OpenRaiser/Intern-Atlas huggingface dataset:
Minimal linear codes from characteristic functions
Venue: arXiv cs.IT 2019
Year: 2019

Minimal linear codes have interesting applications in secret sharing schemes and secure two-party computation. This paper uses characteristic functions of some subsets of $\mathbb{F}_q$ to construct minimal linear codes. By properties of characteristic functions, we can obtain more minimal binary linear codes from known minimal binary linear codes, which generalizes results of Ding et al. [IEEE Trans. Inf. Theory, vol. 64, no. 10, pp. 6536-6545, 2018]. By characteristic functions corresponding to some subspaces of $\mathbb{F}_q$, we obtain many minimal linear codes, which generalizes results of [IEEE Trans. Inf. Theory, vol. 64, no. 10, pp. 6536-6545, 2018] and [IEEE Trans. Inf. Theory, vol. 65, no. 11, pp. 7067-7078, 2019]. Finally, we use characteristic functions to present a characterization of minimal linear codes from the defining set method and present a class of minimal linear codes.
Characterising complex healthcare systems using network science: The small world of emergency surgery
Venue: arXiv cs.SI 2019
Year: 2019

Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Nevertheless, metrics of performance have to date focused on the performance of individual elements rather than the system as a whole. Manipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counter-intuitive outcomes and a holistic metric of hospital function might help design more efficient services. We aimed to characterise the system of peri-operative care for emergency surgical admissions in our tertiary care hospital using network analysis. We used retrospective electronic health record data to construct a weighted directional network of the system. For this we selected all unplanned admissions during a 3.5 year period involving a surgical intervention during the inpatient stay and obtained a set of 16,500 individual inpatient episodes. We then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics. The analysis showed the service to be a complex system with scale-free, small-world network properties. This finding has implications for the structure and resilience of the service as such networks, whilst being robust in general, may be vulnerable to outages at specific key nodes. We also identified such potential hubs and bottlenecks in the system based on a variety of network measures. It is hoped that such a holistic, system-wide description of a hospital service may provide better metrics for hospital strain and serve to help planners engineer systems that are as robust as possible to external shocks.
On the Energy and Data Storage Management in Energy Harvesting Wireless Communications
Venue: arXiv cs.IT 2019
Year: 2019

Energy harvesting (EH) in wireless communications has become the focus of recent transmission technology studies. Herein, energy storage modeling is one of the crucial design benchmarks that must be treated carefully. Understanding the energy storage dynamics and the throughput levels is essential especially for communication systems in which the performance depends solely on harvested energy. While energy outages should be avoided, energy overflows should also be prevented in order to utilize all harvested energy. Hence, a simple, yet comprehensive, analytical model that can represent the characteristics of a general class of EH wireless communication systems needs to be established. In this paper, invoking tools from large deviation theory along with Markov processes, a firm connection between the energy state of the battery and the data transmission process over a wireless channel is established for an EH transmitter. In particular, a simple exponential approximation for the energy overflow probability is formulated, with which the energy decay rate in the battery as a measure of energy usage is characterized. Then, projecting the energy outages and supplies on a Markov process, a discrete state model is established and an expression for the energy outage probability for given energy arrival and demand processes is provided. Finally, under energy overflow and outage constraints, the average data service (transmission) rate over the wireless channel is obtained and the effective capacity of the system, which characterizes the maximum data arrival rate at the transmitter buffer under quality-of-service (QoS) constraints imposed on the data buffer overflow probability, is derived.
Rendering Non-Euclidean Geometry in Real-Time Using Spherical and Hyperbolic Trigonometry
Venue: arXiv cs.GR 2019
Year: 2019

This paper introduces a method of calculating and rendering shapes in a non-Euclidean 2D space. In order to achieve this, we developed a physics and graphics engine that uses hyperbolic trigonometry to calculate and subsequently render the shapes in a 2D space of constant negative or positive curvature in real-time. We have chosen to use polar coordinates to record the parameters of the objects as well as an azimuthal equidistant projection to render the space onto the screen because of the multiple useful properties they have. For example, polar coordinate system works well with trigonometric calculations, due to the distance from the reference point (analogous to origin in Cartesian coordinates) being one of the coordinates by definition. Azimuthal equidistant projection is not a typical projection, used for neither spherical nor hyperbolic space, however one of the main features of our engine relies on it: changing the curvature of the world in real-time without stopping the execution of the application in order to re-calculate the world. This is due to the projection properties that work identically for both spherical and hyperbolic space, as can be seen in the Figure 1 above. We will also be looking at the complexity analysis of this method as well as renderings that the engine produces. Finally we will be discussing the limitations and possible applications of the created engine as well as potential improvements of the described method.
Performance analysis of IEEE 802.11ax heterogeneous network in the presence of hidden terminals
Venue: arXiv cs.NI 2019
Year: 2019

Performance improvement has been among the foci of all previous amendments of IEEE 802.11 protocol. In addition, the draft high efficiency (HE) amendment IEEE 802.11ax, proposed by TGax, aims at increasing network performance. One of the main obstacles to improving spectral and power efficiency is the presence of hidden terminals which degrade throughput, in particular in uplink transmission. IEEE 802.11ax does provide mechanisms such as trigger based uplink transmission that mitigate this degradation to some extent, but are incapable of eliminating it, esp. at high arrival rates. To combat the hidden terminal problem, we propose to increase the carrier sensing threshold (CSTH) of STAs during association with an HE access point. Our results confirm that the proposed mechanism can lead to significant reduction of collision probability in uplink transmission.
Local versus Global Strategies in Social Query Expansion
Venue: arXiv cs.IR 2019
Year: 2019

Link sharing in social media can be seen as a collaboratively retrieved set of documents for a query or topic expressed by a hashtag. Temporal information plays an important role for identifying the correct context for which such annotations are valid for retrieval purposes. We investigate how social data as temporal context can be used for query expansion and compare global versus local strategies for computing such contextual information for a set of hashtags.
Attention Control with Metric Learning Alignment for Image Set-based Recognition
Venue: arXiv cs.CV 2019
Year: 2019

This paper considers the problem of image set-based face verification and identification. Unlike traditional single sample (an image or a video) setting, this situation assumes the availability of a set of heterogeneous collection of orderless images and videos. The samples can be taken at different check points, different identity documents $etc$. The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set. How to model the relationship of orderless images within a set remains a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) in a latent space. Specifically, we first propose a dependency-aware attention control (DAC) network, which uses actor-critic reinforcement learning for attention decision of each image to exploit the correlations among the unordered images. An off-policy experience replay is introduced to speed up the learning process. Moreover, the DAC is combined with a temporal model for videos using divide and conquer strategies. We also introduce a pose-guided representation (PGR) scheme that can further boost the performance at extreme poses. We propose a parameter-free PGR without the need for training as well as a novel metric learning-based PGR for pose alignment without the need for pose detection in testing stage. Extensive evaluations on IJB-A/B/C, YTF, Celebrity-1000 datasets demonstrate that our method outperforms many state-of-art approaches on the set-based as well as video-based face recognition databases.
Animal Wildlife Population Estimation Using Social Media Images Collections
Venue: arXiv cs.LG 2019
Year: 2019

We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches. Here, we present a new framework to take into account the social media bias when using this data source to provide wildlife population size estimates. We show that, surprisingly, this is a learnable and potentially solvable problem.
On the security of ballot marking devices
Venue: arXiv cs.CR 2019
Year: 2019

A recent debate among election experts has considered whether electronic ballot marking devices (BMDs) have adequate security against the risks of malware. A malicious BMD might produce a printed ballot that disagrees with a voter's actual intent, with the hope that voters would be unlikely to detect this subterfuge. This essay considers how an election administrator can create reasonable auditing procedures to gain confidence that their fleet of BMDs is operating correctly, allowing voters to benefit from the usability and accessibility features of BMDs while the overall election still benefits from the same security and reliability properties we expect from hand-marked paper ballots.
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
Venue: arXiv cs.CV 2019
Year: 2019

The importance of wild video based image set recognition is becoming monotonically increasing. However, the contents of these collected videos are often complicated, and how to efficiently perform set modeling and feature extraction is a big challenge for set-based classification algorithms. In recent years, some proposed image set classification methods have made a considerable advance by modeling the original image set with covariance matrix, linear subspace, or Gaussian distribution. As a matter of fact, most of them just adopt a single geometric model to describe each given image set, which may lose some other useful information for classification. To tackle this problem, we propose a novel algorithm to model each image set from a multi-geometric perspective. Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied for set representation simultaneously. In order to fuse these multiple heterogeneous Riemannian manifoldvalued features, the well-equipped Riemannian kernel functions are first utilized to map them into high dimensional Hilbert spaces. Then, a multi-kernel metric learning framework is devised to embed the learned hybrid kernels into a lower dimensional common subspace for classification. We conduct experiments on four widely used datasets corresponding to four different classification tasks: video-based face recognition, set-based object categorization, video-based emotion recognition, and dynamic scene classification, to evaluate the classification performance of the proposed algorithm. Extensive experimental results justify its superiority over the state-of-the-art.
RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual Query
Venue: arXiv cs.DB 2019
Year: 2019

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.
Wait-Free Universality of Consensus in the Infinite Arrival Model
Venue: arXiv cs.DC 2019
Year: 2019

In classical asynchronous distributed systems composed of a fixed number n of processes where some proportion may fail by crashing, many objects do not have a wait-free linearizable implementation (e.g. stacks, queues, etc.). It has been proved that consensus is universal in such systems, which means that this system augmented with consensus objects allows to implement any object that has a sequential specification. To this end, many universal constructions have been proposed in systems augmented with consensus objects or with different equivalent objects or special hardware instructions (compare&swap, fetch&add, etc.). In this paper, we consider a more general system model called infinite arrival model where infinitely many processes may arrive and leave or crash during a run. We prove that consensus is still universal in this more general model. For that, we propose a universal construction. As a first step we build a weak log for which we propose two implementations using consensus objects for the first and the compare&swap special instruction for the other.
Fast Fourier Color Constancy and Grayness Index for ISPA Illumination Estimation Challenge
Venue: arXiv cs.CV 2019
Year: 2019

We briefly introduce two submissions to the Illumination Estimation Challenge, in the Int'l Workshop on Color Vision, affiliated to the 11th Int'l Symposium on Image and Signal Processing and Analysis. The Fourier-transform-based submission is ranked 3rd, and the statistical Gray-pixel-based one ranked 6th.
A State Class Construction for Computing the Intersection of Time Petri Nets Languages
Venue: arXiv cs.LO 2019
Year: 2019

We propose a new method for computing the language intersection of two Time Petri nets (TPN); that is the sequence of labels in timed traces common to the execution of two TPN. Our approach is based on a new product construction between nets and relies on the State Class construction, a widely used method for checking the behaviour of TPN. We prove that this new construct does not add additional expressive power, and yet that it can leads to very concise representation of the result. We have implemented our approach in a new tool, called Twina. We report on some experimental results obtained with this tool and show how to apply our approach on two interesting problems: rst, to dene an equivalent of the twin-plant diagnosability methods for TPN; then as a way to check timed properties without interfering with a system.
What do the founders of online communities owe to their users?
Venue: arXiv cs.CY 2019
Year: 2019

We discuss the organisation of internet communities, focusing on what we call the principle of "bait and switch": founders of internet communities often find it advantageous to recruit members by promising inducements which are later not honoured. We look at some of the dilemmas and ways of attempting to resolve them through two paradigmatic examples, Wikispaces and Wordpress. Our analysis is to a large extent motivated by the demands of CALLector, a university-centred social network we are in the process of establishing. We consider the question of what ethical standards are imposed on universities engaged in this type of activity.
After You, Please: Browser Extensions Order Attacks and Countermeasures
Venue: arXiv cs.CR 2019
Year: 2019

Browser extensions are small applications executed in the browser context that provide additional capabilities and enrich the user experience while surfing the web. The acceptance of extensions in current browsers is unquestionable. For instance, Chrome's official extension repository has more than 63,000 extensions, with some of them having more than 10M users. When installed, extensions are pushed into an internal queue within the browser. The order in which each extension executes depends on a number of factors, including their relative installation times. In this paper, we demonstrate how this order can be exploited by an unprivileged malicious extension (i.e., one with no more permissions than those already assigned when accessing web content) to get access to any private information that other extensions have previously introduced. Our solution does not require modifying the core browser engine as it is implemented as another browser extension. We prove that our approach effectively protects the user against usual attackers (i.e., any other installed extension) as well as against strong attackers having access to the effects of all installed extensions (i.e., knowing who did what). We also prove soundness and robustness of our approach under reasonable assumptions.
Knapsack Problem variants of QAOA for battery revenue optimisation
Venue: arXiv cs.ET 2019
Year: 2019

We implement two Quantum Approximate Optimisation Algorithm (QAOA) variants for a battery revenue optimisation problem, equivalent to the weakly NP-hard Knapsack Problem. Both approaches investigate how to tackle constrained problems with QAOA. A first 'constrained' approach introduces a quadratic penalty to enforce the constraint to be respected strictly and reformulates the problem into an Ising Problem. However, simulations on IBM's simulator highlight non-convergent results for intermediate depth ($ p\leq 50$). A second 'relaxed' approach applies the QAOA with a non-Ising target function to compute a linear penalty, running in time $O(p(\log_2 n)^3)$ and needing $O(n \log n)$ qubits. Simulations reveal an exponential improvement over the number of depth levels and obtain approximations about $0.95$ of the optimum with shallow depth ($p \leq 10$).
On cylindrical regression in three-dimensional Euclidean space
Venue: arXiv cs.CG 2019
Year: 2019

The three-dimensional cylindrical regression problem is a problem of finding a cylinder best fitting a group of points in three-dimensional Euclidean space. The words best fitting are usually understood in the sense of the minimum root mean square deflection of the given points from a cylinder to be found. In this form the problem has no analytic solution. If one replaces the root mean square averaging by a certain biquadratic averaging, the resulting problem has an almost analytic solution. This solution is reproduced in the present paper in a coordinate-free form.
Preliminary design of a device to assist handwriting in children with movement disorders
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents the development of a new passive assistive handwriting device, which aims to stabilize the motion of people living with movement disorders. Many people living with conditions such as cerebral palsy, stroke, muscular dystrophy or dystonia experience upper limbs impairments (muscle spasticity, unselective motor control, muscle weakness or tremors) and are unable to write or draw on their own. The proposed device is designed to be fixed on a table. A pen is attached to the device using a pen holder, which maintains the pen in a fixed orientation. The user interacts with the device using a handle while mechanical dampers and inertia contribute to the stabilization of the user's movements. The overall mechanical design of the device is first presented, followed by the design of the pen holder mechanism.
Who's Tracking Sensitive Domains?
Venue: arXiv cs.CR 2019
Year: 2019

We turn our attention to the elephant in the room of data protection, which is none other than the simple and obvious question: "Who's tracking sensitive domains?". Despite a fast-growing amount of work on more complex facets of the interplay between privacy and the business models of the Web, the obvious question of who collects data on domains where most people would prefer not be seen, has received rather limited attention. First, we develop a methodology for automatically annotating websites that belong to a sensitive category, e.g. as defined by the General Data Protection Regulation (GDPR). Then, we extract the third party tracking services included directly, or via recursive inclusions, by the above mentioned sites. Having analyzed around 30k sensitive domains, we show that such domains are tracked, albeit less intensely than the mainstream ones. Looking in detail at the tracking services operating on them, we find well known names, as well as some less known ones, including some specializing on specific sensitive categories.
Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process?
Venue: arXiv cs.SE 2019
Year: 2019

Developer contribution guidelines are used in social coding sites like GitHub to explain and shape the process a project expects contributors to follow. They set standards for all participants and "save time and hassle caused by improperly created pull requests or issues that have to be rejected and resubmitted" (GitHub). Yet, we lack a systematic understanding of the content of a typical contribution guideline, as well as the extent to which these guidelines are followed in practice. Additionally, understanding how guidelines may impact projects that use Continuous Integration as part of the contribution process is of particular interest. To address this knowledge gap, we conducted a mixed-methods study of 53 GitHub projects with explicit contribution guidelines and coded the guidelines to extract key themes. We then created a process model using GitHub activity data (e.g., commit, new issue, new pull request) to compare the actual activity with the prescribed contribution guidelines. We show that approximately 68% of these projects diverge significantly from the expected process.