OpenRaiser/Intern-Atlas huggingface dataset:
Compliant Fins for Locomotion in Granular Media
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we present an approach to study the behavior of compliant plates in granular media and optimize the performance of a robot that utilizes this technique for mobility. From previous work and fundamental tests on thin plate force generation inside granular media, we introduce an origami-inspired mechanism with non-linear compliance in the joints that can be used in granular propulsion. This concept utilizes one-sided joint limits to create an asymmetric gait cycle that avoids more complicated alternatives often found in other swimming/digging robots. To analyze its locomotion as well as its shape and propulsive force, we utilize granular Resistive Force Theory (RFT) as a starting point. Adding compliance to this theory enables us to predict the time-based evolution of compliant plates when they are dragged and rotated. It also permits more rational design of swimming robots where fin design variables may be optimized against the characteristics of the granular medium. This is done using a Python-based dynamic simulation library to model the deformation of the plates and optimize aspects of the robot's gait. Finally, we prototype and test robot with a gait optimized using the modelling techniques mentioned above.
User perspectives on critical factors for collaborative playlists
Venue: arXiv cs.HC 2021
Year: 2021

Collaborative playlists (CP) enable listeners to curate music together, translating long-standing social practices around music consumption into the age of streaming. Yet despite their role in connecting people through music, we lack an understanding of factors that are critical to CPs and their enjoyment. To understand what users consider important to CPs and their usage, we investigated aspects that are perceived to be most useful and lacking in today's CP implementations. We conducted a survey to collect open-ended text responses from real-world CP users. Using thematic analysis, we derived the Codebook of Critical CP Factors, which comprises eight aspects. We gained insights into which aspects are particularly useful, and which are absent and desired by current CP users. From these findings we propose design implications to inform further design of CP functionalities and platforms, and highlight potential benefits and challenges related to their adoption in current music services.
A Fault Tolerant Mechanism for Partitioning and Offloading Framework in Pervasive Environments
Venue: arXiv cs.DC 2021
Year: 2021

Application partitioning and code offloading are being researched extensively during the past few years. Several frameworks for code offloading have been proposed. However, fewer works attempted to address issues occurred with its implementation in pervasive environments such as frequent network disconnection due to high mobility of users. Thus, in this paper, we proposed a fault tolerant algorithm that helps in consolidating the efficiency and robustness of application partitioning and offloading frameworks. To permit the usage of different fault tolerant policies such as replication and checkpointing, the devices are grouped into high and low reliability clusters. Experimental results shown that the fault tolerant algorithm can easily adapt to different execution conditions while incurring minimum overhead.
Running Time Analysis of Broadcast Consensus Protocols
Venue: arXiv cs.DC 2021
Year: 2021

Broadcast consensus protocols (BCPs) are a model of computation, in which anonymous, identical, finite-state agents compute by sending/receiving global broadcasts. BCPs are known to compute all number predicates in $\mathsf{NL}=\mathsf{NSPACE}(\log n)$ where $n$ is the number of agents. They can be considered an extension of the well-established model of population protocols. This paper investigates execution time characteristics of BCPs. We show that every predicate computable by population protocols is computable by a BCP with expected $\mathcal{O}(n \log n)$ interactions, which is asymptotically optimal. We further show that every log-space, randomized Turing machine can be simulated by a BCP with $\mathcal{O}(n \log n \cdot T)$ interactions in expectation, where $T$ is the expected runtime of the Turing machine. This allows us to characterise polynomial-time BCPs as computing exactly the number predicates in $\mathsf{ZPL}$, i.e. predicates decidable by log-space bounded randomised Turing machine with zero-error in expected polynomial time where the input is encoded as unary.
Multi-Domain Image-to-Image Translation with Adaptive Inference Graph
Venue: arXiv cs.CV 2021
Year: 2021

In this work, we address the problem of multi-domain image-to-image translation with particular attention paid to computational cost. In particular, current state of the art models require a large and deep model in order to handle the visual diversity of multiple domains. In a context of limited computational resources, increasing the network size may not be possible. Therefore, we propose to increase the network capacity by using an adaptive graph structure. At inference time, the network estimates its own graph by selecting specific sub-networks. Sub-network selection is implemented using Gumbel-Softmax in order to allow end-to-end training. This approach leads to an adjustable increase in number of parameters while preserving an almost constant computational cost. Our evaluation on two publicly available datasets of facial and painting images shows that our adaptive strategy generates better images with fewer artifacts than literature methods
Deductive Systems and Coherence for Skew Prounital Closed Categories
Venue: arXiv cs.LO 2021
Year: 2021

In this paper, we develop the proof theory of skew prounital closed categories. These are variants of the skew closed categories of Street where the unit is not represented. Skew closed categories in turn are a weakening of the closed categories of Eilenberg and Kelly where no structural law is required to be invertible. The presence of a monoidal structure in these categories is not required. We construct several equivalent presentations of the free skew prounital closed category on a given set of generating objects: a categorical calculus (Hilbert-style system), a cut-free sequent calculus and a natural deduction system corresponding to a variant of planar (= non-commutative linear) typed lambda-calculus. We solve the coherence problem for skew prounital closed categories by showing that the sequent calculus admits focusing and presenting two reduction-free normalization procedures for the natural deduction calculus: normalization by evaluation and hereditary substitutions. Normal natural deduction derivations (beta-eta-long forms) are in one-to-one correspondence with derivations in the focused sequent calculus. Unexpectedly, the free skew prounital closed category on a set satisfies a left-normality condition which makes it lose its skew aspect. This pitfall can be avoided by considering the free skew prounital closed category on a skew multicategory instead. The latter has a presentation as a cut-free sequent calculus for which it is easy to see that the left-normality condition generally fails. The whole development has been fully formalized in the dependently typed programming language Agda.
WDR FACE: The First Database for Studying Face Detection in Wide Dynamic Range
Venue: arXiv cs.CV 2021
Year: 2021

Currently, face detection approaches focus on facial information by varying specific parameters including pose, occlusion, lighting, background, race, and gender. These studies only utilized the information obtained from low dynamic range images, however, face detection in wide dynamic range (WDR) scenes has received little attention. To our knowledge, there is no publicly available WDR database for face detection research. To facilitate and support future face detection research in the WDR field, we propose the first WDR database for face detection, called WDR FACE, which contains a total of 398 16-bit megapixel grayscale wide dynamic range images collected from 29 subjects. These WDR images (WDRIs) were taken in eight specific WDR scenes. The dynamic range of 90% images surpasses 60,000:1, and that of 70% images exceeds 65,000:1. Furthermore, we show the effect of different face detection procedures on the WDRIs in our database. This is done with 25 different tone mapping operators and five different face detectors. We provide preliminary experimental results of face detection on this unique WDR database.
The Semantic Adjacency Criterion in Time Intervals Mining
Venue: arXiv cs.LG 2021
Year: 2021

Frequent temporal patterns discovered in time-interval-based multivariate data, although syntactically correct, might be non-transparent: For some pattern instances, there might exist intervals for the same entity that contradict the pattern's usual meaning. We conjecture that non-transparent patterns are also less useful as classification or prediction features. We propose a new pruning constraint during a frequent temporal-pattern discovery process, the Semantic Adjacency Criterion [SAC], which exploits domain knowledge to filter out patterns that contain potentially semantically contradictory components. We have defined three SAC versions, and tested their effect in three medical domains. We embedded these criteria in a frequent-temporal-pattern discovery framework. Previously, we had informally presented the SAC principle and showed that using it to prune patterns enhances the repeatability of their discovery in the same clinical domain. Here, we define formally the semantics of three SAC variations, and compare the use of the set of pruned patterns to the use of the complete set of discovered patterns, as features for classification and prediction tasks in three different medical domains. We induced four classifiers for each task, using four machine-learning methods: Random Forests, Naive Bayes, SVM, and Logistic Regression. The features were frequent temporal patterns discovered in each data set. SAC-based temporal pattern-discovery reduced by up to 97% the number of discovered patterns and by up to 98% the discovery runtime. But the classification and prediction performance of the reduced SAC-based pattern-based features set, was as good as when using the complete set. Using SAC can significantly reduce the number of discovered frequent interval-based temporal patterns, and the corresponding computational effort, without losing classification or prediction performance.
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing
Venue: arXiv cs.CV 2021
Year: 2021

Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effectively capture long-range dependencies with self-attention mechanism while short ones by local convolution. However, there is still much gap between long-range and short-range dependencies, which largely reduces the models' flexibility in application to diverse spatial scales and relationships in complicated natural scene images. To fill such a gap, we develop a Middle-Range (MR) branch to capture middle-range dependencies by restricting self-attention into local patches. Also, we observe that the spatial regions which have large correlations with others can be emphasized to exploit long-range dependencies more accurately, and thus propose a Reweighed Long-Range (RLR) branch. Based on the proposed MR and RLR branches, we build an Omni-Range Dependencies Network (ORDNet) which can effectively capture short-, middle- and long-range dependencies. Our ORDNet is able to extract more comprehensive context information and well adapt to complex spatial variance in scene images. Extensive experiments show that our proposed ORDNet outperforms previous state-of-the-art methods on three scene parsing benchmarks including PASCAL Context, COCO Stuff and ADE20K, demonstrating the superiority of capturing omni-range dependencies in deep models for scene parsing task.
How Much Automation Does a Data Scientist Want?
Venue: arXiv cs.LG 2021
Year: 2021

Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications. There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle. However, do DS and ML workers really want to automate their DS/ML workflow? To answer this question, we first synthesize a human-centered AutoML framework with 6 User Role/Personas, 10 Stages and 43 Sub-Tasks, 5 Levels of Automation, and 5 Types of Explanation, through reviewing research literature and marketing reports. Secondly, we use the framework to guide the design of an online survey study with 217 DS/ML workers who had varying degrees of experience, and different user roles "matching" to our 6 roles/personas. We found that different user personas participated in distinct stages of the lifecycle -- but not all stages. Their desired levels of automation and types of explanation for AutoML also varied significantly depending on the DS/ML stage and the user persona. Based on the survey results, we argue there is no rationale from user needs for complete automation of the end-to-end DS/ML lifecycle. We propose new next steps for user-controlled DS/ML automation.
Strictly In-Place Algorithms for Permuting and Inverting Permutations
Venue: arXiv cs.DS 2021
Year: 2021

We revisit the problem of permuting an array of length $n$ according to a given permutation in place, that is, using only a small number of bits of extra storage. Fich, Munro and Poblete [FOCS 1990, SICOMP 1995] obtained an elegant $\mathcal{O}(n\log n)$-time algorithm using only $\mathcal{O}(\log^{2}n)$ bits of extra space for this basic problem by designing a procedure that scans the permutation and outputs exactly one element from each of its cycles. However, in the strict sense in place should be understood as using only an asymptotically optimal $\mathcal{O}(\log n)$ bits of extra space, or storing a constant number of indices. The problem of permuting in this version is, in fact, a well-known interview question, with the expected solution being a quadratic-time algorithm. Surprisingly, no faster algorithm seems to be known in the literature. Our first contribution is a strictly in-place generalisation of the method of Fich et al. that works in $\mathcal{O}_{\varepsilon}(n^{1+\varepsilon})$ time, for any $\varepsilon > 0$. Then, we build on this generalisation to obtain a strictly in-place algorithm for inverting a given permutation on $n$ elements working in the same complexity. This is a significant improvement on a recent result of Guśpiel [arXiv 2019], who designed an $\mathcal{O}(n^{1.5})$-time algorithm.
Evaluating Deep Learning Approaches for Covid19 Fake News Detection
Venue: arXiv cs.LG 2021
Year: 2021

Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the wrong direction but also claimed human lives. During these critical times of the Covid19 pandemic, it is easy to mislead people and make them believe in fatal information. Therefore it is important to curb fake news at source and prevent it from spreading to a larger audience. We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. The classification algorithms are based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). We also evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus. We report the best accuracy of 98.41\% on the Covid-19 Fake news detection dataset.
Distributed Double Machine Learning with a Serverless Architecture
Venue: arXiv cs.DC 2021
Year: 2021

This paper explores serverless cloud computing for double machine learning. Being based on repeated cross-fitting, double machine learning is particularly well suited to exploit the high level of parallelism achievable with serverless computing. It allows to get fast on-demand estimations without additional cloud maintenance effort. We provide a prototype Python implementation \texttt{DoubleML-Serverless} for the estimation of double machine learning models with the serverless computing platform AWS Lambda and demonstrate its utility with a case study analyzing estimation times and costs.
Context- and Sequence-Aware Convolutional Recurrent Encoder for Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Neural Machine Translation model is a sequence-to-sequence converter based on neural networks. Existing models use recurrent neural networks to construct both the encoder and decoder modules. In alternative research, the recurrent networks were substituted by convolutional neural networks for capturing the syntactic structure in the input sentence and decreasing the processing time. We incorporate the goodness of both approaches by proposing a convolutional-recurrent encoder for capturing the context information as well as the sequential information from the source sentence. Word embedding and position embedding of the source sentence is performed prior to the convolutional encoding layer which is basically a n-gram feature extractor capturing phrase-level context information. The rectified output of the convolutional encoding layer is added to the original embedding vector, and the sum is normalized by layer normalization. The normalized output is given as a sequential input to the recurrent encoding layer that captures the temporal information in the sequence. For the decoder, we use the attention-based recurrent neural network. Translation task on the German-English dataset verifies the efficacy of the proposed approach from the higher BLEU scores achieved as compared to the state of the art.
Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study
Venue: arXiv cs.CV 2021
Year: 2021

Colorectal polyps are abnormal tissues growing on the intima of the colon or rectum with a high risk of developing into colorectal cancer, the third leading cause of cancer death worldwide. Early detection and removal of colon polyps via colonoscopy have proved to be an effective approach to prevent colorectal cancer. Recently, various CNN-based computer-aided systems have been developed to help physicians detect polyps. However, these systems do not perform well in real-world colonoscopy operations due to the significant difference between images in a real colonoscopy and those in the public datasets. Unlike the well-chosen clear images with obvious polyps in the public datasets, images from a colonoscopy are often blurry and contain various artifacts such as fluid, debris, bubbles, reflection, specularity, contrast, saturation, and medical instruments, with a wide variety of polyps of different sizes, shapes, and textures. All these factors pose a significant challenge to effective polyp detection in a colonoscopy. To this end, we collect a private dataset that contains 7,313 images from 224 complete colonoscopy procedures. This dataset represents realistic operation scenarios and thus can be used to better train the models and evaluate a system's performance in practice. We propose an integrated system architecture to address the unique challenges for polyp detection. Extensive experiments results show that our system can effectively detect polyps in a colonoscopy with excellent performance in real time.
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text
Venue: arXiv cs.CL 2021
Year: 2021

The social media platform is a convenient medium to express personal thoughts and share useful information. It is fast, concise, and has the ability to reach millions. It is an effective place to archive thoughts, share artistic content, receive feedback, promote products, etc. Despite having numerous advantages these platforms have given a boost to hostile posts. Hate speech and derogatory remarks are being posted for personal satisfaction or political gain. The hostile posts can have a bullying effect rendering the entire platform experience hostile. Therefore detection of hostile posts is important to maintain social media hygiene. The problem is more pronounced languages like Hindi which are low in resources. In this work, we present approaches for hostile text detection in the Hindi language. The proposed approaches are evaluated on the Constraint@AAAI 2021 Hindi hostility detection dataset. The dataset consists of hostile and non-hostile texts collected from social media platforms. The hostile posts are further segregated into overlapping classes of fake, offensive, hate, and defamation. We evaluate a host of deep learning approaches based on CNN, LSTM, and BERT for this multi-label classification problem. The pre-trained Hindi fast text word embeddings by IndicNLP and Facebook are used in conjunction with CNN and LSTM models. Two variations of pre-trained multilingual transformer language models mBERT and IndicBERT are used. We show that the performance of BERT based models is best. Moreover, CNN and LSTM models also perform competitively with BERT based models.
BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
Venue: arXiv cs.CL 2021
Year: 2021

A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation. To automatically extract information from biomedical literature, existing biomedical text-mining approaches typically formulate the problem as a cross-sentence n-ary relation-extraction task that detects relations among n entities across multiple sentences, and use either a graph neural network (GNN) with long short-term memory (LSTM) or an attention mechanism. Recently, Transformer has been shown to outperform LSTM on many natural language processing (NLP) tasks. In this work, we propose a novel architecture that combines Bidirectional Encoder Representations from Transformers with Graph Transformer (BERT-GT), through integrating a neighbor-attention mechanism into the BERT architecture. Unlike the original Transformer architecture, which utilizes the whole sentence(s) to calculate the attention of the current token, the neighbor-attention mechanism in our method calculates its attention utilizing only its neighbor tokens. Thus, each token can pay attention to its neighbor information with little noise. We show that this is critically important when the text is very long, as in cross-sentence or abstract-level relation-extraction tasks. Our benchmarking results show improvements of 5.44% and 3.89% in accuracy and F1-measure over the state-of-the-art on n-ary and chemical-protein relation datasets, suggesting BERT-GT is a robust approach that is applicable to other biomedical relation extraction tasks or datasets.