OpenRaiser/Intern-Atlas huggingface dataset:
Estimation of Personalized Effects Associated With Causal Pathways
Venue: arXiv cs.LG 2018
Year: 2018

The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway may be of interest as well. For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence. In such cases, we may wish to optimize the direct effect of a drug, while keeping the indirect effect to that of some reference treatment. [16] shows how to combine mediation analysis and dynamic treatment regime ideas to defines policies associated with causal pathways and counterfactual responses to these policies. In this paper, we derive a variety of methods for learning high quality policies of this type from data, in a causal model corresponding to a longitudinal setting of practical importance. We illustrate our methods via a dataset of HIV patients undergoing therapy, gathered in the Nigerian PEPFAR program.
What privacy concerns do parents have about children's mobile apps, and how can they stay SHARP?
Venue: arXiv cs.CY 2018
Year: 2018

Tablet computers are widely used by young children. A report in 2016 shows that children aged 5 to 15 years are spending more time online than watching TV. A 2017 update of the same report shows that parents are becoming more concerned about their children's online risks compared to the previous year. Parents are working hard to protect their children's online safety. An increasing number of parents are setting up content filtering at home or having regular discussions with their children regarding online risks. However, although risks related to Social Media platforms or social video sharing sites (like YouTube) are widely known, risks posed by mobile applications or games (i.e. `apps') are less known. Behind the cute characters, apps used by children can not only have the possibility of exposing them to age-inappropriate content or excessive in-app promotions, but may also make a large amount of their personal information accessible to third-party online marketing and advertising industry. Such practices are not unique to children's apps, but young children are probably less capable of resisting the resulting personalised advertisements and game promotions. In this report, we present findings from our online survey of 220 parents with children aged 6-10, mainly from the U.K. and other western countries, regarding their privacy concerns and expectations of their children's use of mobile apps. Parents play a key role in children's use of digital technology, especially for children under 10 years old. Recent reports have highlighted parents' lack of sufficient support for choosing appropriate digital content for their children. Our report sheds some initial light on parents' key struggles and points to immediate steps and possible areas of future development.
An open source massively parallel solver for Richards equation: Mechanistic modelling of water fluxes at the watershed scale
Venue: arXiv cs.CE 2018
Year: 2018

In this paper we present a massively parallel open source solver for Richards equation, named the RichardsFOAM solver. This solver has been developed in the framework of the open source generalist computational fluid dynamics tool box OpenFOAM (R) and is capable to deal with large scale problems in both space and time. The source code for RichardsFOAM may be downloaded from the CPC program library website.It exhibits good parallel performances (up to $\sim$90% parallel efficiency with 1024 processors both in strong and weak scaling), and the conditions required for obtaining such performances are analysed and discussed. These performances enable the mechanistic modelling of water fluxes at the scale of experimental watersheds (up to few square kilometres of surface area), and on time scales of decades to a century. Such a solver can be useful in various applications, such as environmental engineering for long term transport of pollutants in soils, water engineering for assessing the impact of land settlement on water resources, or in the study of weathering processes on the watersheds.
Blockchain and Smart-contracts Modeled in a SwarmESB Ecosystem
Venue: arXiv cs.DC 2018
Year: 2018

Blockchain has emerged as a trusted and secure distributed ledger for transactions while also being decentralised, distributed and its legitimacy not guaranteed by a trusted authority. Since the appearance of Bitcoin, Blockchain has known many implementations based on P2P architectures. This paper presents how the blockchain and smart contracts technologies can be integrated into the SwarmESB ecosystem. SwarmESB is a framework that helps building distributed applications, which benefit from privacy and scalability features. Our proposal will present the flexibility in building not only microservices based applications, but also decen-tralised applications employing blockchain and smart-contracts by modeling a sample Dapp.
Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera using Deep Residual Networks
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel approach to recovering the translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording the translucent objects using the ToF depth camera, their depth values are severely contaminated due to complex light interactions with the surrounding environment. While existing methods suggested new capture systems or developed the depth distortion models, their solutions were less practical because of strict assumptions or heavy computational complexity. In this paper, we adopt the deep residual networks for modeling the ToF depth distortion caused by translucency. To fully utilize both the local and semantic information of objects, multi-scale patches are used to predict the depth value. Based on the quantitative and qualitative evaluation on our benchmark database, we show the effectiveness and robustness of the proposed algorithm.
Strong Coordination over Noisy Channels with Strictly Causal Encoding
Venue: arXiv cs.IT 2018
Year: 2018

We consider a network of two nodes separated by a noisy channel, in which the input and output signals have to be coordinated with the source and its reconstruction. In the case of strictly causal encoding and non-causal decoding, we prove inner and outer bounds for the strong coordination region and show that the inner bound is achievable with polar codes.
Strong Collapse for Persistence
Venue: arXiv cs.CG 2018
Year: 2018

We introduce a fast and memory efficient approach to compute the persistent homology (PH) of a sequence of simplicial complexes. The basic idea is to simplify the complexes of the input sequence by using strong collapses, as introduced by J. Barmak and E. Miniam [DCG (2012)], and to compute the PH of an induced sequence of reduced simplicial complexes that has the same PH as the initial one. Our approach has several salient features that distinguishes it from previous work. It is not limited to filtrations (i.e. sequences of nested simplicial subcomplexes) but works for other types of sequences like towers and zigzags. To strong collapse a simplicial complex, we only need to store the maximal simplices of the complex, not the full set of all its simplices, which saves a lot of space and time. Moreover, the complexes in the sequence can be strong collapsed independently and in parallel. Finally, we can compromize between precision and time by choosing the number of simplicial complexes of the sequence we strong collapse. As a result and as demonstrated by numerous experiments on publicly available data sets, our approach is extremely fast and memory efficient in practice.
From geometric design to numerical analysis: A direct approach using the Finite Cell Method on Constructive Solid Geometry
Venue: arXiv cs.CE 2018
Year: 2018

During the last ten years, increasing efforts were made to improve and simplify the process from Computer Aided Design (CAD) modeling to a numerical simulation. It has been shown that the transition from one model to another, i.e. the meshing, is a bottle-neck. Several approaches have been developed to overcome this time-consuming step, e.g. Isogeometric Analysis (IGA), which applies the shape functions used for the geometry description (typically B-Splines and NURBS) directly to the numerical analysis. In contrast to IGA, which deals with boundary represented models (B-Rep), our approach focuses on parametric volumetric models such as Constructive Solid Geometries (CSG). These models have several advantages, as their geometry description is inherently watertight and they provide a description of the model's interior. To be able to use the explicit mathematical description of these models, we employ the Finite Cell Method (FCM). Herein, the only necessary input is a reliable statement whether an (integration-) point lies inside or outside of the geometric model. This paper mainly discusses such point-in-membership tests on various geometric objects like sweeps and lofts, as well as several geometric operations such as filleting or chamfering. We demonstrate that, based on the information of the construction method of these objects, the point-in-membership-test can be carried out efficiently and robustly
GroupCast: Preference-Aware Cooperative Video Streaming with Scalable Video Coding
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose a preference-aware cooperative video streaming system for videos encoded using Scalable Video Coding (SVC) where all the collaborating users are interested in watching a video together on a shared screen. However, each user's willingness to cooperate is subject to her own constraints such as user data plans and/or energy consumption. Using SVC, each layer of every chunk can be fetched through any of the cooperating users. We formulate the problem of finding the optimal quality decisions and fetching policy of the SVC layers of video chunks subject to the available bandwidth, chunk deadlines, and cooperation willingness of the different users as an optimization problem. The objective is to optimize a QoE metric that maintains a trade-off between maximizing the playback rate of every chunk while ensuring fairness among all chunks for the minimum skip/stall duration without violating any of the imposed constraints. We propose an offline algorithm to solve the non-convex optimization problem when the bandwidth prediction is non-causally known. This algorithm has a run-time complexity that is polynomial in the video length and the number of cooperating users. Furthermore, we propose an online version of the algorithm for more practical scenarios where erroneous bandwidth prediction for a short window is used. Real implementation with android devices using SVC encoded video on public bandwidth traces' dataset reveals the robustness and performance of the proposed algorithm and shows that the algorithm significantly outperforms round robin based mechanisms in terms of avoiding skips/stalls and fetching video chunks at their highest quality possible.
Peer-to-Peer Energy Trading with Sustainable User Participation: A Game Theoretic Approach
Venue: arXiv cs.GT 2018
Year: 2018

This paper explores the feasibility of social cooperation between prosumers within an energy network in establishing their sustainable participation in peer-to-peer (P2P) energy trading. In particular, a canonical coalition game (CCG) is utilized to propose a P2P energy trading scheme, in which a set of participating prosumers form a coalition group to trade their energy, if there is any, with one another. By exploring the concept of the core of the designed CCG framework, the mid-market rate is utilized as a pricing mechanism of the proposed P2P trading to confirm the stability of the coalition as well as to guarantee the benefit to the prosumers for forming the social coalition. The paper further introduces the motivational psychology models that are relevant to the proposed P2P scheme and it is shown that the outcomes of proposed P2P energy trading scheme satisfy the discussed models. Consequently, it is proven that the proposed scheme is consumer-centric that has the potential to corroborate sustainable prosumers participation in P2P energy trading. Finally, some numerical examples are provided to demonstrate the beneficial properties of the proposed scheme.
A Systematic Study on Static Control Flow Obfuscation Techniques in Java
Venue: arXiv cs.CR 2018
Year: 2018

Control flow obfuscation (CFO) alters the control flow path of a program without altering its semantics. Existing literature has proposed several techniques; however, a quick survey reveals a lack of clarity in the types of techniques proposed, and how many are unique. What is also unclear is whether there is a disparity in the theory and practice of CFO. In this paper, we systematically study CFO techniques proposed for Java programs, both from papers and commercially available tools. We evaluate 13 obfuscators using a dataset of 16 programs with varying software characteristics, and different obfuscator parameters. Each program is carefully reverse engineered to study the effect of obfuscation. Our study reveals that there are 36 unique techniques proposed in the literature and 7 from tools. Three of the most popular commercial obfuscators implement only 13 of the 36 techniques in the literature. Thus there appears to be a gap between the theory and practice of CFO. We propose a novel classification of the obfuscation techniques based on the underlying component of a program that is transformed. We identify the techniques that are potent against reverse engineering attacks, both from the perspective of a human analyst and an automated program decompiler. Our analysis reveals that majority of the tools do not implement these techniques, thus defeating the protection obfuscation offers. We furnish examples of select techniques and discuss our findings. To the best of our knowledge, we are the first to assemble such a research. This study will be useful to software designers to decide upon the best techniques to use based upon their needs, for researchers to understand the state-of-the-art and for commercial obfuscator developers to develop new techniques.
Learning to Improve Capture Steps for Disturbance Rejection in Humanoid Soccer
Venue: arXiv cs.RO 2018
Year: 2018

Over the past few years, soccer-playing humanoid robots have advanced significantly. Elementary skills, such as bipedal walking, visual perception, and collision avoidance have matured enough to allow for dynamic and exciting games. When two robots are fighting for the ball, they frequently push each other and balance recovery becomes crucial. In this paper, we report on insights we gained from systematic push experiments performed on a bipedal model and outline an online learning method we used to improve its push-recovery capabilities. In addition, we describe how the localization ambiguity introduced by the uniform goal color was resolved and report on the results of the RoboCup 2013 competition.
The igus Humanoid Open Platform: A Child-sized 3D Printed Open-Source Robot for Research
Venue: arXiv cs.RO 2018
Year: 2018

The use of standard robotic platforms can accelerate research and lower the entry barrier for new research groups. There exist many affordable humanoid standard platforms in the lower size ranges of up to 60cm, but larger humanoid robots quickly become less affordable and more difficult to operate, maintain and modify. The igus Humanoid Open Platform is a new and affordable, fully open-source humanoid platform. At 92cm in height, the robot is capable of interacting in an environment meant for humans, and is equipped with enough sensors, actuators and computing power to support researchers in many fields. The structure of the robot is entirely 3D printed, leading to a lightweight and visually appealing design. The main features of the platform are described in this article.
Data-Driven Modeling of Group Entitativity in Virtual Environments
Venue: arXiv cs.GR 2018
Year: 2018

We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds. This mapping is based on a statistical scheme that dynamically learns pedestrian behavior and computes the resultant entitativity induced emotion through group motion characteristics. We also present a novel interactive multi-agent simulation algorithm to model entitative groups and conduct a VR user study to validate the socio-emotional predictive power of our algorithm. We further show that model-generated high-entitativity groups do induce more negative emotions than low-entitative groups.
Celer Network: Bring Internet Scale to Every Blockchain
Venue: arXiv cs.NI 2018
Year: 2018

Off-chain scaling techniques allow mutually distrustful parties to execute a contract locally among themselves instead of on the global blockchain. Parties involved in the transaction maintain a multi-signature fraud-proof off-chain replicated state machine, and only resort to on-chain consensus when absolutely necessary (e.g., when two parties disagree on a state). Off-chain scaling is the only way to support fully scale-out decentralized applications ("dApps") with better privacy and no compromise on the trust and decentralization guarantees. It is the inflection point for blockchain mass adoption, and will be the engine behind all scalable dApps. Celer Network is an Internet-scale, trust-free, and privacy-preserving platform where everyone can quickly build, operate, and use highly scalable dApps. It is not a standalone blockchain but a networked system running on top of existing and future blockchains. It provides unprecedented performance and flexibility through innovation in off-chain scaling techniques and incentive-aligned cryptoeconomics. Celer Network embraces a layered architecture with clean abstractions that enable rapid evolution of each individual component, including a generalized state channel and sidechain suite that supports fast and generic off-chain state transitions; a provably optimal value transfer routing mechanism that achieves an order of magnitude higher throughput compared to state-of-the-art solutions; a powerful development framework and runtime for off-chain applications; and a new cryptoeconomic model that provides network effect, stable liquidity, and high availability for the off-chain ecosystem.
Estimation-Based Model Predictive Control for Automatic Crosswind Stabilization of Hybrid Aerial Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we study the control design of an automatic crosswind stabilization system for a novel, buoyantly-assisted aerial transportation vehicle. This vehicle has several advantages over other aircraft including the ability to take-off and land in very short distances and without the need for roads or runways. Despite these advantages, the large surface area of the vehicle's wing makes it more susceptible to wind, which introduces undesirable roll angle motions. The role of the automatic crosswind stabilization system is to detect the roll angle deviation, and then use motors at the wingtips to counteract the wind effect. However, due to the relatively large inertia of the wing compared to small-size unmanned aerial vehicles and additional input time delays, an automatic crosswind stabilization system based on traditional control algorithms such as the proportional-integral-derivative (PID) controller results in a response time that is too slow. Another challenge is the lack of high-accuracy wind sensors that can be mounted on the vehicle's wing. Therefore, we first design a wind torque estimator that relies on inertial measurements, and then use feed-forward compensation to directly correct for the wind torque, resulting in a significantly faster response. We second combine the proposed estimator with a model predictive controller (MPC), and compare constrained MPC with unconstrained MPC for the considered application. Experimental results show that our proposed estimation-based MPC strategy reduces the response time of the system by around 80-90% compared to a standard PID controller, without the need for adding wind sensors or changing the hardware of the stabilization system.
The Partially Observable Games We Play for Cyber Deception
Venue: arXiv cs.AI 2018
Year: 2018

Progressively intricate cyber infiltration mechanisms have made conventional means of defense, such as firewalls and malware detectors, incompetent. These sophisticated infiltration mechanisms can study the defender's behavior, identify security caveats, and modify their actions adaptively. To tackle these security challenges, cyber-infrastructures require active defense techniques that incorporate cyber deception, in which the defender (deceiver) implements a strategy to mislead the infiltrator. To this end, we use a two-player partially observable stochastic game (POSG) framework, wherein the deceiver has full observability over the states of the POSG, and the infiltrator has partial observability. Then, the deception problem is to compute a strategy for the deceiver that minimizes the expected cost of deception against all strategies of the infiltrator. We first show that the underlying problem is a robust mixed-integer linear program, which is intractable to solve in general. Towards a scalable approach, we compute optimal finite-memory strategies for the infiltrator by a reduction to a series of synthesis problems for parametric Markov decision processes. We use these infiltration strategies to find robust strategies for the deceiver using mixed-integer linear programming. We illustrate the performance of our technique on a POSG model for network security. Our experiments demonstrate that the proposed approach handles scenarios considerably larger than those of the state-of-the-art methods.
Towards Better Summarizing Bug Reports with Crowdsourcing Elicited Attributes
Venue: arXiv cs.SE 2018
Year: 2018

Recent years have witnessed the growing demands for resolving numerous bug reports in software maintenance. Aiming to reduce the time testers/developers take in perusing bug reports, the task of bug report summarization has attracted a lot of research efforts in the literature. However, no systematic analysis has been conducted on attribute construction which heavily impacts the performance of supervised algorithms for bug report summarization. In this study, we first conduct a survey to reveal the existing methods for attribute construction in mining software repositories. Then, we propose a new method named Crowd-Attribute to infer new effective attributes from the crowdgenerated data in crowdsourcing and develop a new tool named Crowdsourcing Software Engineering Platform to facilitate this method. With Crowd-Attribute, we successfully construct 11 new attributes and propose a new supervised algorithm named Logistic Regression with Crowdsourced Attributes (LRCA). To evaluate the effectiveness of LRCA, we build a series of large scale data sets with 105,177 bug reports. Experiments over both the public data set SDS with 36 manually annotated bug reports and new large-scale data sets demonstrate that LRCA can consistently outperform the state-of-the-art algorithms for bug report summarization.