OpenRaiser/Intern-Atlas huggingface dataset:
Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes first pre-training with the relevant and large in size, Aff-Wild and Aff-Wild2 emotion databases. Low-, mid- and high-level features are extracted from the trained CNN component and are exploited by RNN subnets in a multi-task framework. Their outputs constitute an intermediate level prediction; final estimates are obtained as the mean or median values of these predictions. Fusion of the networks is also examined for boosting the obtained performance, at Decision-, or at Model-level; in the latter case a RNN was used for the fusion. Our approach, although using only the visual modality, outperformed state-of-the-art methods that utilized audio and visual modalities. Some of our developments have been submitted to the OMG-Emotion Challenge, ranking second among the technologies which used only visual information for valence estimation; ranking third overall. Through extensive experimentation, we further show that arousal estimation is greatly improved when low-level features are combined with high-level ones.
MLPerf Training Benchmark
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors.
A Critical View on CIS Controls
Venue: arXiv cs.CR 2020
Year: 2020

CIS Controls is a set of 20 controls and 171 sub-controls that were created with an idea of having a list of something to implement so that organizations can increase their security. While good in theory, it is a big question of how viable this approach is in practice, and does it really help. There is only a minor number of critical views of CIS Controls and since CIS Controls are marketed by two very influential organizations they are very popular. Yet, there are alternatives published by ISO, NIST and even PCI consortium. In this paper we critically assess CIS Controls, assumptions on which they are based as well as validity of approach and claims made in its favor. The conclusion is that scientific community should be more active regarding this topic, but also that more material is necessary. This is something that CIS and SANS should support if they want to make CIS Controls viable alternative to other approaches.
Constant State of Change: Engagement Inequality in Temporal Dynamic Networks
Venue: arXiv cs.SI 2020
Year: 2020

The temporal changes in complex systems of interactions have excited the research community in recent years as they encompass understandings on their dynamics and evolution. From the collective dynamics of organizations and online communities to the spreading of information and fake news, to name a few, temporal dynamics are fundamental in the understanding of complex systems. In this work, we quantify the level of engagement in dynamic complex systems of interactions, modeled as networks. We focus on interaction networks for which the dynamics of the interactions are coupled with that of the topology, such as online messaging, forums, and emails. We define two indices to capture the temporal level of engagement: the Temporal Network (edge) Intensity index, and the Temporal Dominance Inequality index. Our surprising results are that these measures are stationary for most measured networks, regardless of vast fluctuations in the size of the networks in time. Moreover, more than 80% of weekly changes in the indices values are bounded by less than 10%. The indices are stable between the temporal evolution of a network but are different between networks, and a classifier can determine the network the temporal indices belong to with high success. We find an exception in the Enron management email exchange during the year before its disintegration, in which both indices show high volatility throughout the inspected period.
Scalable Global Optimization via Local Bayesian Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive with other paradigms. In this paper we take the view that this is due to the implicit homogeneity of the global probabilistic models and an overemphasized exploration that results from global acquisition. This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems. We propose the $\texttt{TuRBO}$ algorithm that fits a collection of local models and performs a principled global allocation of samples across these models via an implicit bandit approach. A comprehensive evaluation demonstrates that $\texttt{TuRBO}$ outperforms state-of-the-art methods from machine learning and operations research on problems spanning reinforcement learning, robotics, and the natural sciences.
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops
Venue: arXiv cs.CV 2020
Year: 2020

Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. We propose a point supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets - Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time.
Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification
Venue: arXiv cs.CV 2020
Year: 2020

Extreme learning machine (ELM), which can be viewed as a variant of Random Vector Functional Link (RVFL) network without the input-output direct connections, has been extensively used to create multi-layer (deep) neural networks. Such networks employ randomization based autoencoders (AE) for unsupervised feature extraction followed by an ELM classifier for final decision making. Each randomization based AE acts as an independent feature extractor and a deep network is obtained by stacking several such AEs. Inspired by the better performance of RVFL over ELM, in this paper, we propose several deep RVFL variants by utilizing the framework of stacked autoencoders. Specifically, we introduce direct connections (feature reuse) from preceding layers to the fore layers of the network as in the original RVFL network. Such connections help to regularize the randomization and also reduce the model complexity. Furthermore, we also introduce denoising criterion, recovering clean inputs from their corrupted versions, in the autoencoders to achieve better higher level representations than the ordinary autoencoders. Extensive experiments on several classification datasets show that our proposed deep networks achieve overall better and faster generalization than the other relevant state-of-the-art deep neural networks.
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action
Venue: arXiv cs.LG 2020
Year: 2020

The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency controllers. For example, the range of motion and the angles of the motors of a robot can be limited to physical boundaries. Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes. In this paper, we propose a simple modification of a state-of-the-art deep RL algorithm (DQN), enabling learning from forbidden actions. To do so, the standard Q-learning update is enhanced with an extra safety loss inspired by structured classification. We empirically show that it reduces the number of hit constraints during the learning phase and accelerates convergence to near-optimal policies compared to using standard DQN. Experiments are done on a Visual Grid World Environment and Text-World domain.
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
Venue: arXiv cs.LG 2020
Year: 2020

Health information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more degrees confederated machine learning. We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.
Deep Radiance Caching: Convolutional Autoencoders Deeper in Ray Tracing
Venue: arXiv cs.GR 2020
Year: 2020

Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent research uses Deep Neural Networks to predict indirect lighting on image level, but such methods are commonly limited to diffuse materials and require training on each scene.We present Deep Radiance Caching (DRC), an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination. DRC employs a denoising neural network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene.This offers high performance CPU rendering for maximum accessibility. Our method has been evaluated on interior scenes, and is able to produce high-quality images within 180 seconds on a single CPU.
Representing Dependencies in Event Structures
Venue: arXiv cs.LO 2020
Year: 2020

Event structures where the causality may explicitly change during a computation have recently gained the stage. In this kind of event structures the changes in the set of the causes of an event are triggered by modifiers that may add or remove dependencies, thus making the happening of an event contextual. Still the focus is always on the dependencies of the event. In this paper we promote the idea that the context determined by the modifiers plays a major role, and the context itself determines not only the causes but also what causality should be. Modifiers are then used to understand when an event (or a set of events) can be added to a configuration, together with a set of events modeling dependencies, which will play a less important role. We show that most of the notions of Event Structure presented in literature can be translated into this new kind of event structure, preserving the main notion, namely the one of configuration.
Soft-Label Dataset Distillation and Text Dataset Distillation
Venue: arXiv cs.LG 2020
Year: 2020

Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several benefits like speeding up model training, reducing energy consumption, and reducing required storage space. Currently, each synthetic sample is assigned a single `hard' label, and also, dataset distillation can currently only be used with image data. We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a `soft' label (a distribution of labels). Our algorithm increases accuracy by 2-4% over the original algorithm for several image classification tasks. Using `soft' labels also enables distilled datasets to consist of fewer samples than there are classes as each sample can encode information for multiple classes. For example, training a LeNet model with 10 distilled images (one per class) results in over 96% accuracy on MNIST, and almost 92% accuracy when trained on just 5 distilled images. We also extend the dataset distillation algorithm to distill sequential datasets including texts. We demonstrate that text distillation outperforms other methods across multiple datasets. For example, models attain almost their original accuracy on the IMDB sentiment analysis task using just 20 distilled sentences. Our code can be found at $\href{https://github.com/ilia10000/dataset-distillation}{\text{https://github.com/ilia10000/dataset-distillation}}$.
RAMBO: Repeated And Merged BloOm Filter for Ultra-fast Multiple Set Membership Testing (MSMT) on Large-Scale Data
Venue: arXiv cs.DS 2020
Year: 2020

Multiple Set Membership Testing (MSMT) is a well-known problem in a variety of search and query applications. Given a dataset of K different sets and a query q, it aims to find all of the sets containing the query. Trivially, an MSMT instance can be reduced to K membership testing instances, each with the same q, leading to O(K) query time with a simple array of Bloom Filters. We propose a data-structure called RAMBO (Repeated And Merged BloOm Filter) that achieves O(\sqrt{K} log K) query time in expectation with an additional worst-case memory cost factor of O(log K) beyond the array of Bloom Filters. Due to this, RAMBO is a very fast and accurate data-structure. Apart from being embarrassingly parallel, supporting cheap updates for streaming inputs, zero false-negative rate, and low false-positive rate, RAMBO beats the state-of-the-art approaches for genome indexing methods: COBS (Compact bit-sliced signature index), Sequence Bloom Trees (a Bloofi based implementation), HowDeSBT, SSBT, and document indexing methods like BitFunnel. The proposed data-structure is simply a count-min sketch type arrangement of a membership testing utility (Bloom Filter in our case). It indexes k-grams and provides an approximate membership testing based search utility. The simplicity of the algorithm and embarrassingly parallel architecture allows us to index a 170 TB genome dataset in a mere 14 hours on a cluster of 100 nodes while competing methods require weeks.
Language is Power: Representing States Using Natural Language in Reinforcement Learning
Venue: arXiv cs.CL 2020
Year: 2020

Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks. However, as the dimensionality of the task increases, reinforcement learning methods tend to struggle. To overcome this, we explore methods for representing the semantic information embedded in the state. While previous methods focused on information in its raw form (e.g., raw visual input), we propose to represent the state using natural language. Language can represent complex scenarios and concepts, making it a favorable candidate for representation. Empirical evidence, within the domain of ViZDoom, suggests that natural language based agents are more robust, converge faster and perform better than vision based agents, showing the benefit of using natural language representations for reinforcement learning.
Provenance tracking in the LHCb software
Venue: arXiv cs.DC 2020
Year: 2020

Even though computational reproducibility is widely accepted as necessary for research validation and reuse, it is often not considered during the research process. This is because reproducibility tools are typically stand-alone and require additional training to be employed. In this article, we present a solution to foster reproducibility, which is integrated within existing scientific software that is actively used in the LHCb collaboration. Our provenance tracking service captures metadata of a dataset, which is then saved inside the output data file on the disk. The captured information allows a complete understanding of how the file was produced and enables a user to reproduce the dataset, even when the original input code (that was used to initially produce the dataset) is altered or lost. This article describes the implementation of the service and gives examples of its application.
A Machine Learning Model for Long-Term Power Generation Forecasting at Bidding Zone Level
Venue: arXiv cs.LG 2020
Year: 2020

The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and more recently many statistical or machine learning algorithms, and data-driven methods in general, are becoming subject of intense research. While generally the power research community focuses on power forecasting at the level of single plants, in a short future horizon of time, in this time we are interested in aggregated macro-area power generation (i.e., in a territory of size greater than 100000 km^2) with a future horizon of interest up to 15 days ahead. Real data are used to validate the proposed forecasting methodology on a test set of several months.
Visual Abstraction
Venue: arXiv cs.GR 2020
Year: 2020

In this article we revisit the concept of abstraction as it is used in visualization and put it on a solid formal footing. While the term \emph{abstraction} is utilized in many scientific disciplines, arts, as well as everyday life, visualization inherits the notion of data abstraction or class abstraction from computer science, topological abstraction from mathematics, and visual abstraction from arts. All these notions have a lot in common, yet there is a major discrepancy in the terminology and basic understanding about visual abstraction in the context of visualization. We thus root the notion of abstraction in the philosophy of science, clarify the basic terminology, and provide crisp definitions of visual abstraction as a process. Furthermore, we clarify how it relates to similar terms often used interchangeably in the field of visualization. Visual abstraction is characterized by a conceptual space where this process exists, by the purpose it should serve, and by the perceptual and cognitive qualities of the beholder. These characteristics can be used to control the process of visual abstraction to produce effective and informative visual representations.
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Venue: arXiv cs.CL 2020
Year: 2020

Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and ontology-free methods. An ontology-based method selects a value from a candidate-value list for each target slot, while an ontology-free method extracts spans from dialog contexts. Recent work introduced a BERT-based model to strike a balance between the two methods by pre-defining categorical and non-categorical slots. However, it is not clear enough which slots are better handled by either of the two slot types, and the way to use the pre-trained model has not been well investigated. In this paper, we propose a simple yet effective dual-strategy model for DST, by adapting a single BERT-style reading comprehension model to jointly handle both the categorical and non-categorical slots. Our experiments on the MultiWOZ datasets show that our method significantly outperforms the BERT-based counterpart, finding that the key is a deep interaction between the domain-slot and context information. When evaluated on noisy (MultiWOZ 2.0) and cleaner (MultiWOZ 2.1) settings, our method performs competitively and robustly across the two different settings. Our method sets the new state of the art in the noisy setting, while performing more robustly than the best model in the cleaner setting. We also conduct a comprehensive error analysis on the dataset, including the effects of the dual strategy for each slot, to facilitate future research.
DEVDAN: Deep Evolving Denoising Autoencoder
Venue: arXiv cs.LG 2020
Year: 2020

The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves an in-depth study because it characterizes a fixed network capacity that cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of the discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol.