OpenRaiser/Intern-Atlas huggingface dataset:
Pandemic News: Facebook Pages of Mainstream News Media and the Coronavirus Crisis -- A Computational Content Analysis
Venue: arXiv cs.SI 2020
Year: 2020

The unfolding of the COVID-19 pandemic has been an unprecedented challenge for news media around the globe. While journalism is meant to process yet unknown events by design, the dynamically evolving situation affected all aspects of life in such profound ways that even the routines of crisis reporting seemed to be insufficient. Critics noted tendencies to horse-race reporting and uncritical coverage, with journalism being too close to official statements and too affirmative of political decisions. However, empirical data on the performance of journalistic news media during the crisis has been lacking thus far. The current study analyzes the Facebook messages of journalistic news media during the early Coronavirus crisis, based on a large German data set from January to March 2020. Using computational content analysis methods, reach and interactions, topical structure, relevant actors, negativity of messages, as well as the coverage of fabricated news and conspiracy theories were examined. The topical structure of the near-time Facebook coverage changed during various stages of the crisis, with just partial support for the claims of critics. The initial stages were somewhat lacking in topical breadth, but later stages offered a broad range of coverage on Corona-related issues and societal concerns. Further, journalistic media covered fake news and conspiracy theories during the crisis, but they consistently contextualized them as what they were and debunked the false claims circulating in public. While some criticism regarding the performance of journalism during the crisis received mild empirical support, the analysis did not find overwhelming signs of systemic dysfunctionalities. Overall, journalistic media did not default to a uniform reaction nor to sprawling, information-poor pandemic news, but they responded with a multi-perspective coverage of the crisis.
Towards an Open Platform for Legal Information
Venue: arXiv cs.DL 2020
Year: 2020

Recent advances in the area of legal information systems have led to a variety of applications that promise support in processing and accessing legal documents. Unfortunately, these applications have various limitations, e.g., regarding scope or extensibility. Furthermore, we do not observe a trend towards open access in digital libraries in the legal domain as we observe in other domains, e.g., economics of computer science. To improve open access in the legal domain, we present our approach for an open source platform to transparently process and access Legal Open Data. This enables the sustainable development of legal applications by offering a single technology stack. Moreover, the approach facilitates the development and deployment of new technologies. As proof of concept, we implemented six technologies and generated metadata for more than 250,000 German laws and court decisions. Thus, we can provide users of our platform not only access to legal documents, but also the contained information.
Downlink Asynchronous Non-Orthogonal Multiple Access with Quantizer Optimization
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we study a two-user downlink asynchronous non-orthogonal multiple access (ANOMA) with limited feedback. We employ the max-min criterion for the power allocation and derive the closed-form expressions for the upper and lower bounds of the max-min rate. It is demonstrated that ANOMA can achieve the same or even higher average maxmin rate with a lower feedback rate compared with NOMA. Moreover, we propose a quantizer optimization algorithm which applies to both NOMA and ANOMA. Simulation results show that the optimized quantizer significantly improves the average max-min rate compared with the conventional uniform quantizer, especially in the scenario with a low feedback rate.
Compounding Injustice: History and Prediction in Carceral Decision-Making
Venue: arXiv cs.CY 2020
Year: 2020

Risk assessment algorithms in criminal justice put people's lives at the discretion of a simple statistical tool. This thesis explores how algorithmic decision-making in criminal policy can exhibit feedback effects, where disadvantage accumulates among those deemed 'high risk' by the state. Evidence from Philadelphia suggests that risk - and, by extension, criminality - is not fundamental or in any way exogenous to political decision-making. A close look at the geographical and demographic properties of risk calls into question the current practice of prediction in criminal policy. Using court docket summaries from Philadelphia, we find evidence of a criminogenic effect of incarceration, even controlling for existing determinants of 'criminal risk'. With evidence that criminal treatment can influence future criminal convictions, we explore the theoretical implications of compounding effects in repeated carceral decisions.
Thirty Musts for Meaning Banking
Venue: arXiv cs.CL 2020
Year: 2020

Meaning banking--creating a semantically annotated corpus for the purpose of semantic parsing or generation--is a challenging task. It is quite simple to come up with a complex meaning representation, but it is hard to design a simple meaning representation that captures many nuances of meaning. This paper lists some lessons learned in nearly ten years of meaning annotation during the development of the Groningen Meaning Bank (Bos et al., 2017) and the Parallel Meaning Bank (Abzianidze et al., 2017). The paper's format is rather unconventional: there is no explicit related work, no methodology section, no results, and no discussion (and the current snippet is not an abstract but actually an introductory preface). Instead, its structure is inspired by work of Traum (2000) and Bender (2013). The list starts with a brief overview of the existing meaning banks (Section 1) and the rest of the items are roughly divided into three groups: corpus collection (Section 2 and 3, annotation methods (Section 4-11), and design of meaning representations (Section 12-30). We hope this overview will give inspiration and guidance in creating improved meaning banks in the future.
HPC compact quasi-Newton algorithm for interface problems
Venue: arXiv cs.CE 2020
Year: 2020

In this work we present a robust interface coupling algorithm called Compact Interface quasi-Newton (CIQN). It is designed for computationally intensive applications using an MPI multi-code partitioned scheme. The algorithm allows to reuse information from previous time steps, feature that has been previously proposed to accelerate convergence. Through algebraic manipulation, an efficient usage of the computational resources is achieved by: avoiding construction of dense matrices and reduce every multiplication to a matrix-vector product and reusing the computationally expensive loops. This leads to a compact version of the original quasi-Newton algorithm. Altogether with an efficient communication, in this paper we show an efficient scalability up to 4800 cores. Three examples with qualitatively different dynamics are shown to prove that the algorithm can efficiently deal with added mass instability and two-field coupled problems. We also show how reusing histories and filtering does not necessarily makes a more robust scheme and, finally, we prove the necessity of this HPC version of the algorithm. The novelty of this article lies in the HPC focused implementation of the algorithm, detailing how to fuse and combine the composing blocks to obtain an scalable MPI implementation. Such an implementation is mandatory in large scale cases, for which the contact surface cannot be stored in a single computational node, or the number of contact nodes is not negligible compared with the size of the domain. \c{opyright} <2020> Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Github Data Exposure and Accessing Blocked Data using the GraphQL Security Design Flaw
Venue: arXiv cs.CR 2020
Year: 2020

This research study was conducted to illustrate how it is easily possible to get data access to disabled or blocked repositories in Github using GraphQL. There are situations in which you can lose access to your Github repositories; When you use the paid version of Github services and do not pay the monthly payment or another situation is that when you use Github from the countries in the United States sanction list. Having an insecure repository with malicious usages can also put your repository in Github blacklist. In all of these situations, Github will block and disable your repository and you will lose access to your files, codes and project assets. Here, we will discuss the procedure of how an Ethical Hacker can gain access to all those blocked data with GraphQL functionality.
In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology
Venue: arXiv cs.CL 2020
Year: 2020

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in a multilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model outperforms the other two in terms of accuracy, but the Sigmoid model's language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research.
Limitations on counting in Boolean circuits and self-assembly
Venue: arXiv cs.ET 2020
Year: 2020

In self-assembly, a $k$-counter is a tile set that grows a horizontal ruler from left to right, containing $k$ columns each of which encodes a distinct binary string. Counters have been fundamental objects of study in a wide range of theoretical models of tile assembly, molecular robotics and thermodynamics-based self-assembly due to their construction capabilities using few tile types, time-efficiency of growth and combinatorial structure. Here, we define a Boolean circuit model, called $n$-wire local railway circuits, where $n$ parallel wires are straddled by Boolean gates, each with matching fanin/fanout strictly less than $n$, and we show that such a model can not count to $2^n$ nor implement any so-called odd bijective nor quasi-bijective function. We then define a class of self-assembly systems that includes theoretically interesting and experimentally-implemented systems that compute $n$-bit functions and count layer-by-layer. We apply our Boolean circuit result to show that those self-assembly systems can not count to $2^n$. This explains why the experimentally implemented iterated Boolean circuit model of tile assembly can not count to $2^n$, yet some previously studied tile system do. Our work points the way to understanding the kinds of features required from self-assembly and Boolean circuits to implement maximal counters.
Eye Gaze Controlled Interfaces for Head Mounted and Multi-Functional Displays in Military Aviation Environment
Venue: arXiv cs.HC 2020
Year: 2020

Eye gaze controlled interfaces allow us to directly manipulate a graphical user interface just by looking at it. This technology has great potential in military aviation, in particular, operating different displays in situations where pilots hands are occupied with flying the aircraft. This paper reports studies on analyzing accuracy of eye gaze controlled interface inside aircraft undertaking representative flying missions. We reported that pilots can undertake representative pointing and selection tasks at less than 2 secs on average. Further, we evaluated the accuracy of eye gaze tracking glass under various G-conditions and analyzed its failure modes. We observed that the accuracy of an eye tracker is less than 5 degree of visual angle up to +3G, although it is less accurate at minus 1G and plus 5G. We observed that eye tracker may fail to track under higher external illumination. We also infer that an eye tracker to be used in military aviation need to have larger vertical field of view than the present available systems. We used this analysis to develop eye gaze trackers for Multi-Functional displays and Head Mounted Display System. We obtained significant reduction in pointing and selection times using our proposed HMDS system compared to traditional TDS.
D2D: Keypoint Extraction with Describe to Detect Approach
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations. Detect then describe, or detect and describe jointly are two typical strategies for extracting local descriptors. In contrast, we propose an approach that inverts this process by first describing and then detecting the keypoint locations. % Describe-to-Detect (D2D) leverages successful descriptor models without the need for any additional training. Our method selects keypoints as salient locations with high information content which is defined by the descriptors rather than some independent operators. We perform experiments on multiple benchmarks including image matching, camera localisation, and 3D reconstruction. The results indicate that our method improves the matching performance of various descriptors and that it generalises across methods and tasks.
Looking back to lower-level information in few-shot learning
Venue: arXiv cs.LG 2020
Year: 2020

Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples. This challenging scenario is commonly known as few-shot learning. Few-shot learning has garnered increased attention in recent years due to its significance for many real-world problems. Recently, new methods relying on meta-learning paradigms combined with graph-based structures, which model the relationship between examples, have shown promising results on a variety of few-shot classification tasks. However, existing work on few-shot learning is only focused on the feature embeddings produced by the last layer of the neural network. In this work, we propose the utilization of lower-level, supporting information, namely the feature embeddings of the hidden neural network layers, to improve classifier accuracy. Based on a graph-based meta-learning framework, we develop a method called Looking-Back, where such lower-level information is used to construct additional graphs for label propagation in limited data settings. Our experiments on two popular few-shot learning datasets, miniImageNet and tieredImageNet, show that our method can utilize the lower-level information in the network to improve state-of-the-art classification performance.
In-depth Real-World Evaluation of NB-IoT Module Energy Consumption
Venue: arXiv cs.NI 2020
Year: 2020

Narrowband Internet of Things (NB-IoT) is a recent addition to the 3GPP standards offering low power wide area networking (LP-WAN) for a massive amount of IoT devices communicating at low data rates in the licensed bands. As the number of deployed NB-IoT devices worldwide increases, the question of energy-efficient real-world NB-IoT device and network operation that would maximize the device battery lifetime becomes crucial for NB-IoT service popularity and adoption. In this paper, we present a detailed energy consumption of an NB-IoT module obtained from a custom-designed NB-IoT platform from which a large amount of high time-resolution data is collected. We perform a detailed energy consumption analysis of each NB-IoT data transfer phase and discuss both the device and the network-side configurations that may affect the module energy consumption in each of the phases. Preliminary results and conclusions are put forth along with the discussion of ongoing and future study plans.
Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach
Venue: arXiv cs.RO 2020
Year: 2020

Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions that may hinder exteroceptive sensors such as a camera or a laser ranger finder. These methods depend on proprioceptive sensors such as inertial measurement units (IMUs) and/or wheel encoders. Assisted by magnetoreception, the sensors can provide a rudimentary estimation of vehicle trajectory which is used to query a prior known map to obtain location. Named as graph-based proprioceptive localization (GBPL), we provide a low cost fallback solution for localization under challenging environmental conditions. As a robot/vehicle travels, we extract a sequence of heading-length values for straight segments from the trajectory and match the sequence with a pre-processed heading-length graph (HLG) abstracted from the prior known map to localize the robot under a graph-matching approach. Using the information from HLG, our location alignment and verification module compensates for trajectory drift, wheel slip, or tire inflation level. We have implemented our algorithm and tested it in both simulated and physical experiments. The algorithm runs successfully in finding robot location continuously and achieves localization accurate at the level that the prior map allows (less than 10m).
List Decoding of Arikan's PAC Codes
Venue: arXiv cs.IT 2020
Year: 2020

Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. However, its performance at short block lengths is far from optimal. Arikan has recently presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. Such PAC codes provide dramatic improvement in performance as compared to both standard successive-cancellation decoding as well as CRC-aided list decoding. Arikan's PAC codes are based primarily upon the following ideas: replacing CRC precoding with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. His simulations show that PAC codes, resulting from the combination of these ideas, are close to finite-length bounds on the performance of any code under ML decoding. One of our main goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size $L$ is moderately large (say, $L \ge 128$). List decoding has distinct advantages over sequential decoding is certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, using these estimates to approximate the union bound on their performance under ML decoding. These results indicate that PAC codes are superior to both polar codes and Reed-Muller codes, and suggest that the goal of rate-profiling may be to optimize the weight distribution at low weights.
L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
Venue: arXiv cs.CV 2020
Year: 2020

Images captured underwater often suffer from suboptimal illumination settings that can hide important visual features, reducing their quality. We present a novel single-image low-light underwater image enhancer, L^2UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information. We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. A multi-scale fusion process is employed to combine these images while emphasizing regions of higher luminance, saliency and local contrast. We demonstrate the performance of L^2UWE by using seven metrics to test it against seven state-of-the-art enhancement methods specific to underwater and low-light scenes. Code available at: https://github.com/tunai/l2uwe.
A Theory of Auto-Scaling for Resource Reservation in Cloud Services
Venue: arXiv cs.DC 2020
Year: 2020

We consider a distributed server system consisting of a large number of servers, each with limited capacity on multiple resources (CPU, memory, disk, etc.). Jobs with different rewards arrive over time and require certain amounts of resources for the duration of their service. When a job arrives, the system must decide whether to admit it or reject it, and if admitted, in which server to schedule the job. The objective is to maximize the expected total reward received by the system. This problem is motivated by control of cloud computing clusters, in which, jobs are requests for Virtual Machines or Containers that reserve resources for various services, and rewards represent service priority of requests or price paid per time unit of service by clients. We study this problem in an asymptotic regime where the number of servers and jobs' arrival rates scale by a factor $L$, as $L$ becomes large. We propose a resource reservation policy that asymptotically achieves at least $1/2$, and under certain monotone property on jobs' rewards and resources, at least $1-1/e$ of the optimal expected reward. The policy automatically scales the number of VM slots for each job type as the demand changes, and decides in which servers the slots should be created in advance, without the knowledge of traffic rates. It effectively tracks a low-complexity greedy packing of existing jobs in the system while maintaining only a small number, $g(L)=ω(\log L)$, of reserved VM slots for high priority jobs that pack well.
IoT-based Remote Control Study of a Robotic Trans-esophageal Ultrasound Probe via LAN and 5G
Venue: arXiv cs.RO 2020
Year: 2020

A robotic trans-esophageal echocardiography (TEE) probe has been recently developed to address the problems with manual control in the X-ray envi-ronment when a conventional probe is used for interventional procedure guidance. However, the robot was exclusively to be used in local areas and the effectiveness of remote control has not been scientifically tested. In this study, we implemented an Internet-of-things (IoT)-based configuration to the TEE robot so the system can set up a local area network (LAN) or be configured to connect to an internet cloud over 5G. To investigate the re-mote control, backlash hysteresis effects were measured and analysed. A joy-stick-based device and a button-based gamepad were then employed and compared with the manual control in a target reaching experiment for the two steering axes. The results indicated different hysteresis curves for the left-right and up-down steering axes with the input wheel's deadbands found to be 15 deg and deg, respectively. Similar magnitudes of positioning errors at approximately 0.5 deg and maximum overshoots at around 2.5 deg were found when manually and robotically controlling the TEE probe. The amount of time to finish the task indicated a better performance using the button-based gamepad over joystick-based device, although both were worse than the manual control. It is concluded that the IoT-based remote control of the TEE probe is feasible and a trained user can accurately manipulate the probe. The main identified problem was the backlash hysteresis in the steering axes, which can result in continuous oscillations and overshoots.