OpenRaiser/Intern-Atlas huggingface dataset:
Newsalyze: Effective Communication of Person-Targeting Biases in News Articles
Venue: arXiv cs.CY 2021
Year: 2021

Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption, e.g., we present respondents with a news overview and individual articles. We not only measure the visualizations' effect on respondents' bias-awareness, but we can also pinpoint the effects on individual components of the visualizations by employing a conjoint design. Our bias-sensitive overviews strongly and significantly increase bias-awareness in respondents. Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles. In contrast, the reviewed prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets.
Machine Covering in the Random-Order Model
Venue: arXiv cs.DS 2021
Year: 2021

In the Online Machine Covering problem jobs, defined by their sizes, arrive one by one and have to be assigned to $m$ parallel and identical machines, with the goal of maximizing the load of the least-loaded machine. In this work, we study the Machine Covering problem in the recently popular random-order model. Here no extra resources are present, but instead the adversary is weakened in that it can only decide upon the input set while jobs are revealed uniformly at random. It is particularly relevant to Machine Covering where lower bounds are usually associated to highly structured input sequences. We first analyze Graham's Greedy-strategy in this context and establish that its competitive ratio decreases slightly to $Θ\left(\frac{m}{\log(m)}\right)$ which is asymptotically tight. Then, as our main result, we present an improved $\tilde{O}(\sqrt[4]{m})$-competitive algorithm for the problem. This result is achieved by exploiting the extra information coming from the random order of the jobs, using sampling techniques to devise an improved mechanism to distinguish jobs that are relatively large from small ones. We complement this result with a first lower bound showing that no algorithm can have a competitive ratio of $O\left(\frac{\log(m)}{\log\log(m)}\right)$ in the random-order model. This lower bound is achieved by studying a novel variant of the Secretary problem, which could be of independent interest.
Starlink : A Solution to the Digital Connectivity Divide in Education in the Global South
Venue: arXiv cs.CY 2021
Year: 2021

Digital connectivity gap in the global south hampered the education of millions of school children during the COVID-19 pandemic. If not actions are taken to remedy this problem, future prospects of millions of children around will be bleak. This paper explores the feasibility of using the SpaceX Starlink satellite constellation as a means to alleviate the problem of the digital connectivity divide in the global south. First, the paper discusses the issues of digital connectivity in education in rural Sri Lanka and other countries in the global south. Then, the paper gives an introduction to Starlink broadband internet technology and discusses its advantages over traditional technologies. After that, the paper discusses a possible mechanism of adopting Starlink technology as a solution to the rural digital connectivity problem in the global south. Technological, as well as economical aspects of such scheme, are discussed. Finally, challenges that may arise in deploying a system such as Starlink to improve rural digital connectivity in Sri Lanka or any another country in the global south will be discussed with possible remedies.
The search of Type I codes
Venue: arXiv cs.IT 2021
Year: 2021

A self-dual binary linear code is called Type I code if it has singly-even codewords, i.e.~it has codewords with weight divisible by $2.$ The purpose of this paper is to investigate interesting properties of Type I codes of different lengths. Further, we build up a computer-based code-searching program based on our knowledge about Type I codes. Some computation results achieved by this program are given.
Uncertainty-aware Topic Modeling Visualization
Venue: arXiv cs.HC 2021
Year: 2021

Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic modeling procedure is based on a randomly selected initial configuration as well as a number of parameter values than need to be chosen. This induces uncertainties on the topic modeling results, and visualization methods should convey these uncertainties during the analysis process. We propose a visual uncertainty-aware topic modeling analysis. We capture the uncertainty by computing topic modeling ensembles and propose measures for estimating topic modeling uncertainty from the ensemble. Then, we propose to enhance state-of-the-art topic modeling visualization methods to convey the uncertainty in the topic modeling process. We visualize the entire ensemble of topic modeling results at different levels for topic and document analysis. We apply our visualization methods to a text corpus to document the impact of uncertainty on the analysis.
A Sociotechnical View of Algorithmic Fairness
Venue: arXiv cs.CY 2021
Year: 2021

Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.
Multi-Objective Allocation of COVID-19 Testing Centers: Improving Coverage and Equity in Access
Venue: arXiv cs.CY 2021
Year: 2021

At the time of this article, COVID-19 has been transmitted to more than 42 million people and resulted in more than 673,000 deaths across the United States. Throughout this pandemic, public health authorities have monitored the results of diagnostic testing to identify hotspots of transmission. Such information can help reduce or block transmission paths of COVID-19 and help infected patients receive early treatment. However, most current schemes of test site allocation have been based on experience or convenience, often resulting in low efficiency and non-optimal allocation. In addition, the historical sociodemographic patterns of populations within cities can result in measurable inequities in access to testing between various racial and income groups. To address these pressing issues, we propose a novel test site allocation scheme to (a) maximize population coverage, (b) minimize prediction uncertainties associated with projections of outbreak trajectories, and (c) reduce inequities in access. We illustrate our approach with case studies comparing our allocation scheme with recorded allocation of testing sites in Georgia, revealing increases in both population coverage and improvements in equity of access over current practice.
A Methodology for Developing a Verifiable Aircraft Engine Controller from Formal Requirements
Venue: arXiv cs.SE 2021
Year: 2021

Verification of complex, safety-critical systems is a significant challenge. Manual testing and simulations are often used, but are only capable of exploring a subset of the system's reachable states. Formal methods are mathematically-based techniques for the specification and development of software, which can provide proofs of properties and exhaustive checks over a system's state space. In this paper, we present a formal requirements-driven methodology, applied to a model of an aircraft engine controller that has been provided by our industrial partner. Our methodology begins by formalising the controller's natural-language requirements using the (pre-existing) Formal Requirements Elicitation Tool (FRET), iteratively, in consultation with our industry partner. Once formalised, FRET can automatically translate the requirements to enable their verification alongside a Simulink model of the aircraft engine controller; the requirements can also guide formal verification using other approaches. These two parallel streams in our methodology seek to combine the results from formal requirements elicitation, classical verification approaches, and runtime verification; to support the verification of aerospace systems modelled in Simulink, from the requirements phase through to execution. Our methodology harnesses the power of formal methods in a way that complements existing verification techniques, and supports the traceability of requirements throughout the verification process. This methodology streamlines the process of developing verifiable aircraft engine controllers, by ensuring that the requirements are formalised up-front and useable during development. In this paper we give an overview of (FRET), describe our methodology and work to-date on the formalisation and verification of the requirements, and outline future work using our methodology.
Roles of Retailers in the Peer-to-Peer Electricity Market: A Single Retailer Perspective
Venue: arXiv cs.CY 2021
Year: 2021

Despite extensive research in the past five years and several successfully completed and on-going pilot projects, regulators are still reluctant to implement peer-to-peer trading at a large-scale in today's electricity market. The reason could partly be attributed to the perceived disadvantage of current market participants like retailers due to their exclusion from market participation - a fundamental property of decentralised peer-to-peer trading. As a consequence, recently, there has been growing pressure from energy service providers in favour of retailers' participation in peer-to-peer trading. However, the role of retailers in the peer-to-peer market is yet to be established as no existing study has challenged this fundamental circumspection of decentralized trading. In this context, this perspective takes the first step to discuss the feasibility of retailers' involvement in the peer-to-peer market. In doing so, we identify key characteristics of retail-based and peer-to-peer electricity markets and discuss our viewpoint on how to incorporate a single retailer in a peer-to-peer market without compromising the fundamental decision-making characteristics of both markets. Finally, we give an example of a hypothetical business model to demonstrate how a retailer can be a part of a peer-to-peer market with a promise of collective benefits for the participants.
Small Data and Process in Data Visualization: The Radical Translations Case Study
Venue: arXiv cs.HC 2021
Year: 2021

This paper uses the collaborative project Radical Translations as case study to examine some of the theoretical perspectives informing the adoption and critique of data visualization in the digital humanities with applied examples in context. It showcases how data visualization is used within a King's Digital Lab project lifecycle to facilitate collaborative data exploration within the project interdisciplinary team - to support data curation and cleaning and/or to guide the design process - as well as data analysis by users external to the team. Theoretical issues around bridging the gap between approaches adopted for small and/or large-scale datasets are addressed from functional perspectives with reference to evolving data modelling and software development lifecycle approaches and workflows. While anchored to the specific context of the project under examination, some of the identified trade-offs have epistemological value beyond the specific case study iterations and its design solutions.
Proceedings of the 19th International Overture Workshop
Venue: arXiv cs.SE 2021
Year: 2021

This volume contains the papers presented at the 19th International Overture Workshop, which was held in an hybrid format: online and physically at Aarhus, Denmark on 22th October 2021. This event was the latest in a series of workshops around the Vienna Development Method (VDM), the open-source project Overture, and related tools and formalisms. VDM is one of the longest established formal methods for systems development. A lively community of researchers and practitioners has grown up in academia and industry around the modelling languages (VDM-SL, VDM++, VDM-RT, CML) and tools (VDMTools, Overture, VDM VSCode extension, Crescendo, Symphony, the INTO-CPS chain, and ViennaTalk). Together, these provide a platform for work on modelling and analysis technology that includes static and dynamic analysis, test generation, execution support, and model checking. This workshop provided updates on the emerging technology of VDM/Overture, including collaboration infrastructure, collaborative modelling and co-simulation for Cyber-Physical Systems.
Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language
Venue: arXiv cs.CL 2021
Year: 2021

Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the prevalence of regional languages in social media and the paucity of language flexible hate speech detectors. The proposed work focuses on analyzing hate speech in Hindi-English code-switched language. Our method explores transformation techniques to capture precise text representation. To contain the structure of data and yet use it with existing algorithms, we developed MoH or Map Only Hindi, which means "Love" in Hindi. MoH pipeline consists of language identification, Roman to Devanagari Hindi transliteration using a knowledge base of Roman Hindi words. Finally, it employs the fine-tuned Multilingual Bert and MuRIL language models. We conducted several quantitative experiment studies on three datasets and evaluated performance using Precision, Recall, and F1 metrics. The first experiment studies MoH mapped text's performance with classical machine learning models and shows an average increase of 13% in F1 scores. The second compares the proposed work's scores with those of the baseline models and offers a rise in performance by 6%. Finally, the third reaches the proposed MoH technique with various data simulations using the existing transliteration library. Here, MoH outperforms the rest by 15%. Our results demonstrate a significant improvement in the state-of-the-art scores on all three datasets.
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
Venue: arXiv cs.CV 2021
Year: 2021

Facial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achieved great success in various editing techniques such as style transfer and attribute translation. However, current approaches are either focusing on pure style transfer, or on the translation of predefined sets of attributes with restricted interactivity. To address this issue, we propose FacialGAN, a novel framework enabling simultaneous rich style transfers and interactive facial attributes manipulation. While preserving the identity of a source image, we transfer the diverse styles of a target image to the source image. We then incorporate the geometry information of a segmentation mask to provide a fine-grained manipulation of facial attributes. Finally, a multi-objective learning strategy is introduced to optimize the loss of each specific tasks. Experiments on the CelebA-HQ dataset, with CelebAMask-HQ as semantic mask labels, show our model's capacity in producing visually compelling results in style transfer, attribute manipulation, diversity and face verification. For reproducibility, we provide an interactive open-source tool to perform facial manipulations, and the Pytorch implementation of the model.
Early Diagnostic Prediction of Covid-19 using Gradient-Boosting Machine Model
Venue: arXiv cs.LG 2021
Year: 2021

With the huge spike in the COVID-19 cases across the globe and reverse transcriptase-polymerase chain reaction (RT-PCR) test remains a key component for rapid and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In recent months there has been an acute shortage of medical supplies in developing countries, especially a lack of RT-PCR testing resulting in delayed patient care and high infection rates. We present a gradient-boosting machine model that predicts the diagnostics result of SARS-CoV- 2 in an RT-PCR test by utilizing eight binary features. We used the publicly available nationwide dataset released by the Israeli Ministry of Health.
A New Approach to Complex Dynamic Geofencing for Unmanned Aerial Vehicles
Venue: arXiv cs.RO 2021
Year: 2021

The anticipated widespread use of unmanned aerial vehicles (UAVs) raises significant safety and security concerns, including trespassing in restricted areas, colliding with other UAVs, and disrupting high-traffic airspaces. To mitigate these risks, geofences have been proposed as one line of defence, which limit UAVs from flying into the perimeters of other UAVs and restricted locations. In this paper, we address the concern that existing geometric geofencing algorithms lack accuracy during the calculation of complex geofences, particularly in dynamic urban environments. We propose a new algorithm based on alpha shapes and Voronoi diagrams, which we integrate into an on-drone framework using an open-source mapping database from OpenStreetMap. To demonstrate its efficacy, we present performance results using Microsoft's AirSim and a low-cost commercial UAV platform in a real-world urban environment.
Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments
Venue: arXiv cs.NI 2021
Year: 2021

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous cars, driver assistance systems, and in-vehicle entertainment, many vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles' sensing capabilities. Our main contribution is a detailed mathematical specification for a task-based, distributed service placement model that scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to an autonomous vehicular edge computing-based naïve solution.
Protection of the patient data against intentional attacks using a hybrid robust watermarking code
Venue: arXiv cs.CR 2021
Year: 2021

The security of patient information is important during the transfer of medical data. A hybrid spatial domain watermarking algorithm that includes encryption, integrity protection and steganography is proposed to strengthen the information originality based on the authentication. The proposed algorithm checks whether the information of patients has been deliberately changed or not. The created code is distributed at every pixel of the medical image and not only in the regions of noninterest pixels, while the image details are still preserved. To enhance the security of the watermarking code, "SHA-1" is used to get the initial key for the Symmetric Encryption Algorithm. The target of this approach is to preserve the content of the image and the watermark simultaneously, this is achieved by synthesizing an encrypted watermark from one of the components of the original image and not by embedding a watermark in the image. To evaluate the proposed code the Least Significant Bit (LSB), Bit2SB, and Bit3SB were used. The evaluation of the proposed code showed that the LSB is of better quality but overall the Bit2SB is better in its ability against the active attacks up to a size of 2*2 pixels, and it preserves the high image quality.
Image Protection against Forgery and Pixel Tampering based on a Triple Hybrid Security Approach
Venue: arXiv cs.CR 2021
Year: 2021

Due to the widespread of advanced digital imaging devices, forgery of digital images became more serious attack patterns. In this attack scenario, the attacker tries to manipulate the digital image to conceal some meaningful information of the genuine image for malicious purposes. This leads to increase security interest about protecting images against integrity tampers. This paper proposes a novel technique for protecting colored images against forgery and pixel tamper. The proposed approach is designed as a hybrid model from three security techniques, Message Digest hashing algorithm (MD5), Advanced Encryption Standard-128 bits (AES), and Stenography. The proposed approach has been evaluated using set of image quality metrics for testing the impact of embedding the protection code on image quality. The evaluation results proved that protecting image based on Least Significant Bit (LSB) is the best technique that keep image quality compared with other two bit-substitution methods. Moreover, the results proved the superiority of the proposed approach compared with other technique in the literature.