OpenRaiser/Intern-Atlas huggingface dataset:
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
Venue: arXiv cs.IR 2020
Year: 2020

Besides position bias, which has been well-studied, trust bias is another type of bias prevalent in user interactions with rankings: users are more likely to click incorrectly w.r.t. their preferences on highly ranked items because they trust the ranking system. While previous work has observed this behavior in users, we prove that existing Counterfactual Learning to Rank (CLTR) methods do not remove this bias, including methods specifically designed to mitigate this type of bias. Moreover, we prove that Inverse Propensity Scoring (IPS) is principally unable to correct for trust bias under non-trivial circumstances. Our main contribution is a new estimator based on affine corrections: it both reweights clicks and penalizes items displayed on ranks with high trust bias. Our estimator is the first estimator that is proven to remove the effect of both trust bias and position bias. Furthermore, we show that our estimator is a generalization of the existing CLTR framework: if no trust bias is present, it reduces to the original IPS estimator. Our semi-synthetic experiments indicate that by removing the effect of trust bias in addition to position bias, CLTR can approximate the optimal ranking system even closer than previously possible.
Explainable Disease Classification via weakly-supervised segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning based approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. These systems achieve high to very high accuracy in specific disease detection for which they are trained but lack in terms of an explanation for the provided decision/classification result. The activation maps which correspond to decisions do not correlate well with regions of interest for specific diseases. This paper examines this problem and proposes an approach which mimics the clinical practice of looking for an evidence prior to diagnosis. A CAD model is learnt using a mixed set of information: class labels for the entire training set of images plus a rough localisation of suspect regions as an extra input for a smaller subset of training images for guiding the learning. The proposed approach is illustrated with detection of diabetic macular edema (DME) from OCT slices. Results of testing on on a large public dataset show that with just a third of images with roughly segmented fluid filled regions, the classification accuracy is on par with state of the art methods while providing a good explanation in the form of anatomically accurate heatmap /region of interest. The proposed solution is then adapted to Breast Cancer detection from mammographic images. Good evaluation results on public datasets underscores the generalisability of the proposed solution.
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks
Venue: arXiv cs.CV 2020
Year: 2020

Point cloud architecture design has become a crucial problem for 3D deep learning. Several efforts exist to manually design architectures with high accuracy in point cloud tasks such as classification, segmentation, and detection. Recent progress in automatic Neural Architecture Search (NAS) minimizes the human effort in network design and optimizes high performing architectures. However, these efforts fail to consider important factors such as latency during inference. Latency is of high importance in time critical applications like self-driving cars, robot navigation, and mobile applications, that are generally bound by the available hardware. In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency. We implement a novel latency constraint formulation to trade-off between accuracy and latency in our architecture search. Contrary to previous works, our latency loss guarantees that the final network achieves latency under a specified target value. This is crucial when the end task is to be deployed in a limited hardware setting. Extensive experiments show that LC-NAS is able to find state-of-the-art architectures for point cloud classification in ModelNet40 with minimal computational cost. We also show how our searched architectures achieve any desired latency with a reasonably low drop in accuracy. Finally, we show how our searched architectures easily transfer to a different task, part segmentation on PartNet, where we achieve state-of-the-art results while lowering latency by a factor of 10.
Cascade Convolutional Neural Network for Image Super-Resolution
Venue: arXiv cs.CV 2020
Year: 2020

With the development of the super-resolution convolutional neural network (SRCNN), deep learning technique has been widely applied in the field of image super-resolution. Previous works mainly focus on optimizing the structure of SRCNN, which have been achieved well performance in speed and restoration quality for image super-resolution. However, most of these approaches only consider a specific scale image during the training process, while ignoring the relationship between different scales of images. Motivated by this concern, in this paper, we propose a cascaded convolution neural network for image super-resolution (CSRCNN), which includes three cascaded Fast SRCNNs and each Fast SRCNN can process a specific scale image. Images of different scales can be trained simultaneously and the learned network can make full use of the information resided in different scales of images. Extensive experiments show that our network can achieve well performance for image SR.
Computing the Real Isolated Points of an Algebraic Hypersurface
Venue: arXiv cs.CG 2020
Year: 2020

Let $\mathbb{R}$ be the field of real numbers. We consider the problem of computing the real isolated points of a real algebraic set in $\mathbb{R}^n$ given as the vanishing set of a polynomial system. This problem plays an important role for studying rigidity properties of mechanism in material designs. In this paper, we design an algorithm which solves this problem. It is based on the computations of critical points as well as roadmaps for answering connectivity queries in real algebraic sets. This leads to a probabilistic algorithm of complexity $(nd)^{O(n\log(n))}$ for computing the real isolated points of real algebraic hypersurfaces of degree $d$. It allows us to solve in practice instances which are out of reach of the state-of-the-art.
QoE-aware cross-layer architecture for video traffic over Internet
Venue: arXiv cs.NI 2020
Year: 2020

The emergence of video applications and video capable devices have contributed substantially to the increase of video traffic on Internet. New mechanisms recommending video rate adaptation towards delivering enhanced Quality of Experience (QoE) at the same time making room for more sessions. This paper introduces a cross-layer QoE-aware architecture for video traffic over the Internet. It proposes that video sources at the application layer adapt their rate to the network environment by controlling their transmitted bit rate dynamically; and the edge of network at the network layer protects the quality of the active video sessions by controlling the acceptance of new session through a video-aware admission control. In particular, it will seek the most efficient way of accepting new video session and adapting transmission rates to free up resources for more session while maintaining the QoE of active sessions. The proposed framework will contribute to the preparation for the extreme growth of video traffic in the foreseeable future. Simulation results show that the proposed cross-layer architecture guarantees the QoE for the admitted sessions and utilizes the link more efficiently comparing to the rate adaptation only architecture.
Compact Belief State Representation for Task Planning
Venue: arXiv cs.AI 2020
Year: 2020

Task planning in a probabilistic belief state domains allows generating complex and robust execution policies in those domains affected by state uncertainty. The performance of a task planner relies on the belief state representation. However, current belief state representation becomes easily intractable as the number of variables and execution time grows. To address this problem, we developed a novel belief state representation based on cartesian product and union operations over belief substates. These two operations and single variable assignment nodes form And-Or directed acyclic graph of Belief State (AOBS). We show how to apply actions with probabilistic outcomes and measure the probability of conditions holding over belief state. We evaluated AOBS performance in simulated forward state space exploration. We compared the size of AOBS with the size of Binary Decision Diagrams (BDD) that were previously used to represent belief state. We show that AOBS representation is not only much more compact than a full belief state but it also scales better than BDD for most of the cases.
Lossy Image Compression with Normalizing Flows
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been established and refined over many decades. However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization. Due to that, they inherently limit the range of quality levels that can be covered. In contrast, traditional approaches in image compression allow for a larger range of quality levels. Interestingly, they employ an invertible transformation before performing the quantization step which explicitly discards information. Inspired by this, we propose a deep image compression method that is able to go from low bit-rates to near lossless quality by leveraging normalizing flows to learn a bijective mapping from the image space to a latent representation. In addition to this, we demonstrate further advantages unique to our solution, such as the ability to maintain constant quality results through re-encoding, even when performed multiple times. To the best of our knowledge, this is the first work to explore the opportunities for leveraging normalizing flows for lossy image compression.
Products-10K: A Large-scale Product Recognition Dataset
Venue: arXiv cs.CV 2020
Year: 2020

With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution. To fully meet customers' massive and diverse online shopping needs with quick response, the retailing AI system needs to automatically recognize products from images and videos at the stock-keeping unit (SKU) level with high accuracy. However, product recognition is still a challenging task, since many of SKU-level products are fine-grained and visually similar by a rough glimpse. Although there are already some products benchmarks available, these datasets are either too small (limited number of products) or noisy-labeled (lack of human labeling). In this paper, we construct a human-labeled product image dataset named "Products-10K", which contains 10,000 fine-grained SKU-level products frequently bought by online customers in JD.com. Based on our new database, we also introduced several useful tips and tricks for fine-grained product recognition. The products-10K dataset is available via https://products-10k.github.io/.
Multidimensionality of Legal Singularity: Parametric Analysis and the Autonomous Levels of AI Legal Reasoning
Venue: arXiv cs.CY 2020
Year: 2020

Legal scholars have in the last several years embarked upon an ongoing discussion and debate over a potential Legal Singularity that might someday occur, involving a variant or law-domain offshoot leveraged from the Artificial Intelligence (AI) realm amid its many decades of deliberations about an overarching and generalized technological singularity (referred to classically as The Singularity). This paper examines the postulated Legal Singularity and proffers that such AI and Law cogitations can be enriched by these three facets addressed herein: (1) dovetail additionally salient considerations of The Singularity into the Legal Singularity, (2) make use of an in-depth and innovative multidimensional parametric analysis of the Legal Singularity as posited in this paper, and (3) align and unify the Legal Singularity with the Levels of Autonomy (LoA) associated with AI Legal Reasoning (AILR) as propounded in this paper.
Fast and Simple Modular Subset Sum
Venue: arXiv cs.DS 2020
Year: 2020

We revisit the Subset Sum problem over the finite cyclic group $\mathbb{Z}_m$ for some given integer $m$. A series of recent works has provided near-optimal algorithms for this problem under the Strong Exponential Time Hypothesis. Koiliaris and Xu (SODA'17, TALG'19) gave a deterministic algorithm running in time $\tilde{O}(m^{5/4})$, which was later improved to $O(m \log^7 m)$ randomized time by Axiotis et al. (SODA'19). In this work, we present two simple algorithms for the Modular Subset Sum problem running in near-linear time in $m$, both efficiently implementing Bellman's iteration over $\mathbb{Z}_m$. The first one is a randomized algorithm running in time $O(m \log^2 m)$, that is based solely on rolling hash and an elementary data-structure for prefix sums; to illustrate its simplicity we provide a short and efficient implementation of the algorithm in Python. Our second solution is a deterministic algorithm running in time $O(m\ \mathrm{polylog}\ m)$, that uses dynamic data structures for string manipulation. We further show that the techniques developed in this work can also lead to simple algorithms for the All Pairs Non-Decreasing Paths Problem (APNP) on undirected graphs, matching the near-optimal running time of $\tilde{O}(n^2)$ provided in the recent work of Duan et al. (ICALP'19).
Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. To this end, we first propose an efficient approach to convert the RNA sequences into images characterizing their base-pairing probability. As a result, classifying RNA sequences is converted to an image classification problem that can be efficiently solved by available CNN-based classification models. The paper also considers the folding potential of the ncRNAs in addition to their primary sequence. Based on the proposed approach, a benchmark image classification dataset is generated from the RFAM database of ncRNA sequences. In addition, three classical CNN models have been implemented and compared to demonstrate the superior performance and efficiency of the proposed approach. Extensive experimental results show the great potential of using deep learning approaches for RNA classification.
Double Patterns: A Usable Solution to Increase the Security of Android Unlock Patterns
Venue: arXiv cs.CR 2020
Year: 2020

Android unlock patterns remain quite common. Our study, as well as others, finds that roughly 25\% of respondents use a pattern when unlocking their phone. Despite known security issues, the design of the pattern interface remains unchanged since first launch. We propose Double Patterns, a natural and easily adoptable advancement on Android unlock patterns that maintains the core design features, but instead of selecting a single pattern, a user selects two, concurrent Android unlock patterns entered one-after-the-other super-imposed on the same 3x3 grid. We evaluated Double Patterns for both security and usability by conducting an online study with $n=634$ participants in three treatments: a control treatment, a first pattern entry blocklist, and a blocklist for both patterns. We find that in all settings, user chosen Double Patterns are more secure than traditional patterns based on standard guessability metrics, more similar to that of 4-/6-digit PINs, and even more difficult to guess for a simulated attacker. Users express positive sentiments in qualitative feedback, particularly those who currently (or previously) used Android unlock patterns, and overall, participants found the Double Pattern interface quite usable, with high recall retention and comparable entry times to traditional patterns. In particular, current Android pattern users, the target population for Double Patterns, reported SUS scores in the 80th percentile and high perceptions of security and usability in responses to open- and closed-questions. Based on these findings, we would recommend adding Double Patterns as an advancement to Android patterns, much like allowing for added PIN length.
Feature Guided Search for Creative Problem Solving Through Tool Construction
Venue: arXiv cs.RO 2020
Year: 2020

Robots in the real world should be able to adapt to unforeseen circumstances. Particularly in the context of tool use, robots may not have access to the tools they need for completing a task. In this paper, we focus on the problem of tool construction in the context of task planning. We seek to enable robots to construct replacements for missing tools using available objects, in order to complete the given task. We introduce the Feature Guided Search (FGS) algorithm that enables the application of existing heuristic search approaches in the context of task planning, to perform tool construction efficiently. FGS accounts for physical attributes of objects (e.g., shape, material) during the search for a valid task plan. Our results demonstrate that FGS significantly reduces the search effort over standard heuristic search approaches by approximately 93% for tool construction.
Sensitive Information Detection: Recursive Neural Networks for Encoding Context
Venue: arXiv cs.LG 2020
Year: 2020

The amount of data for processing and categorization grows at an ever increasing rate. At the same time the demand for collaboration and transparency in organizations, government and businesses, drives the release of data from internal repositories to the public or 3rd party domain. This in turn increase the potential of sharing sensitive information. The leak of sensitive information can potentially be very costly, both financially for organizations, but also for individuals. In this work we address the important problem of sensitive information detection. Specially we focus on detection in unstructured text documents. We show that simplistic, brittle rule sets for detecting sensitive information only find a small fraction of the actual sensitive information. Furthermore we show that previous state-of-the-art approaches have been implicitly tailored to such simplistic scenarios and thus fail to detect actual sensitive content. We develop a novel family of sensitive information detection approaches which only assumes access to labeled examples, rather than unrealistic assumptions such as access to a set of generating rules or descriptive topical seed words. Our approaches are inspired by the current state-of-the-art for paraphrase detection and we adapt deep learning approaches over recursive neural networks to the problem of sensitive information detection. We show that our context-based approaches significantly outperforms the family of previous state-of-the-art approaches for sensitive information detection, so-called keyword-based approaches, on real-world data and with human labeled examples of sensitive and non-sensitive documents.
Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles
Venue: arXiv cs.CV 2020
Year: 2020

In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must anticipate these situations at an early stage too, to increase the safety and the efficiency of its performance. To deal with lane-change recognition and prediction of surrounding vehicles, we pose the problem as an action recognition/prediction problem by stacking visual cues from video cameras. Two video action recognition approaches are analyzed: two-stream convolutional networks and spatiotemporal multiplier networks. Different sizes of the regions around the vehicles are analyzed, evaluating the importance of the interaction between vehicles and the context information in the performance. In addition, different prediction horizons are evaluated. The obtained results demonstrate the potential of these methodologies to serve as robust predictors of future lane-changes of surrounding vehicles in time horizons between 1 and 2 seconds.
Is this sentence valid? An Arabic Dataset for Commonsense Validation
Venue: arXiv cs.CL 2020
Year: 2020

The commonsense understanding and validation remains a challenging task in the field of natural language understanding. Therefore, several research papers have been published that studied the capability of proposed systems to evaluate the models ability to validate commonsense in text. In this paper, we present a benchmark Arabic dataset for commonsense understanding and validation as well as a baseline research and models trained using the same dataset. To the best of our knowledge, this dataset is considered as the first in the field of Arabic text commonsense validation. The dataset is distributed under the Creative Commons BY-SA 4.0 license and can be found on GitHub.
Towards Better Bus Networks: A Visual Analytics Approach
Venue: arXiv cs.HC 2020
Year: 2020

Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts.