OpenRaiser/Intern-Atlas huggingface dataset:
On Approximability of Clustering Problems Without Candidate Centers
Venue: arXiv cs.CC 2020
Year: 2020

The k-means objective is arguably the most widely-used cost function for modeling clustering tasks in a metric space. In practice and historically, k-means is thought of in a continuous setting, namely where the centers can be located anywhere in the metric space. For example, the popular Lloyd's heuristic locates a center at the mean of each cluster. Despite persistent efforts on understanding the approximability of k-means, and other classic clustering problems such as k-median and k-minsum, our knowledge of the hardness of approximation factors of these problems remains quite poor. In this paper, we significantly improve upon the hardness of approximation factors known in the literature for these objectives. We show that if the input lies in a general metric space, it is NP-hard to approximate: $\bullet$ Continuous k-median to a factor of $2-o(1)$; this improves upon the previous inapproximability factor of 1.36 shown by Guha and Khuller (J. Algorithms '99). $\bullet$ Continuous k-means to a factor of $4- o(1)$; this improves upon the previous inapproximability factor of 2.10 shown by Guha and Khuller (J. Algorithms '99). $\bullet$ k-minsum to a factor of $1.415$; this improves upon the APX-hardness shown by Guruswami and Indyk (SODA '03). Our results shed new and perhaps counter-intuitive light on the differences between clustering problems in the continuous setting versus the discrete setting (where the candidate centers are given as part of the input).
RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble
Venue: arXiv cs.IR 2020
Year: 2020

In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection, we propose a simple yet effective weighted hierarchical rank fusion approach, that ensembles together 102 runs from (a) lexical and semantic retrieval systems, (b) pre-trained and fine-tuned BERT rankers, and (c) relevance feedback runs. Our ablation studies demonstrate the contributions of each of these systems to the overall ensemble. The submitted ensemble runs achieved state-of-the-art performance in rounds 4 and 5 of the TREC-COVID challenge.
A Niching Indicator-Based Multi-modal Many-objective Optimizer
Venue: arXiv cs.NE 2020
Year: 2020

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms.
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
Venue: arXiv cs.CV 2020
Year: 2020

Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the generic image-to-image translation, drawing a caricature automatically is a more challenging task due to the existence of various spacial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for unpaired CARIcature generation with Multiple Exaggerations (CariMe). Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photo to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which helps us to capture more detailed exaggerations than other point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation.
Joint Persian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using BERT
Venue: arXiv cs.CL 2020
Year: 2020

Words are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and multiple words written without any white spaces between them. This paper addresses the problems of word segmentation and zero-width non-joiner (ZWNJ) recognition in Persian, which we approach jointly as a sequence labeling problem. We achieved a macro-averaged F1-score of 92.40% on a carefully collected corpus of 500 sentences with a high level of difficulty.
More Than Just Good Passwords? A Study on Usability and Security Perceptions of Risk-based Authentication
Venue: arXiv cs.CR 2020
Year: 2020

Risk-based Authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional features during login, and when observed feature values differ significantly from previously seen ones, users have to provide additional authentication factors such as a verification code. RBA has the potential to offer more usable authentication, but the usability and the security perceptions of RBA are not studied well. We present the results of a between-group lab study (n=65) to evaluate usability and security perceptions of two RBA variants, one 2FA variant, and password-only authentication. Our study shows with significant results that RBA is considered to be more usable than the studied 2FA variants, while it is perceived as more secure than password-only authentication in general and comparably secure to 2FA in a variety of application types. We also observed RBA usability problems and provide recommendations for mitigation. Our contribution provides a first deeper understanding of the users' perception of RBA and helps to improve RBA implementations for a broader user acceptance.
Can You Trust Your Pose? Confidence Estimation in Visual Localization
Venue: arXiv cs.CV 2020
Year: 2020

Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations. The research so far has focused on improving subcomponents of estimation pipelines, to achieve more accurate poses. However, there is no guarantee for the result to be correct, even though the correctness of pose estimation is critically important in several visual localization applications,such as in autonomous navigation. In this paper we bring to attention a novel research question, pose confidence estimation,where we aim at quantifying how reliable the visually estimated pose is. We develop a novel confidence measure to fulfil this task and show that it can be flexibly applied to different datasets,indoor or outdoor, and for various visual localization pipelines.We also show that the proposed techniques can be used to accomplish a secondary goal: improving the accuracy of existing pose estimation pipelines. Finally, the proposed approach is computationally light-weight and adds only a negligible increase to the computational effort of pose estimation.
When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey
Venue: arXiv cs.AI 2020
Year: 2020

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. In a few decades, computers may be capable of formulating diagnoses and choosing the correct treatment, while robots may perform surgical operations, and conversational agents could interact with patients as virtual coaches. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. In this scenario, important decisions will be controlled by standalone machines that have learned predictive models from provided data. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity in Python and Matlab libraries, just to name two, but to exploit all their possibilities, it is essential to fully understand how models are interpreted and which models are more interpretable than others. In this survey, we analyse current machine learning models, frameworks, databases and other related tools as applied to medicine - specifically, to cancer research - and we discuss their interpretability, performance and the necessary input data. From the evidence available, ANN, LR and SVM have been observed to be the preferred models. Besides, CNNs, supported by the rapid development of GPUs and tensor-oriented programming libraries, are gaining in importance. However, the interpretability of results by doctors is rarely considered which is a factor that needs to be improved. We therefore consider this study to be a timely contribution to the issue.
Citation Sentiment Changes Analysis
Venue: arXiv cs.CL 2020
Year: 2020

Metrics for measuring the citation sentiment changes were introduced. Citation sentiment changes can be observed from global citation sentiment sequences (GCSSs). With respect to a cited paper, the citation sentiment sequences were analysed across a collection of citing papers ordered by the published time. For analysing GCSSs, Eddy Dissipation Rate (EDR) was adopted, with the hypothesis that the GCSSs pattern differences can be spotted by EDR based method. Preliminary evidence showed that EDR based method holds the potential for analysing a publication's impact in a time series fashion.
Deep matrix factorizations
Venue: arXiv cs.LG 2020
Year: 2020

Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques to be able to extract the information contained in large data sets in a relevant way. However, such low-rank approaches are unable to mine complex, interleaved features that underlie hierarchical semantics. Recently, deep matrix factorization (deep MF) was introduced to deal with the extraction of several layers of features and has been shown to reach outstanding performances on unsupervised tasks. Deep MF was motivated by the success of deep learning, as it is conceptually close to some neural networks paradigms. In this paper, we present the main models, algorithms, and applications of deep MF through a comprehensive literature review. We also discuss theoretical questions and perspectives of research.
A Deep Learning Framework for COVID Outbreak Prediction
Venue: arXiv cs.LG 2020
Year: 2020

The outbreak of COVID-19 i.e. a variation of coronavirus, also known as novel corona virus causing respiratory disease is a big concern worldwide since the end of December 2019. As of September 12, 2020, it has turned into an epidemic outbreak with more than 29 million confirmed cases and around 1 million reported deaths worldwide. It has created an urgent need to monitor and forecast COVID-19 spread behavior to better control this spread. Among all the popular models for COVID-19 forecasting, statistical models are receiving much attention in media. However, statistical models are showing less accuracy for long term forecasting, as there is high level of uncertainty and required data is also not sufficiently available. In this paper, we propose a comparative analysis of deep learning models to forecast the COVID-19 outbreak as an alternative to statistical models. We propose a new Attention-based encoder-decoder model, named Attention-Long Short Term Memory (AttentionLSTM). LSTM based neural network layer architecture incorporates the idea of fine-grained attention mechanism i.e., attention on hidden state dimensions instead of hidden state vector itself, which is capable of highlighting the importance and contribution of each hidden state dimension. It helps in detection on crucial temporal information, resulting in a highly interpretable network. Additionally, we implement a learnable vector embedding for time. As, time in a vector representation can be easily added with many architectures. This vector representation is called Time2Vec. We have used COVID-19 data repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University to assess the proposed model's performance. The proposed model give superior forecasting accuracy compared to other existing methods.
Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging
Venue: arXiv cs.DS 2020
Year: 2020

We introduce and study a general version of the fractional online knapsack problem with multiple knapsacks, heterogeneous constraints on which items can be assigned to which knapsack, and rate-limiting constraints on the assignment of items to knapsacks. This problem generalizes variations of the knapsack problem and of the one-way trading problem that have previously been treated separately, and additionally finds application to the real-time control of electric vehicle (EV) charging. We introduce a new algorithm that achieves a competitive ratio within an additive factor of one of the best achievable competitive ratios for the general problem and matches or improves upon the best-known competitive ratio for special cases in the knapsack and one-way trading literatures. Moreover, our analysis provides a novel approach to online algorithm design based on an instance-dependent primal-dual analysis that connects the identification of worst-case instances to the design of algorithms. Finally, we illustrate the proposed algorithm via trace-based experiments of EV charging.
Supercomputing with MPI meets the Common Workflow Language standards: an experience report
Venue: arXiv cs.DC 2020
Year: 2020

Use of standards-based workflows is still somewhat unusual by high-performance computing users. In this paper we describe the experience of using the Common Workflow Language (CWL) standards to describe the execution, in parallel, of MPI-parallelised applications. In particular, we motivate and describe the simple extension to the specification which was required, as well as our implementation of this within the CWL reference runner. We discuss some of the unexpected benefits, such as simple use of HPC-oriented performance measurement tools, and CWL software requirements interfacing with HPC module systems. We close with a request for comment from the community on how these features could be adopted within versions of the CWL standards.
Community detection, pattern recognition, and hypergraph-based learning: approaches using metric geometry and persistent homology
Venue: arXiv cs.SI 2020
Year: 2020

Hypergraph data appear and are hidden in many places in the modern age. They are data structure that can be used to model many real data examples since their structures contain information about higher order relations among data points. One of the main contributions of our paper is to introduce a new topological structure to hypergraph data which bears a resemblance to a usual metric space structure. Using this new topological space structure of hypergraph data, we propose several approaches to study community detection problem, detecting persistent features arising from homological structure of hypergraph data. Also based on the topological space structure of hypergraph data introduced in our paper, we introduce a modified nearest neighbors methods which is a generalization of the classical nearest neighbors methods from machine learning. Our modified nearest neighbors methods have an advantage of being very flexible and applicable even for discrete structures as in hypergraphs. We then apply our modified nearest neighbors methods to study sign prediction problem in hypegraph data constructed using our method.
A baseline Model for the Relationships between Network Operators and Tower Companies
Venue: arXiv cs.NI 2020
Year: 2020

The introduction of virtualization techniques in radio cellular networks allows the emergence of a business based on the outsourcing of towers hosting antennas and operated by the so-called Tower Companies (TowerCos). In this paper, we develop a baseline business model for studying the potential relationships between network operators and TowerCos. It turns out that the gain in operational costs achieved when network operators outsource the management of towers can be gracefully utilized to reduce prices so as to attract more customers. The price drop has however to be carefully realized so as not to break the market share between operators and to preserve competition. To prove this claim, we adopt in a first step a centralized optimization formulation. In a second step, we develop a game theoretic framework.
Developing Effective Community Network Analysis Tools According to Visualization Psychology
Venue: arXiv cs.HC 2020
Year: 2020

Visualization is a useful technology in health science, and especially for community network analysis. Because visualization applications in healthcare are typically risk-averse, health psychologists can play a significant role in ensuring appropriate and effective uses of visualization techniques in healthcare. In this paper, we examine the role of health psychologists in the triangle of "health science", "visualization technology", and "visualization psychology". We conclude that health psychologists can use visualization to aid data intelligence workflows in healthcare and health psychology, while researching into visualization psychology to aid the improvement and optimization of data visualization processes.
LR-FHSS: Overview and Performance Analysis
Venue: arXiv cs.NI 2020
Year: 2020

Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is the new physical layer designed to address extremely long-range and large-scale communication scenarios, such as satellite IoT. At its core is a fast frequency hopping technique designed to offer higher network capacity while offering the same radio link budget as LoRa. Additionally, LR-FHSS finely manages packet transmission thanks to its design principles, enabling QoS policies on a per-packet basis. Given the notorious adoption of LoRaWAN in the IoT application landscape, this article is a reference for understanding how exactly LR-FHSS works, the performance it can offer, and its limitations and research opportunities.
A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users
Venue: arXiv cs.CV 2020
Year: 2020

The ageing population trend is correlated with an increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. Researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In particular, screening for dementia in ageing Deaf signers of British Sign Language (BSL) poses additional challenges as the diagnostic process is bound up with conditions such as quality and availability of interpreters, as well as appropriate questionnaires and cognitive tests. On the other hand, deep learning based approaches for image and video analysis and understanding are promising, particularly the adoption of Convolutional Neural Network (CNN), which require large amounts of training data. In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of any sign language, since it is language independent, c) given the trade-off between complexity and accuracy of machine learning (ML) prediction models as well as the limited amount of training and testing data being available, we show that our approach is not over-fitted and has the potential to scale up.
CARLA: A Convolution Accelerator with a Reconfigurable and Low-Energy Architecture
Venue: arXiv cs.AR 2020
Year: 2020

Convolutional Neural Networks (CNNs) have proven to be extremely accurate for image recognition, even outperforming human recognition capability. When deployed on battery-powered mobile devices, efficient computer architectures are required to enable fast and energy-efficient computation of costly convolution operations. Despite recent advances in hardware accelerator design for CNNs, two major problems have not yet been addressed effectively, particularly when the convolution layers have highly diverse structures: (1) minimizing energy-hungry off-chip DRAM data movements; (2) maximizing the utilization factor of processing resources to perform convolutions. This work thus proposes an energy-efficient architecture equipped with several optimized dataflows to support the structural diversity of modern CNNs. The proposed approach is evaluated by implementing convolutional layers of VGGNet-16 and ResNet-50. Results show that the architecture achieves a Processing Element (PE) utilization factor of 98% for the majority of 3x3 and 1x1 convolutional layers, while limiting latency to 396.9 ms and 92.7 ms when performing convolutional layers of VGGNet-16 and ResNet-50, respectively. In addition, the proposed architecture benefits from the structured sparsity in ResNet-50 to reduce the latency to 42.5 ms when half of the channels are pruned.
Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling
Venue: arXiv cs.CL 2020
Year: 2020

This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity, an established social theory framework for reasoning about privacy norms. Privacy policies, written by lawyers, are lengthy and often comprise incomplete and vague statements. In this paper, we show that traditional NLP tasks, including the recently proposed Question-Answering based solutions, are insufficient to address the privacy parameter extraction problem and provide poor precision and recall. We describe 4 different types of conventional methods that can be partially adapted to address the parameter extraction task with varying degrees of success: Hidden Markov Models, BERT fine-tuned models, Dependency Type Parsing (DP) and Semantic Role Labeling (SRL). Based on a detailed evaluation across 36 real-world privacy policies of major enterprises, we demonstrate that a solution combining syntactic DP coupled with type-specific SRL tasks provides the highest accuracy for retrieving contextual privacy parameters from privacy statements. We also observe that incorporating domain-specific knowledge is critical to achieving high precision and recall, thus inspiring new NLP research to address this important problem in the privacy domain.