OpenRaiser/Intern-Atlas huggingface dataset:
Physics Letters B publications from 1967 to 2020. An analysis of the WEB page content of PLB
Venue: arXiv cs.DL 2020
Year: 2020

Having been an editor for Physics Letters B (PLB) for many years I became interested in the history of publishing, especially in particle physics (HEP). Since PLB goes back to 1967 and an index of all PLB publications is available online, this information was used to look at the history of PLB publications over time. It should be noted that PLB publishes new results in particle physics or high energy physics, nuclear physics and also in astrophysics and cosmology. This is the start of an effort to look at all publishers that publish research from HEP.
Senior Living Communities: Made Safer by AI
Venue: arXiv cs.CY 2020
Year: 2020

There is a historically unprecedented shift in demographics towards seniors, which will result in significant housing development over the coming decade. This is an enormous opportunity for real-estate operators to innovate and address the demand in this growing market. However, investments in this area are fraught with risk. Seniors often have more health issues, and Covid-19 has exposed just how vulnerable they are -- especially those living in close proximity. Conventionally, most services for seniors are "high-touch", requiring close physical contact with trained caregivers. Not only are trained caregivers short in supply, but the pandemic has made it evident that conventional high-touch approaches to senior care are high-cost and greater risk. There are not enough caregivers to meet the needs of this emerging demographic, and even fewer who want to undertake the additional training and risk of working in a senior facility, especially given the current pandemic. In this article, we rethink the design of senior living facilities to mitigate the risks and costs using automation. With AI-enabled pervasive automation, we claim there is an opportunity, if not an urgency, to go from high-touch to almost "no touch" while dramatically reducing risk and cost. Although our vision goes beyond the current reality, we cite measurements from Caspar AI-enabled senior properties that show the potential benefit of this approach.
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization
Venue: arXiv cs.LG 2020
Year: 2020

Training a deep neural network with a small amount of data is a challenging problem as it is vulnerable to overfitting. However, one of the practical difficulties that we often face is to collect many samples. Transfer learning is a cost-effective solution to this problem. By using the source model trained with a large-scale dataset, the target model can alleviate the overfitting originated from the lack of training data. Resorting to the ability of generalization of the source model, several methods proposed to use the source knowledge during the whole training procedure. However, this is likely to restrict the potential of the target model and some transferred knowledge from the source can interfere with the training procedure. For improving the generalization performance of the target model with a few training samples, we proposed a regularization method called sample-based regularization (SBR), which does not rely on the source's knowledge during training. With SBR, we suggested a new training framework for transfer learning. Experimental results showed that our framework outperformed existing methods in various configurations.
Overcoming label noise in audio event detection using sequential labeling
Venue: arXiv cs.SD 2020
Year: 2020

This paper addresses the noisy label issue in audio event detection (AED) by refining strong labels as sequential labels with inaccurate timestamps removed. In AED, strong labels contain the occurrence of a specific event and its timestamps corresponding to the start and end of the event in an audio clip. The timestamps depend on subjectivity of each annotator, and their label noise is inevitable. Contrary to the strong labels, weak labels indicate only the occurrence of a specific event. They do not have the label noise caused by the timestamps, but the time information is excluded. To fully exploit information from available strong and weak labels, we propose an AED scheme to train with sequential labels in addition to the given strong and weak labels after converting the strong labels into the sequential labels. Using sequential labels consistently improved the performance particularly with the segment-based F-score by focusing on occurrences of events. In the mean-teacher-based approach for semi-supervised learning, including an early step with sequential prediction in addition to supervised learning with sequential labels mitigated label noise and inaccurate prediction of the teacher model and improved the segment-based F-score significantly while maintaining the event-based F-score.
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
Venue: arXiv cs.CV 2020
Year: 2020

Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnerability of CNNs to adversarial-example attacks can be easily exploited to break classification and segmentation tasks. This paper is the first to study how fragile a CNN-based model on multiple landmark detection to adversarial perturbations. Specifically, we propose a novel Adaptive Targeted Iterative FGSM (ATI-FGSM) attack against the state-of-the-art models in multiple landmark detection. The attacker can use ATI-FGSM to precisely control the model predictions of arbitrarily selected landmarks, while keeping other stationary landmarks still, by adding imperceptible perturbations to the original image. A comprehensive evaluation on a public dataset for cephalometric landmark detection demonstrates that the adversarial examples generated by ATI-FGSM break the CNN-based network more effectively and efficiently, compared with the original Iterative FGSM attack. Our work reveals serious threats to patients' health. Furthermore, we discuss the limitations of our method and provide potential defense directions, by investigating the coupling effect of nearby landmarks, i.e., a major source of divergence in our experiments. Our source code is available at https://github.com/qsyao/attack_landmark_detection.
Convolutional Polar Coded Modulation
Venue: arXiv cs.IT 2020
Year: 2020

$2^m$-ary modulation creates $m$ bit channels which are neither independent nor identical, and this causes problems when applying polar coding because polar codes are designed for independent identical channels. Different from the existing multi-level coding (MLC) and bit-interleaved coded modulation (BICM) schemes, this paper provides a convolutional polar coded modulation (CPCM) method that preserves the low-complexity nature of BICM while offering improved spectral efficiency. Numerical results are given to show the good performance of the proposed method.
Product age based demand forecast model for fashion retail
Venue: arXiv cs.LG 2020
Year: 2020

Fashion retailers require accurate demand forecasts for the next season, almost a year in advance, for demand management and supply chain planning purposes. Accurate forecasts are important to ensure retailers' profitability and to reduce environmental damage caused by disposal of unsold inventory. It is challenging because most products are new in a season and have short life cycles, huge sales variations and long lead-times. In this paper, we present a novel product age based forecast model, where product age refers to the number of weeks since its launch, and show that it outperforms existing models. We demonstrate the robust performance of the approach through real world use case of a multinational fashion retailer having over 300 stores, 35k items and around 40 categories. The main contributions of this work include unique and significant feature engineering for product attribute values, accurate demand forecast 6-12 months in advance and extending our approach to recommend product launch time for the next season. We use our fashion assortment optimization model to produce list and quantity of items to be listed in a store for the next season that maximizes total revenue and satisfies business constraints. We found a revenue uplift of 41% from our framework in comparison to the retailer's plan. We also compare our forecast results with the current methods and show that it outperforms existing models. Our framework leads to better ordering, inventory planning, assortment planning and overall increase in profit for the retailer's supply chain.
Data-Efficient Ranking Distillation for Image Retrieval
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in deep learning has lead to rapid developments in the field of image retrieval. However, the best performing architectures incur significant computational cost. Recent approaches tackle this issue using knowledge distillation to transfer knowledge from a deeper and heavier architecture to a much smaller network. In this paper we address knowledge distillation for metric learning problems. Unlike previous approaches, our proposed method jointly addresses the following constraints i) limited queries to teacher model, ii) black box teacher model with access to the final output representation, and iii) small fraction of original training data without any ground-truth labels. In addition, the distillation method does not require the student and teacher to have same dimensionality. Addressing these constraints reduces computation requirements, dependency on large-scale training datasets and addresses practical scenarios of limited or partial access to private data such as teacher models or the corresponding training data/labels. The key idea is to augment the original training set with additional samples by performing linear interpolation in the final output representation space. Distillation is then performed in the joint space of original and augmented teacher-student sample representations. Results demonstrate that our approach can match baseline models trained with full supervision. In low training sample settings, our approach outperforms the fully supervised approach on two challenging image retrieval datasets, ROxford5k and RParis6k \cite{Roxf} with the least possible teacher supervision.
Topic Modeling on User Stories using Word Mover's Distance
Venue: arXiv cs.CL 2020
Year: 2020

Requirements elicitation has recently been complemented with crowd-based techniques, which continuously involve large, heterogeneous groups of users who express their feedback through a variety of media. Crowd-based elicitation has great potential for engaging with (potential) users early on but also results in large sets of raw and unstructured feedback. Consolidating and analyzing this feedback is a key challenge for turning it into sensible user requirements. In this paper, we focus on topic modeling as a means to identify topics within a large set of crowd-generated user stories and compare three approaches: (1) a traditional approach based on Latent Dirichlet Allocation, (2) a combination of word embeddings and principal component analysis, and (3) a combination of word embeddings and Word Mover's Distance. We evaluate the approaches on a publicly available set of 2,966 user stories written and categorized by crowd workers. We found that a combination of word embeddings and Word Mover's Distance is most promising. Depending on the word embeddings we use in our approaches, we manage to cluster the user stories in two ways: one that is closer to the original categorization and another that allows new insights into the dataset, e.g. to find potentially new categories. Unfortunately, no measure exists to rate the quality of our results objectively. Still, our findings provide a basis for future work towards analyzing crowd-sourced user stories.
DECAPS: Detail-Oriented Capsule Networks
Venue: arXiv cs.CV 2020
Year: 2020

Capsule Networks (CapsNets) have demonstrated to be a promising alternative to Convolutional Neural Networks (CNNs). However, they often fall short of state-of-the-art accuracies on large-scale high-dimensional datasets. We propose a Detail-Oriented Capsule Network (DECAPS) that combines the strength of CapsNets with several novel techniques to boost its classification accuracies. First, DECAPS uses an Inverted Dynamic Routing (IDR) mechanism to group lower-level capsules into heads before sending them to higher-level capsules. This strategy enables capsules to selectively attend to small but informative details within the data which may be lost during pooling operations in CNNs. Second, DECAPS employs a Peekaboo training procedure, which encourages the network to focus on fine-grained information through a second-level attention scheme. Finally, the distillation process improves the robustness of DECAPS by averaging over the original and attended image region predictions. We provide extensive experiments on the CheXpert and RSNA Pneumonia datasets to validate the effectiveness of DECAPS. Our networks achieve state-of-the-art accuracies not only in classification (increasing the average area under ROC curves from 87.24% to 92.82% on the CheXpert dataset) but also in the weakly-supervised localization of diseased areas (increasing average precision from 41.7% to 80% for the RSNA Pneumonia detection dataset).
SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics
Venue: arXiv cs.CL 2020
Year: 2020

We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics. SacreROUGE removes many obstacles that researchers face when using or developing metrics: (1) The library provides Python wrappers around the official implementations of existing evaluation metrics so they share a common, easy-to-use interface; (2) it provides functionality to evaluate how well any metric implemented in the library correlates to human-annotated judgments, so no additional code needs to be written for a new evaluation metric; and (3) it includes scripts for loading datasets that contain human judgments so they can easily be used for evaluation. This work describes the design of the library, including the core Metric interface, the command-line API for evaluating summarization models and metrics, and the scripts to load and reformat publicly available datasets. The development of SacreROUGE is ongoing and open to contributions from the community.
Characteristics of Monte Carlo Dropout in Wide Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs). It has been interpreted as approximately performing Bayesian inference. Based on previous work on the approximation of Gaussian processes by wide and deep neural networks with random weights, we study the limiting distribution of wide untrained NNs under dropout more rigorously and prove that they as well converge to Gaussian processes for fixed sets of weights and biases. We sketch an argument that this property might also hold for infinitely wide feed-forward networks that are trained with (full-batch) gradient descent. The theory is contrasted by an empirical analysis in which we find correlations and non-Gaussian behaviour for the pre-activations of finite width NNs. We therefore investigate how (strongly) correlated pre-activations can induce non-Gaussian behavior in NNs with strongly correlated weights.
Geometric Style Transfer
Venue: arXiv cs.CV 2020
Year: 2020

Neural style transfer (NST), where an input image is rendered in the style of another image, has been a topic of considerable progress in recent years. Research over that time has been dominated by transferring aspects of color and texture, yet these factors are only one component of style. Other factors of style include composition, the projection system used, and the way in which artists warp and bend objects. Our contribution is to introduce a neural architecture that supports transfer of geometric style. Unlike recent work in this area, we are unique in being general in that we are not restricted by semantic content. This new architecture runs prior to a network that transfers texture style, enabling us to transfer texture to a warped image. This form of network supports a second novelty: we extend the NST input paradigm. Users can input content/style pair as is common, or they can chose to input a content/texture-style/geometry-style triple. This three image input paradigm divides style into two parts and so provides significantly greater versatility to the output we can produce. We provide user studies that show the quality of our output, and quantify the importance of geometric style transfer to style recognition by humans.
Hardware Implementation of Keyless Encryption Scheme for Internet of Things Based on Image of Memristors
Venue: arXiv cs.CR 2020
Year: 2020

The Internet of Things (IoT) is rapidly increasing the number of connected devices. This causes new concerns towards solutions for authenticating numerous IoT devices. Most of these devices are resource-constrained. Therefore, the use of long-secret keys, in traditional cryptography schemes can be hard to implement. Also, the key generation, distribution, and storage are very complex. Moreover, the goal of many reported cyber-attacks is accessing the key. Therefore, researchers have shown an increased interest in designing keyless encryption schemes recently. In this report, we are going to explain the details of the implementation of the keyless protocol by taking advantage of known technology modules such as microcontrollers (MCU), and hash functions. Physical Unclonable Functions (PUFs) have been used in many cryptographic applications such as Password Management Systems, key exchange, Key Generation. In this report, we are going to explain the details of the hardware implementation of keyless encryption in the MCU. Different kinds of memristors have been used in the past. In this work, a look-up-table containing memristor cells value at the various current levels is used since the physical component is unavailable yet. The hardware that is used to implement the system is an evaluation-board of SAMV71 MCU, which is used to implement the control system and hardware hashing.
NaviGAN: A Generative Approach for Socially Compliant Navigation
Venue: arXiv cs.RO 2020
Year: 2020

Robots navigating in human crowds need to optimize their paths not only for their task performance but also for their compliance to social norms. One of the key challenges in this context is the lack of standard metrics for evaluating and optimizing a socially compliant behavior. Existing works in social navigation can be grouped according to the differences in their optimization objectives. For instance, the reinforcement learning approaches tend to optimize on the \textit{comfort} aspect of the socially compliant navigation, whereas the inverse reinforcement learning approaches are designed to achieve \textit{natural} behavior. In this paper, we propose NaviGAN, a generative navigation algorithm that jointly optimizes both of the \textit{comfort} and \textit{naturalness} aspects. Our approach is designed as an adversarial training framework that can learn to generate a navigation path that is both optimized for achieving a goal and for complying with latent social rules. A set of experiments has been carried out on multiple datasets to demonstrate the strengths of the proposed approach quantitatively. We also perform extensive experiments using a physical robot in a real-world environment to qualitatively evaluate the trained social navigation behavior. The video recordings of the robot experiments can be found in the link: https://youtu.be/61blDymjCpw.
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers
Venue: arXiv cs.AI 2020
Year: 2020

It is known due to the work of Van den Broeck et al [KR, 2014] that weighted first-order model counting (WFOMC) in the two-variable fragment of first-order logic can be solved in time polynomial in the number of domain elements. In this paper we extend this result to the two-variable fragment with counting quantifiers.
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty
Venue: arXiv cs.GT 2020
Year: 2020

We study the problem of finding equilibrium strategies in multi-agent games with incomplete payoff information, where the payoff matrices are only known to the players up to some bounded uncertainty sets. In such games, an ex-post equilibrium characterizes equilibrium strategies that are robust to the payoff uncertainty. When the game is one-shot, we show that in zero-sum polymatrix games, an ex-post equilibrium can be computed efficiently using linear programming. We further extend the notion of ex-post equilibrium to stochastic games, where the game is played repeatedly in a sequence of stages and the transition dynamics are governed by an Markov decision process (MDP). We provide sufficient condition for the existence of an ex-post Markov perfect equilibrium (MPE). We show that under bounded payoff uncertainty, the value of any two-player zero-sum stochastic game can be computed up to a tight value interval using dynamic programming.
Feature Interactions in XGBoost
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results show that accurate identification of these constraints can help improve the performance of baseline XGBoost model significantly. Further, the improvement in the model structure can also lead to better interpretability.
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Venue: arXiv cs.CR 2020
Year: 2020

Machine learning models, especially neural network (NN) classifiers, have acceptable performance and accuracy that leads to their wide adoption in different aspects of our daily lives. The underlying assumption is that these models are generated and used in attack free scenarios. However, it has been shown that neural network based classifiers are vulnerable to adversarial examples. Adversarial examples are inputs with special perturbations that are ignored by human eyes while can mislead NN classifiers. Most of the existing methods for generating such perturbations require a certain level of knowledge about the target classifier, which makes them not very practical. For example, some generators require knowledge of pre-softmax logits while others utilize prediction scores. In this paper, we design a practical black-box adversarial example generator, dubbed ManiGen. ManiGen does not require any knowledge of the inner state of the target classifier. It generates adversarial examples by searching along the manifold, which is a concise representation of input data. Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.