OpenRaiser/Intern-Atlas huggingface dataset:
LimeOut: An Ensemble Approach To Improve Process Fairness
Venue: arXiv cs.LG 2020
Year: 2020

Artificial Intelligence and Machine Learning are becoming increasingly present in several aspects of human life, especially, those dealing with decision making. Many of these algorithmic decisions are taken without human supervision and through decision making processes that are not transparent. This raises concerns regarding the potential bias of these processes towards certain groups of society, which may entail unfair results and, possibly, violations of human rights. Dealing with such biased models is one of the major concerns to maintain the public trust. In this paper, we address the question of process or procedural fairness. More precisely, we consider the problem of making classifiers fairer by reducing their dependence on sensitive features while increasing (or, at least, maintaining) their accuracy. To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness. We make use of "LIME Explanations" to assess a classifier's fairness and to determine the sensitive features to remove. This produces a pool of classifiers (through feature dropout) whose ensemble is shown empirically to be less dependent on sensitive features, and with improved or no impact on accuracy.
MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps
Venue: arXiv cs.CV 2020
Year: 2020

Malaria is considered one of the deadliest diseases in today world which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium which infects the red blood cells in human beings. The parasites are transmitted by a female class of mosquitos known as Anopheles. The diagnosis of malaria requires identification and manual counting of parasitized cells by medical practitioners in microscopic blood smears. Due to the unavailability of resources, its diagnostic accuracy is largely affected by large scale screening. State of the art Computer-aided diagnostic techniques based on deep learning algorithms such as CNNs, with end to end feature extraction and classification, have widely contributed to various image recognition tasks. In this paper, we evaluate the performance of custom made convnet Mosquito-Net, to classify the infected and uninfected cells for malaria diagnosis which could be deployed on the edge and mobile devices owing to its fewer parameters and less computation power. Therefore, it can be wildly preferred for diagnosis in remote and countryside areas where there is a lack of medical facilities.
Competitive Policy Optimization
Venue: arXiv cs.LG 2020
Year: 2020

A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. To tackle this, we propose competitive policy optimization (CoPO), a novel policy gradient approach that exploits the game-theoretic nature of competitive games to derive policy updates. Motivated by the competitive gradient optimization method, we derive a bilinear approximation of the game objective. In contrast, off-the-shelf policy gradient methods utilize only linear approximations, and hence do not capture interactions among the players. We instantiate CoPO in two ways:(i) competitive policy gradient, and (ii) trust-region competitive policy optimization. We theoretically study these methods, and empirically investigate their behavior on a set of comprehensive, yet challenging, competitive games. We observe that they provide stable optimization, convergence to sophisticated strategies, and higher scores when played against baseline policy gradient methods.
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Venue: arXiv cs.LG 2020
Year: 2020

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems. Despite the plethora of different models for deep learning on graphs, few approaches have been proposed thus far for dealing with graphs that present some sort of dynamic nature (e.g. evolving features or connectivity over time). In this paper, we present Temporal Graph Networks (TGNs), a generic, efficient framework for deep learning on dynamic graphs represented as sequences of timed events. Thanks to a novel combination of memory modules and graph-based operators, TGNs are able to significantly outperform previous approaches being at the same time more computationally efficient. We furthermore show that several previous models for learning on dynamic graphs can be cast as specific instances of our framework. We perform a detailed ablation study of different components of our framework and devise the best configuration that achieves state-of-the-art performance on several transductive and inductive prediction tasks for dynamic graphs.
AMALGUM -- A Free, Balanced, Multilayer English Web Corpus
Venue: arXiv cs.CL 2020
Year: 2020

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a "better than NLP" benchmark and evaluate the accuracy of the resulting resource.
Market Graph Clustering Via QUBO and Digital Annealing
Venue: arXiv cs.SI 2020
Year: 2020

Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our reference index as a market graph and express the index tracking problem in a quadratic K-medoids form. We take advantage of a purpose built hardware architecture, the Fujitsu Digital Annealer, to circumvent the NP-hard nature of the problem and solve our formulation efficiently. In this article, we combine three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of a broad market index, using only a small subset of its constituent assets. Moreover, our quadratic formulation allows us to take advantage of recent hardware advances, to overcome the NP-hard nature of the problem.
SwissCovid: a critical analysis of risk assessment by Swiss authorities
Venue: arXiv cs.CR 2020
Year: 2020

Ahead of the rollout of the SwissCovid contact tracing app, an official public security test was performed. During this audit, Prof. Serge Vaudenay and Dr. Martin Vuagnoux described a large set of problems with the app, including a new variation of a known false-positive attack, leveraging a cryptographic weakness in the Google and Apple Exposure Notification framework to tamper with the emitted Bluetooth beacons. Separately, the first author described a re-identification attack leveraging rogue apps or SDKs. The response from the Swiss cybersecurity agency and the Swiss public health authority was to claim these various attacks were unlikely as they required physical proximity of the attacker with the target (although it was admitted the attacker could be further than two meters). The physical presence of the attacker in Switzerland was deemed significant as it would imply such attackers would fall under the Swiss Criminal Code. We show through one example that a much larger variety of adversaries must be considered in the scenarios originally described and that these attacks can be done by adversaries without any physical presence in Switzerland. This goes directly against official findings of Swiss public authorities evaluating the risks associated with SwissCovid. To move the discussion further along, we briefly discuss the growth of the attack surface and harms with COVID-19 and SwissCovid prevalence in the population. While the focus of this article is on Switzerland, we emphasize the core technical findings and cybersecurity concerns are of relevance to many contact tracing efforts.
Pervasive Communications Technologies For Managing Pandemics
Venue: arXiv cs.CY 2020
Year: 2020

Pandemics always have had serious consequences unless they were effectively contained. Recent experiences with COVID-19 show that by using a smart and swift approach to deal with pandemics, avoids overwhelming of healthcare systems, and reduces the loss of precious life. This paper is about using smart technologies such as Mobile Edge Clouds (MEC), Internet of Things (IoT), and Artificial Intelligence (AI), as an approach to effectively manage pandemics. IoT provides pervasive connectivity among various devices and can be used for collecting information such as location and symptoms of potentially infected individuals. MECs provide cloud services on the edge, integrating IoT infrastructure and execution of sophisticated AI algorithms in the Cloud. In this paper, we develop a prototype to demonstrate the convergence of pervasive technologies to support research in managing pandemics. Low-cost Single Board Computers (SBC) based clusters are integrated within MEC to support remote medical teams in the field. The prototype implements a lightweight Docker container orchestrated by Kubernetes eco-system which is deployed on the clusters. The prototype successfully demonstrates that mobile medical facilities can utilize the proposed solution to collect information and execute AI algorithms while on the go. Finally, we present a discussion on the role of converging pervasive technologies on managing pandemics.
A Methodology for Assessing the Environmental Effects Induced by ICT Services. Part I: Single Services
Venue: arXiv cs.CY 2020
Year: 2020

Information and communication technologies (ICT) are increasingly seen as key enablers for climate change mitigation measures. They can make existing products and activities more efficient or substitute them altogether. Consequently, different initiatives have started to estimate the environmental effects of ICT services. Such assessments, however, lack scientific rigor and often rely on crude assumptions and methods, leading to inaccurate or even misleading results. The few methodological attempts that exist do not address several crucial aspects, and are thus insufficient to foster good as-sessment practice. Starting from such a high level standard from the European Telecommunication Standardisation Institute (ETSI) and the International Telecommunication Union (ITU), this article identifies the shortcomings of existing methodologies and proposes solutions. It addresses several aspects for the assessment of single ICT services: the goal and scope definition (analyzing differences between ICT substitution and optimization, the time perspective of the assessment, the challenge of a hypothetical baseline for the situation without the ICT solution, and the differences between modelling and case studies) as well as the often ignored influence of rebound effects and the difficult extrapolation from case studies to larger populations.
CoinPolice:Detecting Hidden Cryptojacking Attacks with Neural Networks
Venue: arXiv cs.CR 2020
Year: 2020

Traffic monetization is a crucial component of running most for-profit online businesses. One of its latest incarnations is cryptocurrency mining, where a website instructs the visitor's browser to participate in building a cryptocurrency ledger (e.g., Bitcoin, Monero) in exchange for a small reward in the same currency. In its essence, this practice trades the user's electric bill (or battery level) for cryptocurrency. With user consent, this exchange can be a legitimate funding source - for example, UNICEF has collected over 27k charity donations on a website dedicated to this purpose, thehopepage.org. Regrettably, this practice also easily lends itself to abuse: in this form, called cryptojacking, attacks surreptitiously mine in the users browser, and profits are collected either by website owners or by hackers that planted the mining script into a vulnerable page. Cryptojackers have been bettering their evasion techniques, incorporating in their toolkits domain fluxing, content obfuscation, the use of WebAssembly, and throttling. Whereas most state-of-the-art defenses address multiple of these evasion techniques, none is resistant against all. In this paper, we offer a novel detection method, CoinPolice, that is robust against all of the aforementioned evasion techniques. CoinPolice flips throttling against cryptojackers, artificially varying the browser's CPU power to observe the presence of throttling. Based on a deep neural network classifier, CoinPolice can detect 97.87% of hidden miners with a low false positive rate (0.74%). We compare CoinPolice performance with the current state of the art and show our approach outperforms it when detecting aggressively throttled miners. Finally, we deploy Coinpolice to perform the largest-scale cryptoming investigation to date, identifying 6700 sites that monetize traffic in this fashion.
Particle Swarm Optimization with Velocity Restriction and Evolutionary Parameters Selection for Scheduling Problem
Venue: arXiv cs.NE 2020
Year: 2020

The article presents a study of the Particle Swarm optimization method for scheduling problem. To improve the method's performance a restriction of particles' velocity and an evolutionary meta-optimization were realized. The approach proposed uses the Genetic algorithms for selection of the parameters of Particle Swarm optimization. Experiments were carried out on test tasks of the job-shop scheduling problem. This research proves the applicability of the approach and shows the importance of tuning the behavioral parameters of the swarm intelligence methods to achieve a high performance.
Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
Venue: arXiv cs.CV 2020
Year: 2020

Existing studies for automated melanoma diagnosis are based on single-time point images of lesions. However, melanocytic lesions de facto are progressively evolving and, moreover, benign lesions can progress into malignant melanoma. Ignoring cross-time morphological changes of lesions thus may lead to misdiagnosis in borderline cases. Based on the fact that dermatologists diagnose ambiguous skin lesions by evaluating the dermoscopic changes over time via follow-up examination, in this study, we propose an automated framework for melanoma diagnosis using sequential dermoscopic images. To capture the spatio-temporal characterization of dermoscopic evolution, we construct our model in a two-stream network architecture which capable of simultaneously learning appearance representations of individual lesions while performing temporal reasoning on both raw pixels difference and abstract features difference. We collect 184 cases of serial dermoscopic image data, which consists of histologically confirmed 92 benign lesions and 92 melanoma lesions, to evaluate the effectiveness of the proposed method. Our model achieved AUC of 74.34%, which is ~8% higher than that of only using single images and ~6% higher than the widely used sequence learning model based on LSTM.
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
Venue: arXiv cs.IR 2020
Year: 2020

COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has caused more than 7.4 million cases and over 418,000 deaths. The highly dynamic and rapidly evolving situation with COVID-19 has made it difficult to access accurate, on-demand information regarding the disease. Online communities, forums, and social media provide potential venues to search for relevant questions and answers, or post questions and seek answers from other members. However, due to the nature of such sites, there are always a limited number of relevant questions and responses to search from, and posted questions are rarely answered immediately. With the advancements in the field of natural language processing, particularly in the domain of language models, it has become possible to design chatbots that can automatically answer consumer questions. However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data. In this paper, we propose to apply a language model for automatically answering questions related to COVID-19 and qualitatively evaluate the generated responses. We utilized the GPT-2 language model and applied transfer learning to retrain it on the COVID-19 Open Research Dataset (CORD-19) corpus. In order to improve the quality of the generated responses, we applied 4 different approaches, namely tf-idf, BERT, BioBERT, and USE to filter and retain relevant sentences in the responses. In the performance evaluation step, we asked two medical experts to rate the responses. We found that BERT and BioBERT, on average, outperform both tf-idf and USE in relevance-based sentence filtering tasks. Additionally, based on the chatbot, we created a user-friendly interactive web application to be hosted online.
Towards an Adversarially Robust Normalization Approach
Venue: arXiv cs.LG 2020
Year: 2020

Batch Normalization (BatchNorm) is effective for improving the performance and accelerating the training of deep neural networks. However, it has also shown to be a cause of adversarial vulnerability, i.e., networks without it are more robust to adversarial attacks. In this paper, we investigate how BatchNorm causes this vulnerability and proposed new normalization that is robust to adversarial attacks. We first observe that adversarial images tend to shift the distribution of BatchNorm input, and this shift makes train-time estimated population statistics inaccurate. We hypothesize that these inaccurate statistics make models with BatchNorm more vulnerable to adversarial attacks. We prove our hypothesis by replacing train-time estimated statistics with statistics calculated from the inference-time batch. We found that the adversarial vulnerability of BatchNorm disappears if we use these statistics. However, without estimated batch statistics, we can not use BatchNorm in the practice if large batches of input are not available. To mitigate this, we propose Robust Normalization (RobustNorm); an adversarially robust version of BatchNorm. We experimentally show that models trained with RobustNorm perform better in adversarial settings while retaining all the benefits of BatchNorm. Code is available at \url{https://github.com/awaisrauf/RobustNorm}.
Representing Pure Nash Equilibria in Argumentation
Venue: arXiv cs.AI 2020
Year: 2020

In this paper we describe an argumentation-based representation of normal form games, and demonstrate how argumentation can be used to compute pure strategy Nash equilibria. Our approach builds on Modgil's Extended Argumentation Frameworks. We demonstrate its correctness, prove several theoretical properties it satisfies, and outline how it can be used to explain why certain strategies are Nash equilibria to a non-expert human user.
Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning
Venue: arXiv cs.RO 2020
Year: 2020

High performance lidars are essential in autonomous robots such as self-driving cars, automated ground vehicles and intelligent machines. Traditional mechanical scanning lidars offer superior performance in autonomous vehicles, but the potential mass application is limited by the inherent manufacturing difficulty. We propose a robotic lidar sensor based on incommensurable scanning that allows straightforward mass production and adoption in autonomous robots. Some unique features are additionally permitted by this incommensurable scanning. Similar to the fovea in human retina, this lidar features a peaked central angular density, enabling in applications that prefers eye-like attention. The incommensurable scanning method of this lidar could also provide a much higher resolution than conventional lidars which is beneficial in robotic applications such as sensor calibration. Examples making use of these advantageous features are demonstrated.
Wave Propagation of Visual Stimuli in Focus of Attention
Venue: arXiv cs.CV 2020
Year: 2020

Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field. While current computational models keep improving their predictive ability thanks to the increasing availability of data, they still struggle approximating the effectiveness and efficiency exhibited by foveated animals. In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and that is very well-suited for parallel and distributed implementations. Attention emerges as a wave propagation process originated by visual stimuli corresponding to details and motion information. The resulting field obeys the principle of "inhibition of return" so as not to get stuck in potential holes. An accurate experimentation of the model shows that it achieves top level performance in scanpath prediction tasks. This can easily be understood at the light of a theoretical result that we establish in the paper, where we prove that as the velocity of wave propagation goes to infinity, the proposed model reduces to recently proposed state of the art gravitational models of focus of attention.
A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
Venue: arXiv cs.CL 2020
Year: 2020

Syntactic and semantic parsing has been investigated for decades, which is one primary topic in the natural language processing community. This article aims for a brief survey on this topic. The parsing community includes many tasks, which are difficult to be covered fully. Here we focus on two of the most popular formalizations of parsing: constituent parsing and dependency parsing. Constituent parsing is majorly targeted to syntactic analysis, and dependency parsing can handle both syntactic and semantic analysis. This article briefly reviews the representative models of constituent parsing and dependency parsing, and also dependency graph parsing with rich semantics. Besides, we also review the closely-related topics such as cross-domain, cross-lingual and joint parsing models, parser application as well as corpus development of parsing in the article.
The EuroSys 2020 Online Conference: Experience and lessons learned
Venue: arXiv cs.CY 2020
Year: 2020

The 15th European Conference on Computer Systems (EuroSys'20) was organized as a virtual (online) conference on April 27-30, 2020. The main EuroSys'20 track took place April 28-30, 2020, preceded by five workshops (EdgeSys'20, EuroDW'20, EuroSec'20, PaPoC'20, SPMA'20) on April 27, 2020. The decision to hold a virtual (online) conference was taken in early April 2020, after consultations with the EuroSys community and internal discussions about potential options, eventually allowing about three weeks for the organization. This paper describes the choices we made to organize EuroSys'20 as a virtual (online) conference, the challenges we addressed, and the lessons learned.