distance between them representing semantic similarity. Our experiments demonstrate that $\texttt{top2vec}$ finds topics which are significantly more informative and representative of the corpus trained on than probabilistic generative models.
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
Venue: arXiv cs.IR 2020
Year: 2020

Delivering treatment recommendations via pervasive electronic devices such as mobile phones has the potential to be a viable and scalable treatment medium for long-term health behavior management. But active experimentation of treatment options can be time-consuming, expensive and altogether unethical in some cases. There is a growing interest in methodological approaches that allow an experimenter to learn and evaluate the usefulness of a new treatment strategy before deployment. We present the first development of a treatment recommender system for emotion regulation using real-world historical mobile digital data from n = 114 high socially anxious participants to test the usefulness of new emotion regulation strategies. We explore a number of offline contextual bandits estimators for learning and propose a general framework for learning algorithms. Our experimentation shows that the proposed doubly robust offline learning algorithms performed significantly better than baseline approaches, suggesting that this type of recommender algorithm could improve emotion regulation. Given that emotion regulation is impaired across many mental illnesses and such a recommender algorithm could be scaled up easily, this approach holds potential to increase access to treatment for many people. We also share some insights that allow us to translate contextual bandit models to this complex real-world data, including which contextual features appear to be most important for predicting emotion regulation strategy effectiveness.
Neural Logic Reasoning
Venue: arXiv cs.LG 2020
Year: 2020

Recent years have witnessed the success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of cognitive reasoning. However, the concrete ability of reasoning is critical to many theoretical and practical problems. On the other hand, traditional symbolic reasoning methods do well in making logical inference, but they are mostly hard rule-based reasoning, which limits their generalization ability to different tasks since difference tasks may require different rules. Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs. In this paper, we propose Logic-Integrated Neural Network (LINN) to integrate the power of deep learning and logic reasoning. LINN is a dynamic neural architecture that builds the computational graph according to input logical expressions. It learns basic logical operations such as AND, OR, NOT as neural modules, and conducts propositional logical reasoning through the network for inference. Experiments on theoretical task show that LINN achieves significant performance on solving logical equations and variables. Furthermore, we test our approach on the practical task of recommendation by formulating the task into a logical inference problem. Experiments show that LINN significantly outperforms state-of-the-art recommendation models in Top-K recommendation, which verifies the potential of LINN in practice.
The Coverage Overlapping Problem of Serving Arbitrary Crowds in 3D Drone Cellular Networks
Venue: arXiv cs.NI 2020
Year: 2020

Providing coverage for flash crowds is an important application for drone base stations (DBSs). However, any arbitrary crowd is likely to be distributed at a high density. Under the condition for each DBS to serve the same number of ground users, multiple DBSs may be placed at the same horizontal location but different altitudes and will cause severe co-channel interference, to which we refer as the coverage overlapping problem. To solve this problem, we then proposed the data-driven 3D placement (DDP) and the enhanced DDP (eDDP) algorithms. The proposed DDP and eDDP can effectively find the appropriate number, altitude, location, and coverage of DBSs in the serving area in polynomial time to maximize the system sum rate and guarantee the minimum data rate requirement of the user equipment. The simulation results show that, compared with the balanced k-means approach, the proposed eDDP can increase the system sum rate by 200% and reduce the computation time by 50%. In particular, eDDP can effectively reduce the occurrence of the coverage overlapping problem and then outperform DDP by about 100% in terms of system sum rate.
In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals
Venue: arXiv cs.SI 2020
Year: 2020

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak -- the patient zero or index patient -- requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks. It is also applicable to COVID-19 hospital-associated transmissions.