OpenRaiser/Intern-Atlas huggingface dataset:
Tackling problems of marker-based augmented reality under water
Venue: arXiv cs.CV 2020
Year: 2020

Underwater sites are a harsh environment for augmented reality applications. Obstacles that must be battled include poor visibility conditions, difficult navigation, and hard manipulation with devices under water. This chapter focuses on the problem of localizing a device under water using markers. It discusses various filters that enhance and improve images recorded under water, and their impact on marker-based tracking. It presents various combinations of 10 image improving algorithms and 4 marker detecting algorithms, and tests their performance in real situations. All solutions are designed to run real-time on mobile devices to provide a solid basis for augmented reality. Usability of this solution is evaluated on locations in Mediterranean Sea. It is shown that image improving algorithms with carefully chosen parameters can reduce the problems with visibility under water and improve the detection of markers. The best results are obtained with marker detecting algorithms that are specifically designed for underwater environments.
Generative Model-Enhanced Human Motion Prediction
Venue: arXiv cs.CV 2020
Year: 2020

The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here we formulate a new OoD benchmark based on the Human3.6M and CMU motion capture datasets, and introduce a hybrid framework for hardening discriminative architectures to OoD failure by augmenting them with a generative model. When applied to current state-of-the-art discriminative models, we show that the proposed approach improves OoD robustness without sacrificing in-distribution performance, and can theoretically facilitate model interpretability. We suggest human motion predictors ought to be constructed with OoD challenges in mind, and provide an extensible general framework for hardening diverse discriminative architectures to extreme distributional shift. The code is available at https://github.com/bouracha/OoDMotion.
Spatial Attention as an Interface for Image Captioning Models
Venue: arXiv cs.CV 2020
Year: 2020

The internal workings of modern deep learning models stay often unclear to an external observer, although spatial attention mechanisms are involved. The idea of this work is to translate these spatial attentions into natural language to provide a simpler access to the model's function. Thus, I took a neural image captioning model and measured the reactions to external modification in its spatial attention for three different interface methods: a fixation over the whole generation process, a fixation for the first time-steps and an addition to the generator's attention. The experimental results for bounding box based spatial attention vectors have shown that the captioning model reacts to method dependent changes in up to 52.65% and includes in 9.00% of the cases object categories, which were otherwise unmentioned. Afterwards, I established such a link to a hierarchical co-attention network for visual question answering by extraction of its word, phrase and question level spatial attentions. Here, generated captions for the word level included details of the question-answer pairs in up to 55.20% of the cases. This work indicates that spatial attention seen as an external interface for image caption generators is an useful method to access visual functions in natural language.
Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
Venue: arXiv cs.RO 2020
Year: 2020

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. However, modern LfD techniques, e.g. inverse reinforcement learning (IRL), assume users provide at least stochastically optimal demonstrations. This assumption fails to hold in most real-world scenarios. Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings and following the Luce-Shepard rule. However, we show these approaches make incorrect assumptions and thus suffer from brittle, degraded performance. We overcome these limitations in developing a novel approach that bootstraps off suboptimal demonstrations to synthesize optimality-parameterized data to train an idealized reward function. We empirically validate we learn an idealized reward function with ~0.95 correlation with ground-truth reward versus ~0.75 for prior work. We can then train policies achieving ~200% improvement over the suboptimal demonstration and ~90% improvement over prior work. We present a physical demonstration of teaching a robot a topspin strike in table tennis that achieves 32% faster returns and 40% more topspin than user demonstration.
A Qualitative Analysis of Haptic Feedback in Music Focused Exercises
Venue: arXiv cs.HC 2020
Year: 2020

We present the findings of a pilot-study that analysed the role of haptic feedback in a musical context. To examine the role of haptics in Digital Musical Instrument (DMI) design an experiment was formulated to measure the users' perception of device usability across four separate feedback stages: fully haptic (force and tactile combined), constant force only, vibrotactile only, and no feedback. The study was piloted over extended periods with the intention of exploring the application and integration of DMIs in real-world musical contexts. Applying a music orientated analysis of this type enabled the investigative process to not only take place over a comprehensive period, but allowed for the exploration of DMI integration in everyday compositional practices. As with any investigation that involves creativity, it was important that the participants did not feel rushed or restricted. That is, they were given sufficient time to explore and assess the different feedback types without constraint. This provided an accurate and representational set of qualitative data for validating the participants' experience with the different feedback types they were presented with.
Urban Sound Classification : striving towards a fair comparison
Venue: arXiv cs.SD 2020
Year: 2020

Urban sound classification has been achieving remarkable progress and is still an active research area in audio pattern recognition. In particular, it allows to monitor the noise pollution, which becomes a growing concern for large cities. The contribution of this paper is two-fold. First, we present our DCASE 2020 task 5 winning solution which aims at helping the monitoring of urban noise pollution. It achieves a macro-AUPRC of 0.82 / 0.62 for the coarse / fine classification on validation set. Moreover, it reaches accuracies of 89.7% and 85.41% respectively on ESC-50 and US8k datasets. Second, it is not easy to find a fair comparison and to reproduce the performance of existing models. Sometimes authors copy-pasting the results of the original papers which is not helping reproducibility. As a result, we provide a fair comparison by using the same input representation, metrics and optimizer to assess performances. We preserve data augmentation used by the original papers. We hope this framework could help evaluate new architectures in this field. For better reproducibility, the code is available on our GitHub repository.
Hawkes Process Classification through Discriminative Modeling of Text
Venue: arXiv cs.SI 2020
Year: 2020

Social media has provided a platform for users to gather and share information and stay updated with the news. Such networks also provide a platform to users where they can engage in conversations. However, such micro-blogging platforms like Twitter restricts the length of text. Due to paucity of sufficient word occurrences in such posts, classification of this information is a challenging task using standard tools of natural language processing (NLP). Moreover, high complexity and dynamics of the posts in social media makes text classification a challenging problem. However, considering additional cues in the form of past labels and times associated with the post can be potentially helpful for performing text classification in a better way. To address this problem, we propose models based on the Hawkes process (HP) which can naturally incorporate the temporal features and past labels along with textual features for improving short text classification. In particular, we propose a discriminative approach to model text in HP where the text features parameterize the base intensity and/or the triggering kernel. Another major contribution is to consider kernel to be a function of both time and text, and further use a neural network to model the kernel. This enables modelling and effectively learning the text along with the historical influences for tweet classification. We demonstrate the advantages of the proposed techniques on standard benchmarks for rumour stance classification.
GAZED- Gaze-guided Cinematic Editing of Wide-Angle Monocular Video Recordings
Venue: arXiv cs.CV 2020
Year: 2020

We present GAZED- eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and user eye-gaze tracks are combined to generate an edited video comprising cinematically valid actor shots and shot transitions to generate an aesthetic and vivid representation of the original narrative. We model cinematic video editing as an energy minimization problem over shot selection, whose constraints capture cinematographic editing conventions. Gazed scene locations primarily determine the shots constituting the edited video. Effectiveness of GAZED against multiple competing methods is demonstrated via a psychophysical study involving 12 users and twelve performance videos.
Coded Data Rebalancing for Decentralized Distributed Databases
Venue: arXiv cs.DC 2020
Year: 2020

The performance of replication-based distributed databases is affected due to non-uniform storage across storage nodes (also called \textit{data skew}) and reduction in the replication factor during operation, particularly due to node additions or removals. Data rebalancing refers to the communication involved between the nodes in correcting this data skew, while maintaining the replication factor. For carefully designed distributed databases, transmitting coded symbols during the rebalancing phase has been recently shown to reduce the communication load of rebalancing. In this work, we look at balanced distributed databases with \textit{random placement}, in which each data segment is stored in a random subset of $r$ nodes in the system, where $r$ refers to the replication factor of the distributed database. We call these as decentralized databases. For a natural class of such decentralized databases, we propose rebalancing schemes for correcting data skew and the reduction in the replication factor arising due to a single node addition or removal. We give converse arguments which show that our proposed rebalancing schemes are optimal asymptotically in the size of the file.
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
Venue: arXiv cs.CL 2020
Year: 2020

Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and over how to extend the method to sequence labeling tasks. This method has recently gained traction for text classification. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these complex perturbation models. Furthermore, we find that applying its consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short: UDA need not be unsupervised, and does not require complex data augmentation to be effective.
Language Models are Open Knowledge Graphs
Venue: arXiv cs.CL 2020
Year: 2020

This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.
The Turking Test: Can Language Models Understand Instructions?
Venue: arXiv cs.CL 2020
Year: 2020

Supervised machine learning provides the learner with a set of input-output examples of the target task. Humans, however, can also learn to perform new tasks from instructions in natural language. Can machines learn to understand instructions as well? We present the Turking Test, which examines a model's ability to follow natural language instructions of varying complexity. These range from simple tasks, like retrieving the nth word of a sentence, to ones that require creativity, such as generating examples for SNLI and SQuAD in place of human intelligence workers ("turkers"). Despite our lenient evaluation methodology, we observe that a large pretrained language model performs poorly across all tasks. Analyzing the model's error patterns reveals that the model tends to ignore explicit instructions and often generates outputs that cannot be construed as an attempt to solve the task. While it is not yet clear whether instruction understanding can be captured by traditional language models, the sheer expressivity of instruction understanding makes it an appealing alternative to the rising few-shot inference paradigm.
Flaws in Flawlessness: Perfectionism as a New Technology Driven Mental Disorder
Venue: arXiv cs.CY 2020
Year: 2020

Today, technologies, devices and systems play a major role in our lives. Anecdotal commentary suggests that such technologies and our interactions with them create a false sense of perfectionism about life, events and its outcomes. While it is admirable to strive for better outcomes, constant and sometimes unrealistic expectations create a psychological condition commonly known as Perfectionism, the fear of not doing something right or the fear of not being good enough. In this paper, based on the Diagnostic Statistical Manual of Mental Disorders (DSM-III), we conceptualize digital perfectionism as an emerging disorder, that is specific to our increasing interactions with tools and technologies. By using a sample of 336 individuals, this study makes valuable early insights on digital perfectionism, its conceptualization and its effects on the individuals.
Digital Transformation: Environmental Friend or Foe? Panel Discussion at the Australasian Conference on Information Systems 2019
Venue: arXiv cs.CY 2020
Year: 2020

The advent of digital technologies such as social media, mobile, analytics, cloud computing and internet-of-things has provided unique opportunities for organizations to engage in innovations that are affordable, easy-to-use, easy-to-learn and easy-to-implement. Transformations through such technologies often have positive impacts on business processes, products and services. As such, organizations have managed to increase productivity and efficiency, reduce cycle time and make substantial gains through digital transformation. Such transformations have also been positively associated with reducing harmful environmental impacts by providing organizations alternative ways of undertaking their business activities. However, in recent times, especially with an abundance of technologies being available at near-zero costs, questions regarding the potential negative impacts of digital transformation on the environment have arisen. The morass of the ubiquitous technologies around us necessitates the continuing creation of large data centers, that are increasing their capacity yielding a negative impact on the environment. Considering this dialectical contradiction, a panel was conducted at the Australasian Conference on Information Systems (ACIS) in Perth, Australia, in 2019. Its aim was to invigorate the dialogue regarding the impact of digital transformation on environmental sustainability and suggested some directions for future research in this area.
Using Deep Image Priors to Generate Counterfactual Explanations
Venue: arXiv cs.LG 2020
Year: 2020

Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Though DIP inversion has been known to be superior to conventional regularized inversion strategies such as total variation, such an over-parameterized generator is able to effectively reconstruct even images that are not in the original data distribution. This limitation makes it challenging to utilize such priors for tasks such as counterfactual reasoning, wherein the goal is to generate small, interpretable changes to an image that systematically leads to changes in the model prediction. To this end, we propose a novel regularization strategy based on an auxiliary loss estimator jointly trained with the predictor, which efficiently guides the prior to recover natural pre-images. Our empirical studies with a real-world ISIC skin lesion detection problem clearly evidence the effectiveness of the proposed approach in synthesizing meaningful counterfactuals. In comparison, we find that the standard DIP inversion often proposes visually imperceptible perturbations to irrelevant parts of the image, thus providing no additional insights into the model behavior.
Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance
Venue: arXiv cs.LG 2020
Year: 2020

Optical spectral-temporal signatures extracted from videos of explosions provide information for identifying characteristics of the corresponding explosive devices. Currently, the identification is done using heuristic algorithms and direct subject matter expert review. An improvement in predictive performance may be obtained by using machine learning, but this application lends itself to high consequence national security decisions, so it is not only important to provide high accuracy but clear explanations for the predictions to garner confidence in the model. While much work has been done to develop explainability methods for machine learning models, not much of the work focuses on situations with input variables of the form of functional data such optical spectral-temporal signatures. We propose a procedure for explaining machine learning models fit using functional data that accounts for the functional nature the data. Our approach makes use of functional principal component analysis (fPCA) and permutation feature importance (PFI). fPCA is used to transform the functions to create uncorrelated functional principal components (fPCs). The model is trained using the fPCs as inputs, and PFI is applied to identify the fPCs important to the model for prediction. Visualizations are used to interpret the variability explained by the fPCs that are found to be important by PFI to determine the aspects of the functions that are important for prediction. We demonstrate the technique by explaining neural networks fit to explosion optical spectral-temporal signatures for predicting characteristics of the explosive devices.
Summarizing Utterances from Japanese Assembly Minutes using Political Sentence-BERT-based Method for QA Lab-PoliInfo-2 Task of NTCIR-15
Venue: arXiv cs.CL 2020
Year: 2020

There are many discussions held during political meetings, and a large number of utterances for various topics is included in their transcripts. We need to read all of them if we want to follow speakers\' intentions or opinions about a given topic. To avoid such a costly and time-consuming process to grasp often longish discussions, NLP researchers work on generating concise summaries of utterances. Summarization subtask in QA Lab-PoliInfo-2 task of the NTCIR-15 addresses this problem for Japanese utterances in assembly minutes, and our team (SKRA) participated in this subtask. As a first step for summarizing utterances, we created a new pre-trained sentence embedding model, i.e. the Japanese Political Sentence-BERT. With this model, we summarize utterances without labelled data. This paper describes our approach to solving the task and discusses its results.
GPS-Denied Navigation Using SAR Images and Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori. The distortions relative to the reference image are learned and exploited with a convolutional neural network to recover the initial navigational errors, which can be used to recover the true flight trajectory throughout the synthetic aperture. The proposed neural network approach is able to learn to predict the initial errors on both simulated and real SAR image data.
The nanoPU: Redesigning the CPU-Network Interface to Minimize RPC Tail Latency
Venue: arXiv cs.AR 2020
Year: 2020

The nanoPU is a new networking-optimized CPU designed to minimize tail latency for RPCs. By bypassing the cache and memory hierarchy, the nanoPU directly places arriving messages into the CPU register file. The wire-to-wire latency through the application is just 65ns, about 13x faster than the current state-of-the-art. The nanoPU moves key functions from software to hardware: reliable network transport, congestion control, core selection, and thread scheduling. It also supports a unique feature to bound the tail latency experienced by high-priority applications. Our prototype nanoPU is based on a modified RISC-V CPU; we evaluate its performance using cycle-accurate simulations of 324 cores on AWS FPGAs, including real applications (MICA and chain replication).