OpenRaiser/Intern-Atlas huggingface dataset:
Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.
A Survey on Patch-based Synthesis: GPU Implementation and Optimization
Venue: arXiv cs.GR 2020
Year: 2020

This thesis surveys the research in patch-based synthesis and algorithms for finding correspondences between small local regions of images. We additionally explore a large kind of applications of this new fast randomized matching technique. One of the algorithms we have studied in particular is PatchMatch, can find similar regions or "patches" of an image one to two orders of magnitude faster than previous techniques. The algorithmic program is driven by applying mathematical properties of nearest neighbors in natural images. It is observed that neighboring correspondences tend to be similar or "coherent" and use this observation in algorithm in order to quickly converge to an approximate solution. The algorithm is the most general form can find k-nearest neighbor matching, using patches that translate, rotate, or scale, using arbitrary descriptors, and between two or more images. Speed-ups are obtained over various techniques in an exceeding range of those areas. We have explored many applications of PatchMatch matching algorithm. In computer graphics, we have explored removing unwanted objects from images, seamlessly moving objects in images, changing image aspect ratios, and video summarization. In computer vision we have explored denoising images, object detection, detecting image forgeries, and detecting symmetries. We conclude by discussing the restrictions of our algorithmic program, GPU implementation and areas for future analysis.
Multiparty Session Programming with Global Protocol Combinators
Venue: arXiv cs.PL 2020
Year: 2020

Multiparty Session Types (MPST) is a typing discipline for communication protocols. It ensures the absence of communication errors and deadlocks for well-typed communicating processes. The state-of-the-art implementations of the MPST theory rely on (1) runtime linearity checks to ensure correct usage of communication channels and (2) external domain-specific languages for specifying and verifying multiparty protocols. To overcome these limitations, we propose a library for programming with global combinators -- a set of functions for writing and verifying multiparty protocols in OCaml. Local behaviours for all processes in a protocol are inferred at once from a global combinator. We formalise global combinators and prove a sound realisability of global combinators -- a well-typed global combinator derives a set of local types, by which typed endpoint programs can ensure type and communication safety. Our approach enables fully-static verification and implementation of the whole protocol, from the protocol specification to the process implementations, to happen in the same language. We compare our implementation to untyped and continuation-passing style implementations, and demonstrate its expressiveness by implementing a plethora of protocols. We show our library can interoperate with existing libraries and services, implementing DNS (Domain Name Service) protocol and the OAuth (Open Authentication) protocol.
Cooperative Monitoring and Dissemination of Urban Events Supported by Dynamic Clustering of Vehicles
Venue: arXiv cs.NI 2020
Year: 2020

Critical urban events take places at a random way and they need to be dealt with by public authorities quickly to maintain the proper operation of cities. The main challenges for an efficient handling of an event fall precisely in its random nature, and in the speed and accuracy of the notification of its manifestation to the authority. The pervasiveness of vehicles in urban environments, and their communication and monitoring capabilities, allow to employ VANETs to support the dissemination and handling of such events. Thus, this work proposes MINUET, a dynamic system to support the monitoring and dissemination of urban events, which operates in an ad hoc vehicular network. It uses a cooperative-based strategy where vehicles are able to dynamically coordinate the monitoring and dissemination of events in real time by the means of clusters. Results obtained by NS3 show the performance of MINUET regarding the real-time monitoring and dissemination of events detected in urban areas.
A remark on approximating permanents of positive definite matrices
Venue: arXiv cs.DS 2020
Year: 2020

Let $A$ be an $n \times n$ positive definite Hermitian matrix with all eigenvalues between 1 and 2. We represent the permanent of $A$ as the integral of some explicit log-concave function on ${\Bbb R}^{2n}$. Consequently, there is a fully polynomial randomized approximation scheme (FPRAS) for the permanent of $A$.
Cyclic Bayesian Attack Graphs: A Systematic Computational Approach
Venue: arXiv cs.CR 2020
Year: 2020

Attack graphs are commonly used to analyse the security of medium-sized to large networks. Based on a scan of the network and likelihood information of vulnerabilities, attack graphs can be transformed into Bayesian Attack Graphs (BAGs). These BAGs are used to evaluate how security controls affect a network and how changes in topology affect security. A challenge with these automatically generated BAGs is that cycles arise naturally, which make it impossible to use Bayesian network theory to calculate state probabilities. In this paper we provide a systematic approach to analyse and perform computations over cyclic Bayesian attack graphs. %thus providing a generic approach to handle cycles as well as unifying the theory of Bayesian attack graphs. Our approach first formally introduces two commonly used versions of Bayesian attack graphs and compares their expressiveness. We then present an interpretation of Bayesian attack graphs based on combinational logic circuits, which facilitates an intuitively attractive systematic treatment of cycles. We prove properties of the associated logic circuit and present an algorithm that computes state probabilities without altering the attack graphs (e.g., remove an arc to remove a cycle). Moreover, our algorithm deals seamlessly with all cycles without the need to identify their types. A set of experiments using synthetically created networks demonstrates the scalability of the algorithm on computer networks with hundreds of machines, each with multiple vulnerabilities.
RUPER-LB: Load balancing embarrasingly parallel applications in unpredictable cloud environments
Venue: arXiv cs.DC 2020
Year: 2020

The suitability of cloud computing has been studied by several authors to run scientific applications. However, the unpredictable performance fluctuations in these environments hinders the migration of scientific applications to cloud providers. To mitigate these effects, this work presents RUPER-LB, a load balancer for loosely-coupled iterative parallel applications that runs on infrastructures with disparate computing capabilities. The results obtained with a real world simulation software, show the suitability of RUPER-LB to adapt this kind of applications to execution environments with variable performance and highlight the convenience of its adoption.
Visualising COVID-19 Research
Venue: arXiv cs.IR 2020
Year: 2020

The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources. We apply this method on two recently released publications datasets (Dimensions' COVID-19 dataset and the Allen Institute for AI's CORD-19). The results reveal intriguing information including increased efforts in topics such as social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and the unfolding trajectory of the virus in different territories through publications. The results also demonstrate the need to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid scientists, researchers, and the general public to tackle the numerous issues in the fight against the COVID-19 pandemic.
High Performance and Portable Convolution Operators for ARM-based Multicore Processors
Venue: arXiv cs.PF 2020
Year: 2020

The considerable impact of Convolutional Neural Networks on many Artificial Intelligence tasks has led to the development of various high performance algorithms for the convolution operator present in this type of networks. One of these approaches leverages the \imcol transform followed by a general matrix multiplication (GEMM) in order to take advantage of the highly optimized realizations of the GEMM kernel in many linear algebra libraries. The main problems of this approach are 1) the large memory workspace required to host the intermediate matrices generated by the IM2COL transform; and 2) the time to perform the IM2COL transform, which is not negligible for complex neural networks. This paper presents a portable high performance convolution algorithm based on the BLIS realization of the GEMM kernel that avoids the use of the intermediate memory by taking advantage of the BLIS structure. In addition, the proposed algorithm eliminates the cost of the explicit IM2COL transform, while maintaining the portability and performance of the underlying realization of GEMM in BLIS.
MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
Venue: arXiv cs.CY 2020
Year: 2020

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging
Grand Challenges for Embedded Security Research in a Connected World
Venue: arXiv cs.CY 2020
Year: 2020

Protecting embedded security is becoming an increasingly challenging research problem for embedded systems due to a number of emerging trends in hardware, software, networks, and applications. Without fundamental advances in, and an understanding of embedded security it will be difficult for future engineers to provide assurance for the Internet of Things (IoT) and Operational Technology (OT) in wide ranging applications, from home automation and autonomous transportation to medical devices and factory floors. Common to such applications are cyberphysical risks and consequences stemming from a lack of embedded security. The Computing Community Consortium (CCC) held a one-day visioning workshop to explore these issues. The workshop focused on five major application areas of embedded systems, namely (1) medical/wearable devices, (2) autonomous systems (drones, vehicles, robots), (3) smart homes, (4) industry and supply chain, and (5) critical infrastructure. This report synthesizes the results of that workshop and develops a list of strategic goals for research and education over the next 5-10 years. Embedded security in connected devices presents challenges that require a broad look at the overall systems design, including human and societal dimensions as well as technical. Particular issues related to embedded security are a subset of the overall security of the application areas, which must also balance other design criteria such as cost, power, reliability, usability and function. Recent trends are converging to make the security of embedded systems an increasingly important and difficult objective, requiring new trans-disciplinary approaches to solve problems on a 5-10 year horizon.
Rendezvous Nodes (RN Nodes) Based Cluster Head Selection and Energy Efficient Data Aggregation with Mobile Sink for Lifetime Maximization in Wireless Sensor Networks
Venue: arXiv cs.NI 2020
Year: 2020

Wireless sensor network (WSN) consists of a group of dedicated sensors nodes which are distributed over a certain area for observing and recording the physical conditions (like temperature, sound, pressure) of the environment and organizing the collected data at a central location. These Sensor nodes are powered by a battery, so energy conservation becomes an important aspect in WSN. The Paper proposes a way of combining traditional LEACH (Low Energy Adaptive Clustering Hierarchy) with Mobile Sink and Rendezvous Nodes (RN Nodes) along with some changes in terms of cluster head selection process. The paper aims at decreasing the number of dead nodes which in turn will relate to a increase in the total network lifetime. Particularly for large regions the proposed algorithms outperforms traditional Leach as proved in the Simulation Results.
A Comprehensive Survey of Grammar Error Correction
Venue: arXiv cs.CL 2020
Year: 2020

Grammar error correction (GEC) is an important application aspect of natural language processing techniques. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning, especially in late 2010s when near human-level GEC systems are available. However, there is no prior work focusing on the whole recapitulation of the progress. We present the first survey in GEC for a comprehensive retrospect of the literature in this area. We first give the introduction of five public datasets, data annotation schema, two important shared tasks and four standard evaluation metrics. More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods. Since GEC is typically viewed as a sister task of machine translation, many GEC systems are based on neural machine translation (NMT) approaches, where the neural sequence-to-sequence model is applied. Similarly, some performance boosting techniques are adapted from machine translation and are successfully combined with GEC systems for enhancement on the final performance. Furthermore, we conduct an analysis in level of basic approaches, performance boosting techniques and integrated GEC systems based on their experiment results respectively for more clear patterns and conclusions. Finally, we discuss five prospective directions for future GEC researches.
CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful context-dependent word representations, and (3) combining those representations increases performance on some datasets while decreasing performance on others.
Improving Aspect-Level Sentiment Analysis with Aspect Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesize that transferring knowledge from a pre-trained AE model can benefit the performance of ALSA models. Based on this hypothesis, word embeddings are obtained during AE and subsequently, feed that to the ALSA model. Empirically, this work show that the added information significantly improves the performance of three different baseline ALSA models on two distinct domains. This improvement also translates well across domains between AE and ALSA tasks.
An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques
Venue: arXiv cs.CY 2020
Year: 2020

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
Venue: arXiv cs.CY 2020
Year: 2020

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a widerange of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 on machine learning, and since then over 7,000 students have enrolled. Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses.
Avoiding Improper Treatment of Persons with Dementia by Care Robots
Venue: arXiv cs.CY 2020
Year: 2020

The phrase "most cruel and revolting crimes" has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly human-like aware robots become capable of engaging in behavior which we would consider immoral in a human--either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.
A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
Venue: arXiv cs.CY 2020
Year: 2020

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling
Venue: arXiv cs.CL 2020
Year: 2020

The automation of adverse drug reaction (ADR) detection in social media would revolutionize the practice of pharmacovigilance, supporting drug regulators, the pharmaceutical industry and the general public in ensuring the safety of the drugs prescribed in daily practice. Following from the published proceedings of the Social Media Mining for Health (SMM4H) Applications Workshop & Shared Task in August 2019, we aimed to develop a deep learning model to classify ADRs within Twitter tweets that contain drug mentions. Our approach involved fine-tuning $BERT_{LARGE}$ and two domain-specific BERT implementations, $BioBERT$ and $Bio + clinicalBERT$, applying a domain-specific preprocessor, and developing a max-prediction ensembling approach. Our final model resulted in state-of-the-art performance on both $F_1$-score (0.6681) and recall (0.7700) outperforming all models submitted in SMM4H 2019 and during post-evaluation to date.