OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing the highly cited articles: a large-scale bibliometric analysis of the top 1% most cited research
Venue: arXiv cs.DL 2018
Year: 2018

We conducted a large-scale analysis of around 10,000 scientific articles, from the period 2007-2016, to study the bibliometric or formal aspects influencing citations. A transversal analysis was conducted disaggregating the articles into more than one hundred scientific areas and two groups, one experimental and one control, each with a random sample of around five thousand documents. The experimental group comprised a random sample of the top 1% most cited articles in each field and year of publication (highly cited articles), and the control group a random sample of the remaining articles in the Journal Citation Reports (science and social science citation indexes in the Web of Science database). As the main result, highly cited articles differ from non-highly cited articles in most of the bibliometric aspects considered. There are significant differences, below the 0.01 level, between the groups of articles in many variables and areas. The highly cited articles are published in journals of higher impact factor (33 percentile points above) and have 25% higher co-authorship. The highly cited articles are also longer in terms of number of pages (10% higher) and bibliographical references (35% more). Finally, highly cited articles have slightly shorter titles (3% lower) but, contrastingly, longer abstracts (10% higher).
Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming
Venue: arXiv cs.AI 2018
Year: 2018

Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such ad-hoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on Answer Set Programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.
Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning
Venue: arXiv cs.CV 2018
Year: 2018

Due to low tissue contrast, irregular object appearance, and unpredictable location variation, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, we present a novel method for interactive medical image segmentation with the following merits. (1) Our design is fundamentally different from previous pure patch-based and image-based segmentation methods. We observe that during delineation, the physician repeatedly check the inside-outside intensity changing to determine the boundary, which indicates that comparison in an inside-outside manner is extremely important. Thus, we innovatively model our segmentation task as learning the representation of the bi-directional sequential patches, starting from (or ending in) the given central point of the object. This can be realized by our proposed ConvRNN network embedded with a gated memory propagation unit. (2) Unlike previous interactive methods (requiring bounding box or seed points), we only ask the physician to merely click on the rough central point of the object before segmentation, which could simultaneously enhance the performance and reduce the segmentation time. (3) We utilize our method in a multi-level framework for better performance. We systematically evaluate our method in three different segmentation tasks including CT kidney tumor, MR prostate, and PROMISE12 challenge, showing promising results compared with state-of-the-art methods. The code is available here: \href{https://github.com/sunalbert/Sequential-patch-based-segmentation}{Sequential-patch-based-segmentation}.
Learning Non-Stationary Space-Time Models for Environmental Monitoring
Venue: arXiv cs.LG 2018
Year: 2018

One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both space and time and it is imperative to develop a deeper understanding of techniques that can model space-time dynamics accurately. In this paper we propose NOSTILL-GP - NOn-stationary Space TIme variable Latent Length scale GP, a generic non-stationary, spatio-temporal Gaussian Process (GP) model. We present several strategies, for efficient training of our model, necessary for real-world applicability. Extensive empirical validation is performed using three real-world environmental monitoring datasets, with diverse dynamics across space and time. Results from the experiments clearly demonstrate general applicability and effectiveness of our approach for applications in environmental monitoring.
Alleviating partisan gerrymandering: can math and computers help to eliminate wasted votes?
Venue: arXiv cs.CY 2018
Year: 2018

Partisan gerrymandering is a major cause for voter disenfranchisement in United States. However, convincing US courts to adopt specific measures to quantify gerrymandering has been of limited success to date. Recently, Stephanopoulos and McGhee introduced a new and precise measure of partisan gerrymandering via the so-called "efficiency gap" that computes the absolutes difference of wasted votes between two political parties in a two-party system. Quite importantly from a legal point of view, this measure was found legally convincing enough in a US appeals court in a case that claims that the legislative map of the state of Wisconsin was gerrymandered; the case is now pending in US Supreme Court. In this article, we show the following: (a) We provide interesting mathematical and computational complexity properties of the problem of minimizing the efficiency gap measure. To the best of our knowledge, these are the first non-trivial theoretical and algorithmic analyses of this measure of gerrymandering. (b) We provide a simple and fast algorithm that can "un-gerrymander" the district maps for the states of Texas, Virginia, Wisconsin and Pennsylvania by bring their efficiency gaps to acceptable levels from the current unacceptable levels. Our work thus shows that, notwithstanding the general worst case approximation hardness of the efficiency gap measure as shown by us,finding district maps with acceptable levels of efficiency gaps is a computationally tractable problem from a practical point of view. Based on these empirical results, we also provide some interesting insights into three practical issues related the efficiency gap measure. We believe that, should the US Supreme Court uphold the decision of lower courts, our research work and software will provide a crucial supporting hand to remove partisan gerrymandering.
Enforceable Data Sharing Agreements Using Smart Contracts
Venue: arXiv cs.CY 2018
Year: 2018

As more and more data is collected for various reasons, the sharing of such data becomes paramount to increasing its value. Many applications ranging from smart cities to personalized health care require individuals and organizations to share data at an unprecedented scale. Data sharing is crucial in today's world, but due to privacy reasons, security concerns and regulation issues, the conditions under which the sharing occurs needs to be carefully specified. Currently, this process is done by lawyers and requires the costly signing of legal agreements. In many cases, these data sharing agreements are hard to track, manage or enforce. In this work, we propose a novel alternative for tracking, managing and especially enforcing such data sharing agreements using smart contracts and blockchain technology. We design a framework that generates smart contracts from parameters based on legal data sharing agreements. The terms in these agreements are automatically enforced by the system. Monetary punishment can be employed using secure voting by external auditors to hold the violators accountable. Our experimental evaluation shows that our proposed framework is efficient and low-cost.
Mapping Road Lanes Using Laser Remission and Deep Neural Networks
Venue: arXiv cs.RO 2018
Year: 2018

We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations. We take a segmentation approach to the problem and use the Efficient Neural Network (ENet) DNN for segmenting LiDAR remission grid maps into road maps. We represent road maps using what we called road grid maps. Road grid maps are square matrixes and each element of these matrixes represents a small square region of real-world space. The value of each element is a code associated with the semantics of the road map. Our road grid maps contain all information about the roads' lanes required for building the Road Definition Data Files (RDDFs) that are necessary for the operation of our autonomous car, IARA (Intelligent Autonomous Robotic Automobile). We have built a dataset of tens of kilometers of manually marked road lanes and used part of it to train ENet to segment road grid maps from remission grid maps. After being trained, ENet achieved an average segmentation accuracy of 83.7%. We have tested the use of inferred road grid maps in the real world using IARA on a stretch of 3.7 km of urban roads and it has shown performance equivalent to that of the previous IARA's subsystem that uses a manually generated RDDF.
Alternative parameterizations of Metric Dimension
Venue: arXiv cs.DS 2018
Year: 2018

A set of vertices $W$ in a graph $G$ is called resolving if for any two distinct $x,y\in V(G)$, there is $v\in W$ such that ${\rm dist}_G(v,x)\neq{\rm dist}_G(v,y)$, where ${\rm dist}_G(u,v)$ denotes the length of a shortest path between $u$ and $v$ in the graph $G$. The metric dimension ${\rm md}(G)$ of $G$ is the minimum cardinality of a resolving set. The Metric Dimension problem, i.e. deciding whether ${\rm md}(G)\le k$, is NP-complete even for interval graphs (Foucaud et al., 2017). We study Metric Dimension (for arbitrary graphs) from the lens of parameterized complexity. The problem parameterized by $k$ was proved to be $W[2]$-hard by Hartung and Nichterlein (2013) and we study the dual parameterization, i.e., the problem of whether ${\rm md}(G)\le n- k,$ where $n$ is the order of $G$. We prove that the dual parameterization admits (a) a kernel with at most $3k^4$ vertices and (b) an algorithm of runtime $O^*(4^{k+o(k)}).$ Hartung and Nichterlein (2013) also observed that Metric Dimension is fixed-parameter tractable when parameterized by the vertex cover number $vc(G)$ of the input graph. We complement this observation by showing that it does not admit a polynomial kernel even when parameterized by $vc(G) + k$. Our reduction also gives evidence for non-existence of polynomial Turing kernels.
Buffered Count-Min Sketch on SSD: Theory and Experiments
Venue: arXiv cs.DS 2018
Year: 2018

Frequency estimation data structures such as the count-min sketch (CMS) have found numerous applications in databases, networking, computational biology and other domains. Many applications that use the count-min sketch process massive and rapidly evolving datasets. For data-intensive applications that aim to keep the overestimate error low, the count-min sketch may become too large to store in available RAM and may have to migrate to external storage (e.g., SSD.) Due to the random-read/write nature of hash operations of the count-min sketch, simply placing it on SSD stifles the performance of time-critical applications, requiring about 4-6 random reads/writes to SSD per estimate (lookup) and update (insert) operation. In this paper, we expand on the preliminary idea of the Buffered Count-Min Sketch (BCMS) [15], an SSD variant of the count-min sketch, that used hash localization to scale efficiently out of RAM while keeping the total error bounded. We describe the design and implementation of the buffered count-min sketch, and empirically show that our implementation achieves 3.7x-4.7x the speedup on update (insert) and 4.3x speedup on estimate (lookup) operations. Our design also offers an asymptotic improvement in the external-memory model [1] over the original data structure: r random I/Os are reduced to 1 I/O for the estimate operation. For a data structure that uses k blocks on SSD, was the word/counter size, r as the number of rows, M as the number of bits in the main memory, our data structure uses kwr/M amortized I/Os for updates, or, if kwr/M >1, 1 I/O in the worst case. In typical scenarios, kwr/M is much smaller than 1. This is in contrast to O(r) I/Os incurred for each update in the original data structure.
Remote Credential Management with Mutual Attestation for Trusted Execution Environments
Venue: arXiv cs.CR 2018
Year: 2018

Trusted Execution Environments (TEEs) are rapidly emerging as a root-of-trust for protecting sensitive applications and data using hardware-backed isolated worlds of execution. TEEs provide robust assurances regarding critical algorithm execution, tamper-resistant credential storage, and platform integrity using remote attestation. However, the challenge of remotely managing credentials between TEEs remains largely unaddressed in existing literature. In this work, we present novel protocols using mutual attestation for supporting four aspects of secure remote credential management with TEEs: backups, updates, migration, and revocation. The proposed protocols are agnostic to the underlying TEE implementation and subjected to formal verification using Scyther, which found no attacks.
Modelling Formation of Online Temporal Communities
Venue: arXiv cs.SI 2018
Year: 2018

Contemporary social media networks can be viewed as a break to the early two-step flow model in which influential individuals act as intermediaries between the media and the public for information diffusion. Today's social media platforms enable users to both generate and consume online contents. Users continuously engage and disengage in discussions with varying degrees of interaction leading to formation of distinct online communities. Such communities are often formed at high-level either based on metadata, such as hashtags on Twitter, or popular content triggered by few influential users. These online communities often do not reflect true connectivity and lack the cohesiveness of traditional communities. In this study, we investigate real-time formation of temporal communities on Twitter. We aim at defining both high and low levels connections and to reveal the magnitude of clustering cohesion on temporal basis. Inspired by a real-life event center sitting arrangement scenario, the proposed method aims to cluster users into distinct and cohesive online temporal communities. Membership to a community relies on intrinsic tweet properties to define similarity as the basis for interaction networks. The proposed method can be useful for local event monitoring and clique-based marketing among other applications.
Secure Computation-and-Forward with Linear Codes
Venue: arXiv cs.IT 2018
Year: 2018

We discuss secure transmission via an untrusted relay when we have a multiple access phase from two nodes to the relay and broadcast phase from the relay to the two nodes. To realize the security, we construct a code that securely transmits the modulo sum of the messages of two nodes via a multiple access channel. In this code, the relay cannot obtain any information for the message of each node, and can decode only the messages of the two nodes. Our code is constructed by simple combination of an existing liner code and universal2 hash function.
Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity
Venue: arXiv cs.CV 2018
Year: 2018

Non-rigid inter-modality registration can facilitate accurate information fusion from different modalities, but it is challenging due to the very different image appearances across modalities. In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images. In particular, the training of our inter-modality registration network is supervised by intra-modality similarity metric based on the available paired data, which is derived from a pre-aligned CT and MR dataset. Specifically, in the training stage, to register the input CT and MR images, their similarity is evaluated on the warped MR image and the MR image that is paired with the input CT. So that, the intra-modality similarity metric can be directly applied to measure whether the input CT and MR images are well registered. Moreover, we use the idea of dual-modality fashion, in which we measure the similarity on both CT modality and MR modality. In this way, the complementary anatomies in both modalities can be jointly considered to more accurately train the inter-modality registration network. In the testing stage, the trained inter-modality registration network can be directly applied to register the new multimodal images without any paired data. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging non-rigid inter-modality registration task and also outperforms the state-of-the-art approaches.
A Resource Pooling Switch Architecture with High Performance Scheduler
Venue: arXiv cs.NI 2018
Year: 2018

With the rapid development of network-based services and applications, current data plane solutions of network node, which focus on packets forwarding traditionally, are not optimally addressing new requirements such as function flexibility, performance expansibility and resource utilization, etc. In this paper, we propose a novel data plane structure called Resource Pooling Switch Architecture (RPSA), which utilizes global shared resource pool to provide different processing functionalities and capacities to different packets/flows. As all network functions are instantiated by the resources connected to the switching fabric and constructed in the form of Service Function Chains (SFC), the traffic patterns changed a lot. We design Balanced Service Capacity based FIRM algorithm (BSC-FIRM) to overcome the deficiencies of classical scheduler in RPSA. Simulation results show that our algorithm outperforms on the aspect of packet switching delay and loss rate compared with FIRM and iSLIP algorithm.
New algorithms for Steiner tree reoptimization
Venue: arXiv cs.DS 2018
Year: 2018

{\em Reoptimization} is a setting in which we are given an (near) optimal solution of a problem instance and a local modification that slightly changes the instance. The main goal is that of finding an (near) optimal solution of the modified instance. We investigate one of the most studied scenarios in reoptimization known as {\em Steiner tree reoptimization}. Steiner tree reoptimization is a collection of strongly NP-hard optimization problems that are defined on top of the classical Steiner tree problem and for which several constant-factor approximation algorithms have been designed in the last decade. In this paper we improve upon all these results by developing a novel technique that allows us to design {\em polynomial-time approximation schemes}. Remarkably, prior to this paper, no approximation algorithm better than recomputing a solution from scratch was known for the elusive scenario in which the cost of a single edge decreases. Our results are best possible since none of the problems addressed in this paper admits a fully polynomial-time approximation scheme, unless P=NP.
CRAFT: Complementary Recommendations Using Adversarial Feature Transformer
Venue: arXiv cs.CV 2018
Year: 2018

Traditional approaches for complementary product recommendations rely on behavioral and non-visual data such as customer co-views or co-buys. However, certain domains such as fashion are primarily visual. We propose a framework that harnesses visual cues in an unsupervised manner to learn the distribution of co-occurring complementary items in real world images. Our model learns a non-linear transformation between the two manifolds of source and target complementary item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring object categories, we train a generative transformer network directly on the feature representation space by casting it as an adversarial optimization problem. Such a conditional generative model can produce multiple novel samples of complementary items (in the feature space) for a given query item. The final recommendations are selected from the closest real world examples to the synthesized complementary features. We apply our framework to the task of recommending complementary tops for a given bottom clothing item. The recommendations made by our system are diverse, and are favored by human experts over the baseline approaches.
Restricted Max-Min Fair Allocation
Venue: arXiv cs.DS 2018
Year: 2018

The restricted max-min fair allocation problem seeks an allocation of resources to players that maximizes the minimum total value obtained by any player. It is NP-hard to approximate the problem to a ratio less than 2. Comparing the current best algorithm for estimating the optimal value with the current best for constructing an allocation, there is quite a gap between the ratios that can be achieved in polynomial time: roughly 4 for estimation and roughly $6 + 2\sqrt{10}$ for construction. We propose an algorithm that constructs an allocation with value within a factor of $6 + δ$ from the optimum for any constant $δ> 0$. The running time is polynomial in the input size for any constant $δ$ chosen.