OpenRaiser/Intern-Atlas huggingface dataset:
Streaming Computations with Region-Based State on SIMD Architectures
Venue: arXiv cs.DC 2020
Year: 2020

Streaming computations on massive data sets are an attractive candidate for parallelization, particularly when they exhibit independence (and hence data parallelism) between items in the stream. However, some streaming computations are stateful, which disrupts independence and can limit parallelism. In this work, we consider how to extract data parallelism from streaming computations with a common, limited form of statefulness. The stream is assumed to be divided into variably-sized regions, and items in the same region are processed in a common context of state. In general, the computation to be performed on a stream is also irregular, with each item potentially undergoing different, data-dependent processing. This work describes mechanisms to implement such computations efficiently on a SIMD-parallel architecture such as a GPU. We first develop a low-level protocol by which a data stream can be augmented with control signals that are delivered to each stage of a computation at precise points in the stream. We then describe an abstraction, enumeration and aggregation, by which an application developer can specify the behavior of a streaming application with region-based state. Finally, we study an implementation of our ideas as part of the MERCATOR system for irregular streaming computations on GPUs, investigating how the frequency of region boundaries in a stream impacts SIMD occupancy and hence application performance.
Conversational User Interfaces for Blind Knowledge Workers: A Case Study
Venue: arXiv cs.HC 2020
Year: 2020

Modern trends in interface design for office equipment using controls on touch surfaces create greater obstacles for blind and visually impaired users and contribute to an environment of dependency in work settings. We believe that \textit{conversational user interfaces} (CUIs) offer a reasonable alternative to touchscreen interactions enabling more access and most importantly greater independence for blind knowledge workers. We present a case study of our work to develop a conversational user interface for accessibility for multifunction printers. We also describe our approach to conversational interfaces in general, which emphasizes task-based collaborative interactions between people and intelligent agents, and we detail the specifics of the solution we created for multifunction printers. To guide our design, we worked with a group of blind and visually impaired individuals starting with focus group sessions to ascertain the challenges our target users face in their professional lives. We followed our technology development with a user study to assess the solution and direct our future efforts. We present our findings and conclusions from the study.
Blockchain for Mobile Edge Computing: Consensus Mechanisms and Scalability
Venue: arXiv cs.DC 2020
Year: 2020

Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in a inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives.
Equivariant Neural Rendering
Venue: arXiv cs.CV 2020
Year: 2020

We propose a framework for learning neural scene representations directly from images, without 3D supervision. Our key insight is that 3D structure can be imposed by ensuring that the learned representation transforms like a real 3D scene. Specifically, we introduce a loss which enforces equivariance of the scene representation with respect to 3D transformations. Our formulation allows us to infer and render scenes in real time while achieving comparable results to models requiring minutes for inference. In addition, we introduce two challenging new datasets for scene representation and neural rendering, including scenes with complex lighting and backgrounds. Through experiments, we show that our model achieves compelling results on these datasets as well as on standard ShapeNet benchmarks.
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Venue: arXiv cs.CV 2020
Year: 2020

Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D volume reconstruction from freehand US scans require external tracking devices to provide spatial position for every frame. In this paper, we propose a deep contextual learning network (DCL-Net), which can efficiently exploit the image feature relationship between US frames and reconstruct 3D US volumes without any tracking device. The proposed DCL-Net utilizes 3D convolutions over a US video segment for feature extraction. An embedded self-attention module makes the network focus on the speckle-rich areas for better spatial movement prediction. We also propose a novel case-wise correlation loss to stabilize the training process for improved accuracy. Highly promising results have been obtained by using the developed method. The experiments with ablation studies demonstrate superior performance of the proposed method by comparing against other state-of-the-art methods. Source code of this work is publicly available at https://github.com/DIAL-RPI/FreehandUSRecon.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya
Venue: arXiv cs.CL 2020
Year: 2020

In recent years, transformer models have achieved great success in natural language processing (NLP) tasks. Most of the current state-of-the-art NLP results are achieved by using monolingual transformer models, where the model is pre-trained using a single language unlabelled text corpus. Then, the model is fine-tuned to the specific downstream task. However, the cost of pre-training a new transformer model is high for most languages. In this work, we propose a cost-effective transfer learning method to adopt a strong source language model, trained from a large monolingual corpus to a low-resource language. Thus, using XLNet language model, we demonstrate competitive performance with mBERT and a pre-trained target language model on the cross-lingual sentiment (CLS) dataset and on a new sentiment analysis dataset for low-resourced language Tigrinya. With only 10k examples of the given Tigrinya sentiment analysis dataset, English XLNet has achieved 78.88% F1-Score outperforming BERT and mBERT by 10% and 7%, respectively. More interestingly, fine-tuning (English) XLNet model on the CLS dataset has promising results compared to mBERT and even outperformed mBERT for one dataset of the Japanese language.
An Ontology for the Materials Design Domain
Venue: arXiv cs.DB 2020
Year: 2020

In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models. Therefore, the users have to face the challenges to find the data from adequate sources and integrate data from multiple sources. Ontologies and ontology-based techniques can address such problems as the formal representation of domain knowledge can make data more available and interoperable among different systems. In this paper, we introduce the Materials Design Ontology (MDO), which defines concepts and relations to cover knowledge in the field of materials design. MDO is designed using domain knowledge in materials science (especially in solid-state physics), and is guided by the data from several databases in the materials design field. We show the application of the MDO to materials data retrieved from well-known materials databases.
Through the Twitter Glass: Detecting Questions in Micro-Text
Venue: arXiv cs.CL 2020
Year: 2020

In a separate study, we were interested in understanding people's Q&A habits on Twitter. Finding questions within Twitter turned out to be a difficult challenge, so we considered applying some traditional NLP approaches to the problem. On the one hand, Twitter is full of idiosyncrasies, which make processing it difficult. On the other, it is very restricted in length and tends to employ simple syntactic constructions, which could help the performance of NLP processing. In order to find out the viability of NLP and Twitter, we built a pipeline of tools to work specifically with Twitter input for the task of finding questions in tweets. This work is still preliminary, but in this paper we discuss the techniques we used and the lessons we learned.
UAV-Aided Interference Assessment for Private 5G NR Deployments: Challenges and Solutions
Venue: arXiv cs.NI 2020
Year: 2020

Industrial automation has created a high demand for private 5G networks, the deployment of which calls for an efficient and reliable solution to ensure strict compliance with the regulatory emission limits. While traditional methods for measuring outdoor interference include collecting real-world data by walking or driving, the use of unmanned aerial vehicles (UAVs) offers an attractive alternative due to their flexible mobility and adaptive altitude. As UAVs perform measurements quickly and semiautomatically, they can potentially assist in near realtime adjustments of the network configuration and fine-tuning its parameters, such as antenna settings and transmit power, as well as help improve indoor connectivity while respecting outdoor emission constraints. This article offers a firsthand tutorial on using aerial 5G emission assessment for interference management in nonpublic networks (NPNs) by reviewing the key challenges of UAV-mounted radio-scanner measurements. Particularly, we (i) outline the challenges of practical assessment of the outdoor interference originating from a local indoor 5G network while discussing regulatory and other related constraints and (ii) address practical methods and tools while summarizing the recent results of our measurement campaign. The reported proof of concept confirms that UAV-based systems represent a promising tool for capturing outdoor interference from private 5G systems.
Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms
Venue: arXiv cs.LG 2020
Year: 2020

We formulate the problem of learning to imitate multiple, non-deterministic teachers with minimal interaction cost. Rather than learning a specific policy as in standard imitation learning, the goal in this problem is to learn a distribution over a policy space. We first present a general framework that efficiently models and estimates such a distribution by learning continuous representations of the teacher policies. Next, we develop Active Performance-Based Imitation Learning (APIL), an active learning algorithm for reducing the learner-teacher interaction cost in this framework. By making query decisions based on predictions of future progress, our algorithm avoids the pitfalls of traditional uncertainty-based approaches in the face of teacher behavioral uncertainty. Results on both toy and photo-realistic navigation tasks show that APIL significantly reduces the numbers of interactions with teachers without compromising on performance. Moreover, it is robust to various degrees of teacher behavioral uncertainty.
Relative Pose Estimation for Stereo Rolling Shutter Cameras
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present a novel linear algorithm to estimate the 6 DoF relative pose from consecutive frames of stereo rolling shutter (RS) cameras. Our method is derived based on the assumption that stereo cameras undergo motion with constant velocity around the center of the baseline, which needs 9 pairs of correspondences on both left and right consecutive frames. The stereo RS images enable the recovery of depth maps from the semi-global matching (SGM) algorithm. With the estimated camera motion and depth map, we can correct the RS images to get the undistorted images without any scene structure assumption. Experiments on both simulated points and synthetic RS images demonstrate the effectiveness of our algorithm in relative pose estimation.
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Venue: arXiv cs.CV 2020
Year: 2020

State-of-the-art deep networks implicitly encode gender information while being trained for face recognition. Gender is often viewed as an important attribute with respect to identifying faces. However, the implicit encoding of gender information in face descriptors has two major issues: (a.) It makes the descriptors susceptible to privacy leakage, i.e. a malicious agent can be trained to predict the face gender from such descriptors. (b.) It appears to contribute to gender bias in face recognition, i.e. we find a significant difference in the recognition accuracy of DCNNs on male and female faces. Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks. We show that AGENDA significantly reduces gender predictability of face descriptors. Consequently, we are also able to reduce gender bias in face verification while maintaining reasonable recognition performance.
Exploratory Analysis of a Social Media Network in Sri Lanka during the COVID-19 Virus Outbreak
Venue: arXiv cs.SI 2020
Year: 2020

During the COVID-19 pandemic, multiple aspects of human life were subjected to unprecedented changes, globally. In Sri Lanka, a developing country located in South Asia, it was possible to observe a range of events that arose due to the influence of the COVID-19 virus outbreak. Thus, the people of Sri Lanka used Social Media to voice their opinions regarding such events and those involved in them, enabling the ideal avenue to explore the social perception. However, the outcome of such actions was at certain times detrimental. This study was conducted as an attempt to identify the reasons for such instances as well as to identify the behaviours of the Sri Lankan populace during such a crisis event. To support this study, observations, as well as data of related posts from a sample of 50 sources, were manually collected from the most popular social media platform in Sri Lanka, Facebook. The posts considered spanned until approximately a month after the initial major virus outbreak in the country and contained content that even vaguely related to the virus. Utilising such data, various forms of analyses such as topic significance and topic co-occurrences were conducted. The findings highlight, while there can be social detrimental ideas shared, the majority of the posts point constructive and positive thoughts suggesting the successful influence from the cultural and social values Sri Lanka society promotes throughout.
Repulsive Curves
Venue: arXiv cs.GR 2020
Year: 2020

Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This paper develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited to problems in computational design. Our starting point is the so-called tangent-point energy, which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space. A reformulation of gradient descent, based on a Sobolev-Slobodeckij inner product enables us to make rapid progress toward local minima---independent of curve resolution. We also develop a hierarchical multigrid scheme that significantly reduces the per-step cost of optimization. The energy is easily integrated with a variety of constraints and penalties (e.g., inextensibility, or obstacle avoidance), which we use for applications including curve packing, knot untangling, graph embedding, non-crossing spline interpolation, flow visualization, and robotic path planning.
FinEst BERT and CroSloEngual BERT: less is more in multilingual models
Venue: arXiv cs.CL 2020
Year: 2020

Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. The research has been mostly focused on English language, though. While massively multilingual models exist, studies have shown that monolingual models produce much better results. We train two trilingual BERT-like models, one for Finnish, Estonian, and English, the other for Croatian, Slovenian, and English. We evaluate their performance on several downstream tasks, NER, POS-tagging, and dependency parsing, using the multilingual BERT and XLM-R as baselines. The newly created FinEst BERT and CroSloEngual BERT improve the results on all tasks in most monolingual and cross-lingual situations
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \subseteq \{0,1\}^d$, and in each round the learner pulls an action $x \in \mathcal{X}$ and receives a random reward with expectation $x^{\top} θ$, with $θ\in \mathbb{R}^d$ a latent and unknown environment vector. The objective is to identify the optimal action with the highest expected reward, using as few samples as possible. For CPE-BL, we design the first {\em polynomial-time adaptive} algorithm, whose sample complexity matches the lower bound (within a logarithmic factor) for a family of instances and has a light dependence of $Δ_{\min}$ (the smallest gap between the optimal action and sub-optimal actions). Furthermore, we propose a novel generalization of CPE-BL with flexible feedback structures, called combinatorial pure exploration with partial linear feedback (CPE-PL), which encompasses several families of sub-problems including full-bandit feedback, semi-bandit feedback, partial feedback and nonlinear reward functions. In CPE-PL, each pull of action $x$ reports a random feedback vector with expectation of $M_{x} θ$, where $M_x \in \mathbb{R}^{m_x \times d}$ is a transformation matrix for $x$, and gains a random (possibly nonlinear) reward related to $x$. For CPE-PL, we develop the first {\em polynomial-time} algorithm, which simultaneously addresses limited feedback, general reward function and combinatorial action space, and provide its sample complexity analysis. Our empirical evaluation demonstrates that our algorithms run orders of magnitude faster than the existing ones, and our CPE-BL algorithm is robust across different $Δ_{\min}$ settings while our CPE-PL algorithm is the only one returning correct answers for nonlinear reward functions.
Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback
Venue: arXiv cs.LG 2020
Year: 2020

Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee's suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Therefore, candidates need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee's facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results.
Fake Reviews Detection through Ensemble Learning
Venue: arXiv cs.LG 2020
Year: 2020

Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to conventional machine learning techniques. Motivated by the recent trends in ensemble learning, this paper evaluates the performance of ensemble learning-based approaches to identify bogus online information. The application of a number of ensemble learning-based approaches to a collection of fake restaurant reviews that we developed show that these ensemble learning-based approaches detect deceptive information better than conventional machine learning algorithms.
Categorical anomaly detection in heterogeneous data using minimum description length clustering
Venue: arXiv cs.DB 2020
Year: 2020

Fast and effective unsupervised anomaly detection algorithms have been proposed for categorical data based on the minimum description length (MDL) principle. However, they can be ineffective when detecting anomalies in heterogeneous datasets representing a mixture of different sources, such as security scenarios in which system and user processes have distinct behavior patterns. We propose a meta-algorithm for enhancing any MDL-based anomaly detection model to deal with heterogeneous data by fitting a mixture model to the data, via a variant of k-means clustering. Our experimental results show that using a discrete mixture model provides competitive performance relative to two previous anomaly detection algorithms, while mixtures of more sophisticated models yield further gains, on both synthetic datasets and realistic datasets from a security scenario.
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Venue: arXiv cs.LG 2020
Year: 2020

Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several crafting methods.