OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification
Venue: arXiv cs.CV 2020
Year: 2020

While recent person re-identification (ReID) methods achieve high accuracy in a supervised setting, their generalization to an unlabelled domain is still an open problem. In this paper, we introduce a novel unsupervised disentanglement generative adversarial network (UD-GAN) to address the domain adaptation issue of supervised person ReID. Our framework jointly trains a ReID network for discriminative features extraction in a source labelled domain using identity annotation, and adapts the ReID model to an unlabelled target domain by learning disentangled latent representations on the domain. Identity-unrelated features in the target domain are distilled from the latent features. As a result, the ReID features better encompass the identity of a person in the unsupervised domain. We conducted experiments on the Market1501, DukeMTMC and MSMT17 datasets. Results show that the unsupervised domain adaptation problem in ReID is very challenging. Nevertheless, our method shows improvement in half of the domain transfers and achieve state-of-the-art performance for one of them.
Canopy Density Estimation in Perennial Horticulture Crops Using 3D Spinning Lidar SLAM
Venue: arXiv cs.RO 2020
Year: 2020

We propose a novel, canopy density estimation solution using a 3D ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous-time 3D SLAM (Simultaneous Localisation and Mapping) to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The AgScan3D data is processed through a Continuous-Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Then, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analysed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons and 64 data collection campaigns, resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of vines with a combined total of approximately 93,000 scanned vines. Our experiments show canopy density repeatability of 3.8% (Relative RMSE) per vineyard panel, for acquisition speeds of 5-6 km/h, and under half the standard deviation in estimated densities when compared to an industry standard gap-fraction based solution. The code and field datasets are available at https://github.com/csiro-robotics/agscan3d.
Optimal Layout Synthesis for Quantum Computing
Venue: arXiv cs.AR 2020
Year: 2020

Recent years have witnessed the fast development of quantum computing. Researchers around the world are eager to run larger and larger quantum algorithms that promise speedups impossible to any classical algorithm. However, the available quantum computers are still volatile and error-prone. Thus, layout synthesis, which transforms quantum programs to meet these hardware limitations, is a crucial step in the realization of quantum computing. In this paper, we present two synthesizers, one optimal and one approximate but nearly optimal. Although a few optimal approaches to this problem have been published, our optimal synthesizer explores a larger solution space, thus is optimal in a stronger sense. In addition, it reduces time and space complexity exponentially compared to some leading optimal approaches. The key to this success is a more efficient spacetime-based variable encoding of the layout synthesis problem as a mathematical programming problem. By slightly changing our formulation, we arrive at an approximate synthesizer that is even more efficient and outperforms some leading heuristic approaches, in terms of additional gate cost, by up to 100%, and also fidelity by up to 10x on a comprehensive set of benchmark programs and architectures. For a specific family of quantum programs named QAOA, which is deemed to be a promising application for near-term quantum computers, we further adjust the approximate synthesizer by taking commutation into consideration, achieving up to 75% reduction in depth and up to 65% reduction in additional cost compared to the tool used in a leading QAOA study.
From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback
Venue: arXiv cs.CV 2020
Year: 2020

Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images. However, due to the difficulties in describing human facial appearances verbally and directly, people naturally tend to depict by referring to well-known existing images and comparing specific areas of faces with them and it is also challenging to provide complete comparison at each time. Therefore, we propose an end-to-end framework to retrieve facial images with relevance feedback progressively provided by the witness, enabling an exploitation of history information during multiple rounds and an interactive and iterative approach to retrieving the mental image. With no need of any extra annotations, our model can be applied at the cost of a little response effort. We experiment on \texttt{CelebA} and evaluate the performance by ranking percentile and achieve 99\% under the best setting. Since this topic remains little explored to the best of our knowledge, we hope our work can serve as a stepping stone for further research.
Deep learning for lithological classification of carbonate rock micro-CT images
Venue: arXiv cs.CV 2020
Year: 2020

In addition to the ongoing development, pre-salt carbonate reservoir characterization remains a challenge, primarily due to inherent geological particularities. These challenges stimulate the use of well-established technologies, such as artificial intelligence algorithms, for image classification tasks. Therefore, this work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images, thus making possible lithological classification. Four convolutional neural network models were proposed. The first model includes three convolutional layers followed by fully connected layers and is used as a base model for the following proposals. In the next two models, we replace the max pooling layer with a spatial pyramid pooling and a global average pooling layer. The last model uses a combination of spatial pyramid pooling followed by global average pooling in place of the last pooling layer. All models are compared using original images, when possible, as well as resized images. The dataset consists of 6,000 images from three different classes. The model performances were evaluated by each image individually, as well as by the most frequently predicted class for each sample. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second. We developed a workflow to automate and accelerate the lithology classification of Brazilian pre-salt carbonate samples by categorizing microtomographic images using deep learning algorithms in a non-destructive way.
PR-NN: RNN-based Detection for Coded Partial-Response Channels
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E2PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training. Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals.
An Asymptotic Lower Bound for Online Vector Bin Packing
Venue: arXiv cs.DS 2020
Year: 2020

We consider the online vector bin packing problem where $n$ items specified by $d$-dimensional vectors must be packed in the fewest number of identical $d$-dimensional bins. Azar et al. (STOC'13) showed that for any online algorithm $A$, there exist instances I, such that $A(I)$, the number of bins used by $A$ to pack $I$, is $Ω(d/\log^2 d)$ times $OPT(I)$, the minimal number of bins to pack $I$. However in those instances, $OPT(I)$ was only $O(\log d)$, which left open the possibility of improved algorithms with better asymptotic competitive ratio when $OPT(I) \gg d$. We rule this out by showing that for any arbitrary function $q(\cdot)$ and any randomized online algorithm $A$, there exist instances $I$ such that $ E[A(I)] \geq c\cdot d/\log^3d \cdot OPT(I) + q(d)$, for some universal constant $c$.
Neural Language Generation: Formulation, Methods, and Evaluation
Venue: arXiv cs.CL 2020
Year: 2020

Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. Neural architectures have been employed to generate text excerpts to various degrees of success, in a multitude of contexts and tasks that fulfil various user needs. Notably, high capacity deep learning models trained on large scale datasets demonstrate unparalleled abilities to learn patterns in the data even in the lack of explicit supervision signals, opening up a plethora of new possibilities regarding producing realistic and coherent texts. While the field of natural language generation is evolving rapidly, there are still many open challenges to address. In this survey we formally define and categorize the problem of natural language generation. We review particular application tasks that are instantiations of these general formulations, in which generating natural language is of practical importance. Next we include a comprehensive outline of methods and neural architectures employed for generating diverse texts. Nevertheless, there is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field. To this end, we also review current approaches to evaluating natural language generation systems. We hope this survey will provide an informative overview of formulations, methods, and assessments of neural natural language generation.
App-Aware Response Synthesis for User Reviews
Venue: arXiv cs.SE 2020
Year: 2020

Responding to user reviews promptly and satisfactorily improves application ratings, which is key to application popularity and success. The proliferation of such reviews makes it virtually impossible for developers to keep up with responding manually. To address this challenge, recent work has shown the possibility of automatic response generation. However, because the training review-response pairs are aggregated from many different apps, it remains challenging for such models to generate app-specific responses, which, on the other hand, are often desirable as apps have different features and concerns. Solving the challenge by simply building a model per app (i.e., training with review-response pairs of a single app) may be insufficient because individual apps have limited review-response pairs, and such pairs typically lack the relevant information needed to respond to a new review. To enable app-specific response generation, this work proposes AARSynth: an app-aware response synthesis system. The key idea behind AARSynth is to augment the seq2seq model with information specific to a given app. Given a new user review, it first retrieves the top-K most relevant app reviews and the most relevant snippet from the app description. The retrieved information and the new user review are then fed into a fused machine learning model that integrates the seq2seq model with a machine reading comprehension model. The latter helps digest the retrieved reviews and app description. Finally, the fused model generates a response that is customized to the given app. We evaluated AARSynth using a large corpus of reviews and responses from Google Play. The results show that AARSynth outperforms the state-of-the-art system by 22.2% on BLEU-4 score. Furthermore, our human study shows that AARSynth produces a statistically significant improvement in response quality compared to the state-of-the-art system.
Seating preference analysis for hybrid workplaces
Venue: arXiv cs.CY 2020
Year: 2020

Due to the increasing nature of flexible work and the recent requirements from COVID-19 restrictions, workplaces are becoming more hybrid (i.e. allowing workers to work between traditional office spaces and elsewhere including from home). Since workplaces are different in design, layout and available facilities, many workers find it difficult to adjust accordingly. Eventually, this impacts negatively towards work productivity and other related parameters including concentration, stress, and mood while at work. One of the key factors that causes this negative work experience is directly linked to the available seating arrangements. In this paper, we conduct an analysis to understand various seating preferences of 37 workers with varying demographics, using the data collected pre-COVID-19, and analyse the findings in the context of hybrid workplace settings. We also discuss a list of implications illustrating how our findings can be adapted across wider hybrid work settings.
OREBA: A Dataset for Objectively Recognizing Eating Behaviour and Associated Intake
Venue: arXiv cs.HC 2020
Year: 2020

Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of the labeled sensor data to automatically learn how to make detections. One characteristic, especially for deep learning models, is the need for large datasets. To meet this need, we collected the Objectively Recognizing Eating Behavior and Associated Intake (OREBA) dataset. The OREBA dataset aims to provide comprehensive multi-sensor data recorded during the course of communal meals for researchers interested in intake gesture detection. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands. We report the details of data collection and annotation, as well as details of sensor processing. The results of studies on IMU and video data involving deep learning models are reported to provide a baseline for future research. Specifically, the best baseline models achieve performances of $F_1$ = 0.853 for the discrete dish using video and $F_1$ = 0.852 for the shared dish using inertial data.
Feature Learning for Accelerometer based Gait Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in pattern matching, such as speech or object recognition support the viability of feature learning with deep learning solutions for gait recognition. Past papers have evaluated deep neural networks trained in a supervised manner for this task. In this work, we investigated both supervised and unsupervised approaches. Feature extractors using similar architectures incorporated into end-to-end models and autoencoders were compared based on their ability of learning good representations for a gait verification system. Both feature extractors were trained on the IDNet dataset then used for feature extraction on the ZJU-GaitAccel dataset. Results show that autoencoders are very close to discriminative end-to-end models with regards to their feature learning ability and that fully convolutional models are able to learn good feature representations, regardless of the training strategy.
Greedy Bandits with Sampled Context
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian strategies for contextual bandits have proved promising in single-state reinforcement learning tasks by modeling uncertainty using context information from the environment. In this paper, we propose Greedy Bandits with Sampled Context (GB-SC), a method for contextual multi-armed bandits to develop the prior from the context information using Thompson Sampling, and arm selection using an epsilon-greedy policy. The framework GB-SC allows for evaluation of context-reward dependency, as well as providing robustness for partially observable context vectors by leveraging the prior developed. Our experimental results show competitive performance on the Mushroom environment in terms of expected regret and expected cumulative regret, as well as insights on how each context subset affects decision-making.
Graph signal processing for machine learning: A review and new perspectives
Venue: arXiv cs.LG 2020
Year: 2020

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.
Can a Wi-Fi WLAN Support a First Person Shooter?
Venue: arXiv cs.NI 2020
Year: 2020

In corporate and commercial environments, the deployment of a set of coordinated Wi-Fi APs is becoming a common solution to provide Internet coverage to moving users. In these scenarios, real-time services as online games can also be present. This paper presents a set of experiments developed in a test scenario where an end device moves between different APs while generating game traffic. A WLAN solution based on virtual APs is used, in order to make the handoffs transparent for Layer 3. The results show that it is possible to maintain an acceptable level of subjective quality during the handoff. At the same time, it is set clear that the fact of having a gamer in an AP could be taken into account by radio resource management algorithms, in order to provide a better quality.
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on vehicle surface so that it's not recognizable by detectors in the photo-realistic Carla simulator. Our approach contains two main techniques, an Enlarge-and-Repeat process and a Discrete Searching method, to craft mosaic-like adversarial vehicle textures without access to neither the model weight of the detector nor a differential rendering procedure. The experimental results demonstrate the effectiveness of our approach in the simulator.
On Package Freshness in Linux Distributions
Venue: arXiv cs.SE 2020
Year: 2020

The open-source Linux operating system is available through a wide variety of distributions, each containing a collection of installable software packages. It can be important to keep these packages as fresh as possible to benefit from new features, bug fixes and security patches. However, not all distributions place the same emphasis on package freshness. We conducted a survey in the first half of 2020 with 170 Linux users to gauge their perception of package freshness in the distributions they use, the value they place on package freshness and the reasons why they do so, and the methods they use to update packages. The results of this survey reveal that, for the aforementioned reasons, keeping packages up to date is an important concern to Linux users and that they install and update packages through their distribution's official repositories whenever possible, but often resort to third-party repositories and package managers for proprietary software and programming language libraries. Some distributions are perceived to be much quicker in deploying package updates than others. These results are valuable to assess the requirements and expectations of Linux users in terms of package freshness.
Robust Benchmarking for Machine Learning of Clinical Entity Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Clinical studies often require understanding elements of a patient's narrative that exist only in free text clinical notes. To transform notes into structured data for downstream use, these elements are commonly extracted and normalized to medical vocabularies. In this work, we audit the performance of and indicate areas of improvement for state-of-the-art systems. We find that high task accuracies for clinical entity normalization systems on the 2019 n2c2 Shared Task are misleading, and underlying performance is still brittle. Normalization accuracy is high for common concepts (95.3%), but much lower for concepts unseen in training data (69.3%). We demonstrate that current approaches are hindered in part by inconsistencies in medical vocabularies, limitations of existing labeling schemas, and narrow evaluation techniques. We reformulate the annotation framework for clinical entity extraction to factor in these issues to allow for robust end-to-end system benchmarking. We evaluate concordance of annotations from our new framework between two annotators and achieve a Jaccard similarity of 0.73 for entity recognition and an agreement of 0.83 for entity normalization. We propose a path forward to address the demonstrated need for the creation of a reference standard to spur method development in entity recognition and normalization.