OpenRaiser/Intern-Atlas huggingface dataset:
Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations?
Venue: arXiv cs.DL 2019
Year: 2019

Dissertations can be the single most important scholarly outputs of junior researchers. Whilst sets of journal articles are often evaluated with the help of citation counts from the Web of Science or Scopus, these do not index dissertations and so their impact is hard to assess. In response, this article introduces a new multistage method to extract Google Scholar citation counts for large collections of dissertations from repositories indexed by Google. The method was used to extract Google Scholar citation counts for 77,884 American doctoral dissertations from 2013-2017 via ProQuest, with a precision of over 95%. Some ProQuest dissertations that were dual indexed with other repositories could not be retrieved with ProQuest-specific searches but could be found with Google Scholar searches of the other repositories. The Google Scholar citation counts were then compared with Mendeley reader counts, a known source of scholarly-like impact data. A fifth of the dissertations had at least one citation recorded in Google Scholar and slightly fewer had at least one Mendeley reader. Based on numerical comparisons, the Mendeley reader counts seem to be more useful for impact assessment purposes for dissertations that are less than two years old, whilst Google Scholar citations are more useful for older dissertations, especially in social sciences, arts and humanities. Google Scholar citation counts may reflect a more scholarly type of impact than that of Mendeley reader counts because dissertations attract a substantial minority of their citations from other dissertations. In summary, the new method now makes it possible for research funders, institutions and others to systematically evaluate the impact of dissertations, although additional Google Scholar queries for other online repositories are needed to ensure comprehensive coverage.
A Decentralised Digital Identity Architecture
Venue: arXiv cs.CY 2019
Year: 2019

Current architectures to validate, certify, and manage identity are based on centralised, top-down approaches that rely on trusted authorities and third-party operators. We approach the problem of digital identity starting from a human rights perspective, with a primary focus on identity systems in the developed world. We assert that individual persons must be allowed to manage their personal information in a multitude of different ways in different contexts and that to do so, each individual must be able to create multiple unrelated identities. Therefore, we first define a set of fundamental constraints that digital identity systems must satisfy to preserve and promote privacy as required for individual autonomy. With these constraints in mind, we then propose a decentralised, standards-based approach, using a combination of distributed ledger technology and thoughtful regulation, to facilitate many-to-many relationships among providers of key services. Our proposal for digital identity differs from others in its approach to trust in that we do not seek to bind credentials to each other or to a mutually trusted authority to achieve strong non-transferability. Because the system does not implicitly encourage its users to maintain a single aggregated identity that can potentially be constrained or reconstructed against their interests, individuals and organisations are free to embrace the system and share in its benefits.
Multilinear Cryptography using Nilpotent Groups
Venue: arXiv cs.CR 2019
Year: 2019

In this paper we generalize the definition of a multilinear map to arbitrary groups and develop a novel idea of multilinear cryptosystem using nilpotent group identities.
Faster and simpler algorithms for finding large patterns in permutations
Venue: arXiv cs.DS 2019
Year: 2019

Permutation patterns and pattern avoidance have been intensively studied in combinatorics and computer science, going back at least to the seminal work of Knuth on stack-sorting (1968). Perhaps the most natural algorithmic question in this area is deciding whether a given permutation of length $n$ contains a given pattern of length $k$. In this work we give two new algorithms for this well-studied problem, one whose running time is $n^{0.44k+o(k)}$, and one whose running time is the better of $O(1.6181^n)$ and $n^{k/2+o(k)}$. These results improve the earlier best bounds of Ahal and Rabinovich (2000), and Bruner and Lackner (2012), and are the fastest algorithms for the problem when $k = Ω(\log n)$. When $k = o(\log n)$, the parameterized algorithm of Guillemot and Marx (2013) dominates. Our second algorithm uses polynomial space and is significantly simpler than all previous approaches with comparable running times, including an $n^{k/2+o(k)}$ algorithm proposed by Guillemot and Marx. Our approach can be summarized as follows: "for every matching of the even-valued entries of the pattern, try to match all odd-valued entries left-to-right". For the special case of patterns that are Jordan-permutations, we show an improved, subexponential running time.
Statistical Method to Model the Quality Inconsistencies of the Welding Process
Venue: arXiv cs.LG 2019
Year: 2019

Resistance Spot Welding (RSW) is an important manufacturing process that attracts increasing attention in automotive industry. However, due to the complexity of the manufacturing process, the corresponding product quality shows significant inconsistencies even under the same process setup. This paper develops a statistical method to capture the inconsistence of welding quality measurements (e.g., nugget width) based on process parameters to efficiently monitor product quality. The proposed method provides engineering efficiency and cost saving benefit through reduction of physical testing required for weldability and verification. The developed method is applied to the real-world welding process.
TBNet:Pulmonary Tuberculosis Diagnosing System using Deep Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Tuberculosis is a deadly infectious disease prevalent around the world. Due to the lack of proper technology in place, the early detection of this disease is unattainable. Also, the available methods to detect Tuberculosis is not up-to a commendable standards due to their dependency on unnecessary features, this make such technology obsolete for a reliable health-care technology. In this paper, I propose a deep-learning based system which diagnoses tuberculosis based on the important features in Chest X-rays along with original chest X-rays. Employing our system will accelerate the process of tuberculosis diagnosis by overcoming the need to perform the time-consuming sputum-based testing method (Diagnostic Microbiology). In contrast to the previous methods \cite{kant2018towards, melendez2016automated}, our work utilizes the state-of-the-art ResNet \cite{he2016deep} with proper data augmentation using traditional robust features like Haar \cite{viola2005detecting,viola2001rapid} and LBP \cite{ojala1994performance,ojala1996comparative}. I observed that such a procedure enhances the rate of tuberculosis detection to a highly satisfactory level. Our work uses the publicly available pulmonary chest X-ray dataset to train our network \cite{jaeger2014two}. Nevertheless, the publicly available dataset is very small and is inadequate to achieve the best accuracy. To overcome this issue I have devised an intuitive feature based data augmentation pipeline. Our approach shall help the deep neural network \cite{lecun2015deep,he2016deep,krizhevsky2012imagenet} to focus its training on tuberculosis affected regions making it more robust and accurate, when compared to other conventional methods that use procedures like mirroring and rotation. By using our simple yet powerful techniques, I observed a 10\% boost in performance accuracy.
MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses
Venue: arXiv cs.CR 2019
Year: 2019

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names through which bots can establish a resilient communication channel with their command and control servers. Recent publications presented deep learning, character-level classifiers that are able to detect algorithmically generated domain (AGD) names with high accuracy, and correspondingly, significantly reduce the effectiveness of DGAs for botnet communication. In this paper we present MaskDGA, a practical adversarial learning technique that adds perturbation to the character-level representation of algorithmically generated domain names in order to evade DGA classifiers, without the attacker having any knowledge about the DGA classifier's architecture and parameters. MaskDGA was evaluated using the DMD-2018 dataset of AGD names and four recently published DGA classifiers, in which the average F1-score of the classifiers degrades from 0.977 to 0.495 when applying the evasion technique. An additional evaluation was conducted using the same classifiers but with adversarial defenses implemented: adversarial re-training and distillation. The results of this evaluation show that MaskDGA can be used for improving the robustness of the character-level DGA classifiers against adversarial attacks, but that ideally DGA classifiers should incorporate additional features alongside character-level features that are demonstrated in this study to be vulnerable to adversarial attacks.
Unlexicalized Transition-based Discontinuous Constituency Parsing
Venue: arXiv cs.CL 2019
Year: 2019

Lexicalized parsing models are based on the assumptions that (i) constituents are organized around a lexical head (ii) bilexical statistics are crucial to solve ambiguities. In this paper, we introduce an unlexicalized transition-based parser for discontinuous constituency structures, based on a structure-label transition system and a bi-LSTM scoring system. We compare it to lexicalized parsing models in order to address the question of lexicalization in the context of discontinuous constituency parsing. Our experiments show that unlexicalized models systematically achieve higher results than lexicalized models, and provide additional empirical evidence that lexicalization is not necessary to achieve strong parsing results. Our best unlexicalized model sets a new state of the art on English and German discontinuous constituency treebanks. We further provide a per-phenomenon analysis of its errors on discontinuous constituents.
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transition matrix as well as the neural network parameters in an alternating manner until convergence. While we do not make any probabilistic assumption on the underlying clean data, our ICE resembles Expectation-Maximization (EM) with variational approximation, and it takes advantage of the property of N-DUDE being locally robust around the true channel. With extensive experiments on several radically different types of data, we show that the ICE equipped N-DUDE (dubbed as ICE-N-DUDE) can perform \emph{universally} well regardless of the uncertainties in both the channel and the clean source. Moreover, we show ICE-N-DUDE becomes extremely robust to its hyperparameters and significantly outperforms the strong baseline that can deal with the channel uncertainties for denoising, the widely used Baum-Welch (BW) algorithm for hidden Markov models (HMM).
Demystifying TCP Initial Window Configurations of Content Distribution Networks
Venue: arXiv cs.NI 2019
Year: 2019

Driven by their quest to improve web performance, Content Delivery Networks (CDNs) are known adaptors of performance optimizations. In this regard, TCP congestion control and particularly its initial congestion window (IW) size is one long-debated topic that can influence CDN performance. Its size is, however, assumed to be static by IETF recommendations---despite being network- and application-dependent---and only infrequently changed in its history. To understand if the standardization and research perspective still meets Internet reality, we study the IW configurations of major CDNs. Our study uses a globally distributed infrastructure of VPNs giving access to residential access links that enable to shed light on network-dependent configurations. We observe that most CDNs are well aware of the IW's impact and find a high amount of customization that is beyond current Internet standards. Further, we find CDNs that utilize different IWs for different customers and content while others resort to fixed values. We find various initial window configurations, most below 50 segments yet with exceptions of up to 100 segments---the tenfold of current standards. Our study highlights that Internet reality drifted away from recommended and standardized practices.
Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.
EUI-64 Considered Harmful
Venue: arXiv cs.NI 2019
Year: 2019

This position paper considers the privacy and security implications of EUI-64-based IPv6 addresses. By encoding MAC addresses, EUI-64 addresses violate layers by exposing hardware identifiers in IPv6 addresses. The hypothetical threat of EUI-64 addresses is well-known, and the adoption of privacy extensions in operating systems (OSes) suggests this vulnerability has been mitigated. Instead, our work seeks to quantify the empirical existence of EUI-64 IPv6 addresses in today's Internet. By analyzing: i) traceroutes; ii) DNS records; and iii) mobile phone behaviors, we find surprisingly significant use of EUI-64. We characterize the origins and behaviors of these EUI-64 IPv6 addresses, and advocate for changes in provider IPv6 addressing policies.
The Capacity of Memoryless Channels with Sampled Cyclostationary Gaussian Noise
Venue: arXiv cs.IT 2019
Year: 2019

Non-orthogonal communications play an important role in future communication architectures. In such scenarios, the received signal is corrupted by an interfering signal which is typically cyclostationary in continuous-time. If the period of interference is synchronized with the sampling period, the sampled interference is a discrete-time (DT) cyclostationary process. However, in the common interference scenario, the period of the interference is not necessarily synchronized with the sampling period. In such cases, the DT interference is an almost cyclostationary process. In this work we characterize the capacity of channels in which the noise arises from a sampled cyclostationary Gaussian process. For the case of synchronous sampling, capacity can be obtained in closed form. When sampling is not synchronized with the symbol rate of the interference, the resulting channel is not information stable, thus classic tools are not applicable. Using information spectrum methods, we prove that capacity can be obtained as the limit of a sequence of capacities of channels with cyclostationary noise. Our results characterize the effects of changes in the sampling rate and sampling time offset on the capacity of the resulting DT channel. In particular, we demonstrate that minor variations in the sampling period can substantially affect capacity.
A Review of Routing Protocol Selection for Wireless Sensor Networks in Smart Cities
Venue: arXiv cs.NI 2019
Year: 2019

Today, the advancements in urban technology have transformed into the concept of smart cities. These smart cities are envisioned to be heavily dependent on wireless sensor networks and internet of things. In this context, a number of routing protocols have been proposed in literature for use in sensor networks. We articulate on why these routing protocols need to be segregated on the basis of their operational mechanism and utility, so that selection of these protocols results in network longevity and improved performance. We classify these protocols in four categories in terms of topology incognizant, data centric, location assisted and mobility based protocols. We identify the prevailing open issues to make space for more productive research and propose how these categories may be useful in terms of their operational utility.
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds
Venue: arXiv cs.CL 2019
Year: 2019

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.
Small World with High Risks: A Study of Security Threats in the npm Ecosystem
Venue: arXiv cs.CR 2019
Year: 2019

The popularity of JavaScript has lead to a large ecosystem of third-party packages available via the npm software package registry. The open nature of npm has boosted its growth, providing over 800,000 free and reusable software packages. Unfortunately, this open nature also causes security risks, as evidenced by recent incidents of single packages that broke or attacked software running on millions of computers. This paper studies security risks for users of npm by systematically analyzing dependencies between packages, the maintainers responsible for these packages, and publicly reported security issues. Studying the potential for running vulnerable or malicious code due to third-party dependencies, we find that individual packages could impact large parts of the entire ecosystem. Moreover, a very small number of maintainer accounts could be used to inject malicious code into the majority of all packages, a problem that has been increasing over time. Studying the potential for accidentally using vulnerable code, we find that lack of maintenance causes many packages to depend on vulnerable code, even years after a vulnerability has become public. Our results provide evidence that npm suffers from single points of failure and that unmaintained packages threaten large code bases. We discuss several mitigation techniques, such as trusted maintainers and total first-party security, and analyze their potential effectiveness.
Near-optimal demand side management in retail electricity markets with coupling constraints via indirect mechanism design
Venue: arXiv cs.GT 2019
Year: 2019

Recently there have been several historical changes in electricity networks that necessitate the development of Demand Side Management (DSM). The main objective of DSM is to achieve an aggregated consumption pattern that is efficient in terms of energy cost reduction, welfare maximization and satisfaction of network constraints. This is generally envisaged by encouraging electricity use at low-peak times. In this paper, we consider a system with strategic, price anticipating consumers with private preferences that choose their electricity consumption patterns so as to maximize their own benefit. In this context, we take on the problem of coordinating the consumers' consumption behavior without sacrificing their welfare (Quality of Experience). In order to tackle this problem, we draw on concepts of indirect mechanism design and propose a DSM architecture that is able to fulfill specific system-wide constraints (e.g. energy cost reduction) and simultaneously achieve welfare that is very close to optimal. The proposed billing rule preserves both the budget-balance and the individual rationality properties. According to our evaluation, the proposed DSM architecture achieves a close to optimal allocation (1%-3% gap), compared to an "optimal" system that would use central optimization of user loads without user consensus or protection of their privacy
Multi-Label Network Classification via Weighted Personalized Factorizations
Venue: arXiv cs.LG 2019
Year: 2019

Multi-label network classification is a well-known task that is being used in a wide variety of web-based and non-web-based domains. It can be formalized as a multi-relational learning task for predicting nodes labels based on their relations within the network. In sparse networks, this prediction task can be very challenging when only implicit feedback information is available such as in predicting user interests in social networks. Current approaches rely on learning per-node latent representations by utilizing the network structure, however, implicit feedback relations are naturally sparse and contain only positive observed feedbacks which mean that these approaches will treat all observed relations as equally important. This is not necessarily the case in real-world scenarios as implicit relations might have semantic weights which reflect the strength of those relations. If those weights can be approximated, the models can be trained to differentiate between strong and weak relations. In this paper, we propose a weighted personalized two-stage multi-relational matrix factorization model with Bayesian personalized ranking loss for network classification that utilizes basic transitive node similarity function for weighting implicit feedback relations. Experiments show that the proposed model significantly outperforms the state-of-art models on three different real-world web-based datasets and a biology-based dataset.