OpenRaiser/Intern-Atlas huggingface dataset:
An alternative analysis on the scientific output of Spanish Sociology What can altmetrics tell us?
Venue: arXiv cs.DL 2020
Year: 2020

In recent years, new indicators known as altmetrics have been introduced to measure the impact of scientific activity. These indicators are obtained through the mentions realised from different social media, existing several aggregators of these data that collect several of them in the same database, being Altmetric.com the most popular. However, in spite of the popularization of these metrics, several limitations in their use have been manifested. For this reason, rhe objective of this work is twofold: (1) to show the possibilities of altimetric techniques applied to the Spanish social sciences in general and sociology in particular; (2) to critically analyse the results to observe the limitations of these indicators; (3) to check whether they can really add useful information that can be used to describe a scientific field and (4) to see the reasons why altmetrics cannot be applied in these fields.
The "cardinality of extended solution set" criterion for establishing the intractability of NP problems
Venue: arXiv cs.CC 2020
Year: 2020

The intractability of any problem and the randomness of its solutions have an obvious intuitive connection. However, the challenge till now has been that there is no practical way to firmly establish if the solution to a problem is actually random (or whether it has some hidden undiscovered structure, which upon being detected would render it non-random). This has prevented the conclusive declaration of hard problems (such as NP) as being definitely intractable. For dealing with this, a concept called "extensibility" of a sequence is developed. Based on this, a criterion termed as "cardinality of extended solution set" is conceived to ascertain the (non)randomness of any sequence. Further, this can then be used to establish the (in)tractability of any problem depending on whether its solutions are random or non-random. This criterion is applied to problems such as 2-SAT, 3-SAT and hardness of approximation to analyze their (in)tractability. Finally, a proof for the validity of the Unique Games Conjecture based on the same criterion is also presented.
Trustless parallel local search for effective distributed algorithm discovery
Venue: arXiv cs.DC 2020
Year: 2020

Metaheuristic search strategies have proven their effectiveness against man-made solutions in various contexts. They are generally effective in local search area exploitation, and their overall performance is largely impacted by the balance between exploration and exploitation. Recent developments in parallel local search explore methods to take advantage of the efficient local exploitation of searches and reach impressive results. This however restricts the scaling potential to nodes within a private, trusted computer cluster. In this research we propose a novel blockchain protocol that allows parallel local search to scale to untrusted and anonymous computational nodes. The protocol introduces publicly verifiable performance evaluation of the local optima reported by each node, creating a competitive environment between the local searches. That is strengthened with economical stimuli for producing good solutions, that provide coordination between the nodes, as every node tries to explore different sections of the search space to beat their competition.
Hawkes Process Multi-armed Bandits for Disaster Search and Rescue
Venue: arXiv cs.LG 2020
Year: 2020

We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the tradeoff between exploiting geographic regions where data has been collected and exploring geographic regions where data is unobserved. We first validate our model using simulated data and then apply it to the problem of disaster search and rescue using calls for service data from hurricane Harvey in 2017. Our model outperforms the state of the art baseline spatial MAB algorithms in terms of cumulative reward and several other ranking evaluation metrics.
Using HEP experiment workflows for the benchmarking and accounting of WLCG computing resources
Venue: arXiv cs.PF 2020
Year: 2020

Benchmarking of CPU resources in WLCG has been based on the HEP-SPEC06 (HS06) suite for over a decade. It has recently become clear that HS06, which is based on real applications from non-HEP domains, no longer describes typical HEP workloads. The aim of the HEP-Benchmarks project is to develop a new benchmark suite for WLCG compute resources, based on real applications from the LHC experiments. By construction, these new benchmarks are thus guaranteed to have a score highly correlated to the throughputs of HEP applications, and a CPU usage pattern similar to theirs. Linux containers and the CernVM-FS filesystem are the two main technologies enabling this approach, which had been considered impossible in the past. In this paper, we review the motivation, implementation and outlook of the new benchmark suite.
Weighted Random Search for Hyperparameter Optimization
Venue: arXiv cs.LG 2020
Year: 2020

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem defined on a discrete domain.
User-Space Emulation Framework for Domain-Specific SoC Design
Venue: arXiv cs.DC 2020
Year: 2020

In this work, we propose a portable, Linux-based emulation framework to provide an ecosystem for hardware-software co-design of Domain-specific SoCs (DSSoCs) and enable their rapid evaluation during the pre-silicon design phase. This framework holistically targets three key challenges of DSSoC design: accelerator integration, resource management, and application development. We address these challenges via a flexible and lightweight user-space runtime environment that enables easy integration of new accelerators, scheduling heuristics, and user applications, and we illustrate the utility of each through various case studies. With signal processing (WiFi and RADAR) as the target domain, we use our framework to evaluate the performance of various dynamic workloads on hypothetical DSSoC hardware configurations composed of mixtures of CPU cores and FFT accelerators using a Zynq UltraScale+TM MPSoC. We show the portability of this framework by conducting a similar study on an Odroid platform composed of big.LITTLE ARM clusters. Finally, we introduce a prototype compilation toolchain that enables automatic mapping of unlabeled C code to DSSoC platforms. Taken together, this environment offers a unique ecosystem to rapidly perform functional verification and obtain performance and utilization estimates that help accelerate convergence towards a final DSSoC design.
An Approach for Selecting Cloud Service Adequate to Big Data Case Study: E-health Context
Venue: arXiv cs.DC 2020
Year: 2020

The expanding Cloud computing's services offers great opportunities for consumers to find the best service and best cost. It offers a computing power and a storage space adapted especially for Big Data processing. However, it raises new challenges on how to select the best service out of the huge pool. It is time-consuming for consumers to collect the necessary information and analyze all service providers to make the right decision. Moreover, it'is a highly demanding task from a computational perspective, because the same computations may be conducted repeatedly by multiple consumers who have similar requirements. Therefore, in this paper, we propose an approach based on Analytic Hierarchy Process (AHP) method, which manages the selection of the Cloud Service adequate to Big Data based on its parameters and criteria. We applied this approach on a case study in order to validate its efficity. The studied case is about the selection of the adequate Cloud Service for Big Data in the context of National Health Service (NHS) of United Kingdom (UK).
TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications
Venue: arXiv cs.DC 2020
Year: 2020

As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilience techniques (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the application's resilience. In this work, we present TensorFI, a high-level fault injection (FI) framework for TensorFlow-based applications. TensorFI is able to inject both hardware and software faults in general TensorFlow programs. TensorFI is a configurable FI tool that is flexible, easy to use, and portable. It can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., faults in particular operators). We use TensorFI to evaluate the resilience of 12 ML programs, including DNNs used in the autonomous vehicle domain. Our tool is publicly available at https://github.com/DependableSystemsLab/TensorFI.
Typosquatting for Fun and Profit: Cross-Country Analysis of Pop-Up Scam
Venue: arXiv cs.CR 2020
Year: 2020

Today, many different types of scams can be found on the internet. Online criminals are always finding new creative ways to trick internet users, be it in the form of lottery scams, downloading scam apps for smartphones or fake gambling websites. This paper presents a large-scale study on one particular delivery method of online scam: pop-up scam on typosquatting domains. Typosquatting describes the concept of registering domains which are very similar to existing ones while deliberately containing common typing errors; these domains are then used to trick online users while under the belief of browsing the intended website. Pop-up scam uses JavaScript alert boxes to present a message which attracts the user's attention very effectively, as they are a blocking user interface element. Our study among typosquatting domains derived from the Majestic Million list utilising an Austrian IP address revealed on 1219 distinct typosquatting URLs a total of 2577 pop-up messages, out of which 1538 were malicious. Approximately a third of those distinct URLs (403) were targeted and displayed pop-up messages to one specific HTTP user agent only. Based on our scans, we present an in-depth analysis as well as a detailed classification of different targeting parameters (user agent and language) which triggered varying kinds of pop-up scams. Furthermore, we expound the differences of current pop-up scam characteristics in comparison with a previous scan performed in late 2018 and examine the use of IDN homograph attacks as well as the application of message localisation using additional scans with IP addresses from the United States and Japan.
Max-Min Fair Beamforming for Cooperative Multigroup Multicasting with Rate-Splitting
Venue: arXiv cs.IT 2020
Year: 2020

The demand of massive access to the same multimedia content at the same time is one major challenge for next-generation cellular networks in densely-packed urban areas. The content-aware multicast transmission strategies provide promising solutions to such use cases involving many mobile users trying to fetch the same data. In this work, we consider multigroup multicast transmission with a common message (e.g., multimedia content), in which different multicast groups are interested along with their private multicast messages. We further assume that a relay helps the cellular base station (BS) disseminate multicast content to the users experiencing high path loss and/or blockage. We propose superposition and concatenated coding schemes, denoted by SC and CC, respectively, to transmit the common and private multicast messages. In order to maximize max-min fair (MMF) rates, we design a novel low-complexity alternating-optimization algorithm to compute transmit and relay precoders. We also propose rate-splitting (RS) alternatives of SC and CC schemes together with an iterative algorithm to derive dedicated transmit and relay precoders so as to maximize MMF rates. We rigorously evaluate the performance of the proposed transmission schemes and precoders, and verify the superiority of RS-based schemes in overloaded scenarios without any saturation with increasing signal-to-noise ratio.
Privacy-Preserving Eye Videos using Rubber Sheet Model
Venue: arXiv cs.CV 2020
Year: 2020

Video-based eye trackers estimate gaze based on eye images/videos. As security and privacy concerns loom over technological advancements, tackling such challenges is crucial. We present a new approach to handle privacy issues in eye videos by replacing the current identifiable iris texture with a different iris template in the video capture pipeline based on the Rubber Sheet Model. We extend to image blending and median-value representations to demonstrate that videos can be manipulated without significantly degrading segmentation and pupil detection accuracy.
Cooperative Double-IRS Aided Communication: Beamforming Design and Power Scaling
Venue: arXiv cs.IT 2020
Year: 2020

Intelligent reflecting surface (IRS) is a promising technology to support high performance wireless communication. By adaptively configuring the reflection amplitude and/or phase of each passive reflecting element on it, the IRS can reshape the electromagnetic environment in favour of signal transmission. This letter advances the existing research by proposing and analyzing a double-IRS aided wireless communication system. Under the reasonable assumption that the reflection channel from IRS 1 to IRS 2 is of rank 1 (e.g., line-of-sight channel), we propose a joint passive beamforming design for the two IRSs. Based on this, we show that deploying two cooperative IRSs with in total K elements can yield a power gain of order O(K^4), which greatly outperforms the case of deploying one traditional IRS with a power gain of order O(K^2). Our simulation results validate that the performance of deploying two cooperative IRSs is significantly better than that of deploying one IRS given a sufficient total number of IRS elements. We also extend our line-of-sight channel model to show how different channel models affect the performance of the double-IRS aided wireless communication system.
Attacking with bitcoin: Using Bitcoin to Build Resilient Botnet Armies
Venue: arXiv cs.CR 2020
Year: 2020

We focus on the problem of botnet orchestration and discuss how attackers can leverage decentralised technologies to dynamically control botnets with the goal of having botnets that are resilient against hostile takeovers. We cover critical elements of the Bitcoin blockchain and its usage for `floating command and control servers'. We further discuss how blockchain-based botnets can be built and include a detailed discussion of our implementation. We also showcase how specific Bitcoin APIs can be used in order to write extraneous data to the blockchain. Finally, while in this paper, we use Bitcoin to build our resilient botnet proof of concept, the threat is not limited to Bitcoin blockchain and can be generalized.
Identifying Radiological Findings Related to COVID-19 from Medical Literature
Venue: arXiv cs.IR 2020
Year: 2020

Coronavirus disease 2019 (COVID-19) has infected more than one million individuals all over the world and caused more than 55,000 deaths, as of April 3 in 2020. Radiological findings are important sources of information in guiding the diagnosis and treatment of COVID-19. However, the existing studies on how radiological findings are correlated with COVID-19 are conducted separately by different hospitals, which may be inconsistent or even conflicting due to population bias. To address this problem, we develop natural language processing methods to analyze a large collection of COVID-19 literature containing study reports from hospitals all over the world, reconcile these results, and draw unbiased and universally-sensible conclusions about the correlation between radiological findings and COVID-19. We apply our method to the CORD-19 dataset and successfully extract a set of radiological findings that are closely tied to COVID-19.
FeCAM: A Universal Compact Digital and Analog Content Addressable Memory Using Ferroelectric
Venue: arXiv cs.ET 2020
Year: 2020

Ferroelectric field effect transistors (FeFETs) are being actively investigated with the potential for in-memory computing (IMC) over other non-volatile memories (NVMs). Content Addressable Memories (CAMs) are a form of IMC that performs parallel searches for matched entries over a memory array for a given input query. CAMs are widely used for data-centric applications that involve pattern matching and search functionality. To accommodate the ever expanding data, it is attractive to resort to analog CAM for memory density improvement. However, the digital CAM design nowadays based on standard CMOS or emerging nonvolatile memories (e.g., resistive storage devices) is already challenging due to area, power, and cost penalties. Thus, it can be extremely expensive to achieve analog CAM with those technologies due to added cell components. As such, we propose, for the first time, a universal compact FeFET based CAM design, FeCAM, with search and storage functionality enabled in digital and analog domain simultaneously. By exploiting the multi-level-cell (MLC) states of FeFET, FeCAM can store and search inputs in either digital or analog domain. We perform a device-circuit co-design of the proposed FeCAM and validate its functionality and performance using an experimentally calibrated FeFET model. Circuit level simulation results demonstrate that FeCAM can either store continuous matching ranges or encode 3-bit data in a single CAM cell. When compared with the existing digital CMOS based CAM approaches, FeCAM is found to improve both memory density by 22.4X and energy saving by 8.6/3.2X for analog/digital modes, respectively. In the CAM-related application, our evaluations show that FeCAM can achieve 60.5X/23.1X saving in area/search energy compared with conventional CMOS based CAMs.
Knowledge Guided Metric Learning for Few-Shot Text Classification
Venue: arXiv cs.CL 2020
Year: 2020

The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain. When the labeled data is scarce, models tend to struggle to achieve satisfactory performance. However, human beings can distinguish new categories very efficiently with few examples. This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate relation network parameters. Metrics can be transferred among tasks when equipped with these generated parameters, so that similar tasks use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the state-of-the-art few-shot text classification models.
Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task. Examining a variety of architectures with different numbers of parameters, we demonstrate that the recent text-to-text transfer transformer (T5) achieves the best results both on CANARD and CAsT with fewer parameters, compared to similar transformer architectures.
A Lower Bound for Byzantine Agreement and Consensus for Adaptive Adversaries using VDFs
Venue: arXiv cs.DC 2020
Year: 2020

Large scale cryptocurrencies require the participation of millions of participants and support economic activity of billions of dollars, which has led to new lines of work in binary Byzantine Agreement (BBA) and consensus. The new work aims to achieve communication-efficiency---given such a large $n$, not everyone can speak during the protocol. Several protocols have achieved consensus with communication-efficiency, even under an adaptive adversary, but they require additional strong assumptions---proof-of-work, memory-erasure, etc. All of these protocols use multicast: every honest replica multicasts messages to all other replicas. Under this model, we provide a new communication-efficient consensus protocol using Verifiable Delay Functions (VDFs) that is secure against adaptive adversaries and does not require the same strong assumptions present in other protocols. A natural question is whether we can extend the synchronous protocols to the partially synchronous setting---in this work, we show that using multicast, we cannot. Furthermore, we cannot achieve always safe communication-efficient protocols (that maintain safety with probability 1) even in the synchronous setting against a static adversary when honest replicas only choose to multicast its messages. Considering these impossibility results, we describe a new communication-efficient BBA protocol in a modified partially synchronous network model which is secure against adaptive adversaries with high probability.
The Paradox of Information Access: Growing Isolation in the Age of Sharing
Venue: arXiv cs.CY 2020
Year: 2020

Modern online media, such as Twitter, Instagram, and YouTube, enable anyone to become an information producer and to offer online content for potentially global consumption. By increasing the amount of globally accessible real-time information, today's ubiquitous producers contribute to a world, where an individual consumes vanishingly smaller fractions of all produced content. In general, consumers preferentially select information that closely matches their individual views and values. The bias inherent in such selection is further magnified by today's information curation services that maximize user engagement (and thus service revenue) by filtering new content in accordance with observed consumer preferences. Consequently, individuals get exposed to increasingly narrower bands of the ideology spectrum. Societies get fragmented into increasingly ideologically isolated enclaves. These enclaves (or echo-chambers) then become vulnerable to misinformation spread, which in turn further magnifies polarization and bias. We call this dynamic the paradox of information access; a growing ideological fragmentation in the age of sharing. This article describes the technical, economic, and socio-cognitive contributors to this paradox, and explores research directions towards its mitigation.