pwc-archive/papers-with-abstracts huggingface dataset:
Proximity full-text searches of frequently occurring words with a response time guarantee
Authors: Alexander B. Veretennikov
Date: 2020-09-06 00:00:00
Tasks: Information Retrieval, Retrieval

Full-text search engines are important tools for information retrieval. In a proximity full-text search, a document is relevant if it contains query terms near each other, especially if the query terms are frequently occurring words. For each word in the text, we use additional indexes to store information about nearby words at distances from the given word of less than or equal to MaxDistance, which is a parameter. A search algorithm for the case when the query consists of high-frequently used words is discussed. In addition, we present results of experiments with different values of MaxDistance to evaluate the search speed dependence on the value of MaxDistance. These results show that the average time of the query execution with our indexes is 94.7-45.9 times (depending on the value of MaxDistance) less than that with standard inverted files when queries that contain high-frequently occurring words are evaluated. This is a pre-print of a contribution published in Pinelas S., Kim A., Vlasov V. (eds) Mathematical Analysis With Applications. CONCORD-90 2018. Springer Proceedings in Mathematics & Statistics, vol 318, published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-42176-2_37
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Authors: Qing Ye, Yuxuan Han, Yanan sun, Jiancheng Lv
Date: 2020-09-06 00:00:00
Tasks: image-classification, Image Classification

Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs). To reduce the communication and synchronization overhead of synchronous methods, decreasing the synchronization frequency (e.g., every $n$ mini-batches) is a straightforward approach. However, it often suffers from poor convergence. In this paper, we propose a new algorithm of integrating Particle Swarm Optimization (PSO) into the distributed training process of DNNs to automatically compute new parameters. In the proposed algorithm, a computing work is encoded by a particle, the weights of DNNs and the training loss are modeled by the particle attributes. At each synchronization stage, the weights are updated by PSO from the sub weights gathered from all workers, instead of averaging the weights or the gradients. To verify the performance of the proposed algorithm, the experiments are performed on two commonly used image classification benchmarks: MNIST and CIFAR10, and compared with the peer competitors at multiple different synchronization configurations. The experimental results demonstrate the competitiveness of the proposed algorithm.
Automatic Yara Rule Generation Using Biclustering
Authors: Edward Raff, Richard Zak, Gary Lopez Munoz, William Fleming, Hyrum S. Anderson, Bobby Filar, Charles Nicholas, James Holt
Date: 2020-09-06 00:00:00

Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little work has been done on how to automate the generation of Yara rules for specific families. In this paper, we leverage large n-grams ($n \geq 8$) combined with a new biclustering algorithm to construct simple Yara rules more effectively than currently available software. Our method, AutoYara, is fast, allowing for deployment on low-resource equipment for teams that deploy to remote networks. Our results demonstrate that AutoYara can help reduce analyst workload by producing rules with useful true-positive rates while maintaining low false-positive rates, sometimes matching or even outperforming human analysts. In addition, real-world testing by malware analysts indicates AutoYara could reduce analyst time spent constructing Yara rules by 44-86%, allowing them to spend their time on the more advanced malware that current tools can't handle. Code will be made available at https://github.com/NeuromorphicComputationResearchProgram .
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification
Authors: Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Yi Guo, Jun Cheng, Xinwang Liu, Bin Hu
Date: 2020-09-05 00:00:00
Tasks: Contrastive Learning, Person Re-Identification, Representation Learning, Self-Supervised Learning

Person re-identification (Re-ID) via gait features within 3D skeleton sequences is a newly-emerging topic with several advantages. Existing solutions either rely on hand-crafted descriptors or supervised gait representation learning. This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID. Specifically, we first create self-supervision by learning to reconstruct unlabeled skeleton sequences reversely, which involves richer high-level semantics to obtain better gait representations. Other pretext tasks are also explored to further improve self-supervised learning. Second, inspired by the fact that motion's continuity endows adjacent skeletons in one skeleton sequence and temporally consecutive skeleton sequences with higher correlations (referred as locality in 3D skeleton data), we propose a locality-aware attention mechanism and a locality-aware contrastive learning scheme, which aim to preserve locality-awareness on intra-sequence level and inter-sequence level respectively during self-supervised learning. Last, with context vectors learned by our locality-aware attention mechanism and contrastive learning scheme, a novel feature named Constrastive Attention-based Gait Encodings (CAGEs) is designed to represent gait effectively. Empirical evaluations show that our approach significantly outperforms skeleton-based counterparts by 15-40% Rank-1 accuracy, and it even achieves superior performance to numerous multi-modal methods with extra RGB or depth information. Our codes are available at https://github.com/Kali-Hac/Locality-Awareness-SGE.
Sensors, Safety Models and A System-Level Approach to Safe and Scalable Automated Vehicles
Authors: Jack Weast
Date: 2020-09-04 00:00:00

When considering the accuracy of sensors in an automated vehicle (AV), it is not sufficient to evaluate the performance of any given sensor in isolation. Rather, the performance of any individual sensor must be considered in the context of the overall system design. Techniques like redundancy and different sensing modalities can reduce the chances of a sensing failure. Additionally, the use of safety models is essential to understanding whether any particular sensing failure is relevant. Only when the entire system design is taken into account can one properly understand the meaning of safety-relevant sensing failures in an AV. In this paper, we will consider what should actually constitute a sensing failure, how safety models play an important role in mitigating potential failures, how a system-level approach to safety will deliver a safe and scalable AV, and what an acceptable sensing failure rate should be considering the full picture of an AV's architecture.
Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing
Authors: Abdallah Moubayed, Tanveer Ahmed, Anwar Haque, Abdallah Shami
Date: 2020-09-04 00:00:00
Tasks: BIG-bench Machine Learning

The growth in wireless broadband users, devices, and novel applications has led to a significant increase in the demand for new radio frequency spectrum. This is expected to grow even further given the projection that the global traffic per year will reach 4.8 zettabytes by 2022. Moreover, it is projected that the number of Internet users will reach 4.8 billion and the number of connected devices will be close 28.5 billion devices. However, due to the spectrum being mostly allocated and divided, providing more spectrum to expand existing services or offer new ones has become more challenging. To address this, spectrum sharing has been proposed as a potential solution to improve spectrum utilization efficiency. Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it. To that end, this paper provides an overview of the different spectrum sharing levels and techniques that have been proposed in the literature. Moreover, it discusses the potential of adopting dynamic sharing mechanisms by offering Spectrum-as-a-Service architecture. Furthermore, it describes the potential role of machine learning models in facilitating the automated and efficient dynamic sharing of the spectrum and offering Spectrum-as-a-Service.
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
Authors: Lyes Khacef, Vincent Gripon, Benoit Miramond
Date: 2020-09-04 00:00:00
Tasks: Classification, Clustering, Few-Shot Image Classification, Few-Shot Learning, General Classification, GPU, Transfer Learning

Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples. We argue that this problem is very likely to occur on the edge, when the embedded device directly acquires the data, and the expert needed to perform labeling cannot be prompted often. To address this problem, we consider an algorithm consisting of the concatenation of transfer learning with clustering using Self-Organizing Maps (SOMs). We introduce a TensorFlow-based implementation to speed-up the process in multi-core CPUs and GPUs. Finally, we demonstrate the effectiveness of the method using standard off-the-shelf few-shot classification benchmarks.
DCTRGAN: Improving the Precision of Generative Models with Reweighting
Authors: Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih
Date: 2020-09-03 00:00:00

Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs). We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol. The correction takes the form of a reweighting function that can be applied to generated examples when making predictions from the simulation. We illustrate this approach using GANs trained on standard multimodal probability densities as well as calorimeter simulations from high energy physics. We show that the weighted GAN examples significantly improve the accuracy of the generated samples without a large loss in statistical power. This approach could be applied to any generative model and is a promising refinement method for high energy physics applications and beyond.
Latency and Throughput Optimization in Modern Networks: A Comprehensive Survey
Authors: Amir Mirzaeinnia, Mehdi Mirzaeinia, Abdelmounaam Rezgui
Date: 2020-09-01 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Modern applications are highly sensitive to communication delays and throughput. This paper surveys major attempts on reducing latency and increasing the throughput. These methods are surveyed on different networks and surroundings such as wired networks, wireless networks, application layer transport control, Remote Direct Memory Access, and machine learning based transport control.
Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders
Authors: Sara Hahner, Rodrigo Iza-Teran, Jochen Garcke
Date: 2020-08-31 00:00:00

For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape. We incorporate long short-term memory (LSTM) layers into an autoencoder to create low dimensional representations that allow the detection of patterns in the data and additionally detect the temporal dynamics in the deformation behavior. This is achieved with two decoders, one for reconstruction and one for prediction of future time steps of the sequence. In a preprocessing step the components of the studied object are converted to oriented bounding boxes which capture the impact of plastic deformation and allow reducing the dimensionality of the data describing the structure. The architecture is tested on the results of 196 car crash simulations of a model with 133 different components, where material properties are varied. In the latent representation we can detect patterns in the plastic deformation for the different components. The predicted bounding boxes give an estimate of the final simulation result and their quality is improved in comparison to different baselines.
The Parameter-Less Self-Organizing Map algorithm
Authors: Erik Berglund, Joaquin Sitte
Date: 2007-05-02 00:00:00

The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighbourhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.
Learning From Strategic Agents: Accuracy, Improvement, and Causality
Authors: Yonadav Shavit, Benjamin Edelman, Brian Axelrod
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Decision Making

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' incentives to ``game'' their features in order to receive better decisions. Whereas the strategic classification literature generally assumes that agents' outcomes are not causally dependent on their features (and thus strategic behavior is a form of lying), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. Our formulation is the first to incorporate a crucial phenomenon: when agents act to change observable features, they may as a side effect perturb unobserved features that causally affect their true outcomes. We consider three distinct desiderata for a decision-maker's model: accurately predicting agents' post-gaming outcomes (accuracy), incentivizing agents to improve these outcomes (improvement), and, in the linear setting, estimating the visible coefficients of the true causal model (causal precision). As our main contribution, we provide the first algorithms for learning accuracy-optimizing, improvement-optimizing, and causal-precision-optimizing linear regression models directly from data, without prior knowledge of agents' possible actions. These algorithms circumvent the hardness result of Miller et al. (2019) by allowing the decision maker to observe agents' responses to a sequence of decision rules, in effect inducing agents to perform causal interventions for free.
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding
Authors: Yibo Yang, Robert Bamler, Stephan Mandt
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Data Compression, Image Compression, Word Embeddings

Deep Bayesian latent variable models have enabled new approaches to both model and data compression. Here, we propose a new algorithm for compressing latent representations in deep probabilistic models, such as variational autoencoders, in post-processing. The approach thus separates model design and training from the compression task. Our algorithm generalizes arithmetic coding to the continuous domain, using adaptive discretization accuracy that exploits estimates of posterior uncertainty. A consequence of the "plug and play" nature of our approach is that various rate-distortion trade-offs can be achieved with a single trained model, eliminating the need to train and store multiple models for different bit rates. Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates using only a single machine learning model. Further experiments on Bayesian neural word embeddings demonstrate the versatility of the proposed method.
Uniform Convergence of Rank-weighted Learning
Authors: Liu Leqi, Justin Khim, Adarsh Prasad, Pradeep Ravikumar
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: BIG-bench Machine Learning, Decision Making

The decision-theoretic foundations of classical machine learning models have largely focused on estimating model parameters that minimize the expectation of a given loss function. However, as machine learning models are deployed in varied contexts, such as in high-stakes decision-making and societal settings, it is clear that these models are not just evaluated by their average performances. In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning. These L-Risks, induced by rank-dependent weighting functions with bounded variation, is a unification of popular risk measures such as conditional value-at-risk and those defined by cumulative prospect theory. We give uniform convergence bounds of this broad class of risk measures and study their consequences on a logistic regression example.
Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition
Authors: Rizard Renanda Adhi Pramono, Yie Tarng Chen, Wen Hsien Fang
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Activity Recognition, Group Activity Recognition

This paper presents a novel relational network for group activity recognition. The core of our network is to augment the conditional random fields (CRF), amenable to learning inter-dependency of correlated observations, with the newly devised temporal and spatial self-attention to learn the temporal evolution and spatial relational contexts of every actor in videos. Such a combination utilizes the global receptive fields of self-attention to construct a spatio-temporal graph topology to address the temporal dependency and non-local relationships of the actors. The network first uses the temporal self-attention along with the spatial self-attention, which considers multiple cliques with different scales of locality to account for the diversity of the actors' relationships in group activities, to model the pairwise energy of CRF. Afterward, to accommodate the distinct characteristics of each video, a new mean-field inference algorithm with dynamic halting is also addressed. Finally, a bidirectional universal transformer encoder (UTE), which combines both of the forward and backward temporal context information, is used to aggregate the relational contexts and scene information for group activity recognition. Simulations show that the proposed approach surpasses the state-of-the-art methods on the widespread Volleyball and Collective Activity datasets.
Privacy Preserving Structure-from-Motion
Authors: Marcel Geppert, Viktor Larsson, Pablo Speciale, Johannes L. Schönberger, Marc Pollefeys
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Camera Pose Estimation, Image-Based Localization, Mixed Reality, Pose Estimation, Privacy Preserving, Visual Localization

Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived image features are uploaded. Recent research addresses some of these concerns for the task of image-based localization by concealing the geometry of the query images and database maps. The core idea of the approach is to lift 2D/3D feature points to random lines, while still providing sufficient constraints for camera pose estimation. In this paper, we further build upon this idea and propose solutions to the different core algorithms of an incremental Structure-from-Motion pipeline based on random line features. With this work, we make another fundamental step towards enabling privacy preserving cloud-based mapping solutions. Various experiments on challenging real-world datasets demonstrate the practicality of our approach achieving comparable results to standard Structure-from-Motion systems.
ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes
Authors: Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, Leonidas Guibas
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object

In this work we study the problem of using referential language to identify common objects in real-world 3D scenes. We focus on a challenging setup where the referred object belongs to a extit{fine-grained} object class and the underlying scene contains extit{multiple} object instances of that class. Due to the scarcity and unsuitability of existent 3D-oriented linguistic resources for this task, we first develop two large-scale and complementary visio-linguistic datasets: i) extbf{ extit{Sr3D}}, which contains 83.5K template-based utterances leveraging extit{spatial relations} with other fine-grained object classes to localize a referred object in a given scene, and ii) extbf{ extit{Nr3D}} which contains 41.5K extit{natural, free-form}, utterances collected by deploying a 2-player object reference game in 3D scenes. Using utterances of either datasets, human listeners can recognize the referred object with high ($>$86\%, 92\% resp.) accuracy. By tapping on this data, we develop novel neural listeners that can comprehend object-centric natural language and identify the referred object extit{directly} in a 3D scene. Our key technical contribution is designing an approach for combining linguistic and geometric information (in the form of 3D point-clouds) and creating multi-modal (3D) neural listeners. We also show that architectures which promote object-to-object communication via graph neural networks outperform less context-aware alternatives, and that language-assisted 3D object identification outperforms language-agnostic object classifiers.
A Consistently Fast and Globally Optimal Solution to the Perspective-n-Point Problem
Authors: George Terzakis, Manolis Lourakis
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

An approach for estimating the pose of a camera given a set of 3D points and their corresponding 2D image projections is presented. It formulates the problem as a non-linear quadratic program and identifies regions in the parameter space that contain unique minima with guarantees that at least one of them will be the global minimum. Each regional minimum is computed with a sequential quadratic programming scheme. These premises result in an algorithm that always determines the global minima of the perspective-n-point problem for any number of input correspondences, regardless of possible coplanar arrangements of the imaged 3D points. For its implementation, the algorithm merely requires ordinary operations available in any standard off-the-shelf linear algebra library. Comparative evaluation demonstrates that the algorithm achieves state-of-the-art results at a consistently low computational cost.