OpenRaiser/Intern-Atlas huggingface dataset:
A Blind Signal Separation Algorithm for Energy Detection of Dynamic PU Signals
Venue: arXiv cs.IT 2020
Year: 2020

Energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this letter, we propose a low-complexity and accurate signal separation algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.
WiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi Signals
Venue: arXiv cs.HC 2020
Year: 2020

Eating is a fundamental activity in people's daily life. Studies have shown that many health-related problems such as obesity, diabetes and anemia are closely associated with people's unhealthy eating habits (e.g., skipping meals, eating irregularly and overeating). Traditional eating monitoring solutions relying on self-reports remain an onerous task, while the recent trend requiring users to wear expensive dedicated hardware is still invasive. To overcome these limitations, in this paper, we develop a device-free eating monitoring system using WiFi-enabled devices (e.g., smartphone or laptop). Our system aims to automatically monitor users' eating activities through identifying the fine-grained eating motions and detecting the chewing and swallowing. In particular, our system extracts the fine-grained Channel State Information (CSI) from WiFi signals to distinguish eating from non-eating activities and further recognizing users' detailed eating motions with different utensils (e.g., using a folk, knife, spoon or bare hands). Moreover, the system has the capability of identifying chewing and swallowing through detecting users' minute facial muscle movements based on the derived CSI spectrogram. Such fine-grained eating monitoring results are beneficial to the understanding of the user's eating behaviors and can be used to estimate food intake types and amounts. Extensive experiments with 20 users over 1600-minute eating show that the proposed system can recognize the user's eating motions with up to 95% accuracy and estimate the chewing and swallowing amount with 10% percentage error.
Online Continual Learning on Sequences
Venue: arXiv cs.LG 2020
Year: 2020

Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that address OCL must alleviate \textit{catastrophic forgetting} in which hidden representations are disrupted or completely overwritten when learning from streams of novel input. In this chapter, we summarize and discuss recent deep learning models that address OCL on sequential input through the use (and combination) of synaptic regularization, structural plasticity, and experience replay. Different implementations of replay have been proposed that alleviate catastrophic forgetting in connectionists architectures via the re-occurrence of (latent representations of) input sequences and that functionally resemble mechanisms of hippocampal replay in the mammalian brain. Empirical evidence shows that architectures endowed with experience replay typically outperform architectures without in (online) incremental learning tasks.
Automatic Identification of Types of Alterations in Historical Manuscripts
Venue: arXiv cs.LG 2020
Year: 2020

Alterations in historical manuscripts such as letters represent a promising field of research. On the one hand, they help understand the construction of text. On the other hand, topics that are being considered sensitive at the time of the manuscript gain coherence and contextuality when taking alterations into account, especially in the case of deletions. The analysis of alterations in manuscripts, though, is a traditionally very tedious work. In this paper, we present a machine learning-based approach to help categorize alterations in documents. In particular, we present a new probabilistic model (Alteration Latent Dirichlet Allocation, alterLDA in the following) that categorizes content-related alterations. The method proposed here is developed based on experiments carried out on the digital scholarly edition Berlin Intellectuals, for which alterLDA achieves high performance in the recognition of alterations on labelled data. On unlabelled data, applying alterLDA leads to interesting new insights into the alteration behavior of authors, editors and other manuscript contributors, as well as insights into sensitive topics in the correspondence of Berlin intellectuals around 1800. In addition to the findings based on the digital scholarly edition Berlin Intellectuals, we present a general framework for the analysis of text genesis that can be used in the context of other digital resources representing document variants. To that end, we present in detail the methodological steps that are to be followed in order to achieve such results, giving thereby a prime example of an Machine Learning application the Digital Humanities.
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
Venue: arXiv cs.CV 2020
Year: 2020

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most authoritative academic competitions in the field of Computer Vision (CV) in recent years. But applying ILSVRC's annual champion directly to fine-grained visual categorization (FGVC) tasks does not achieve good performance. To FGVC tasks, the small inter-class variations and the large intra-class variations make it a challenging problem. Our attention object location module (AOLM) can predict the position of the object and attention part proposal module (APPM) can propose informative part regions without the need of bounding-box or part annotations. The obtained object images not only contain almost the entire structure of the object, but also contains more details, part images have many different scales and more fine-grained features, and the raw images contain the complete object. The three kinds of training images are supervised by our multi-branch network. Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales. Our approach can be trained end-to-end, while provides short inference time. Through the comprehensive experiments demonstrate that our approach can achieves state-of-the-art results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets. Our code will be available at https://github.com/ZF1044404254/MMAL-Net
Evolutionary Multi-Objective Optimization Framework for Mining Association Rules
Venue: arXiv cs.NE 2020
Year: 2020

In this paper, two multi-objective optimization frameworks in two variants (i.e., NSGA-III-ARM-V1, NSGA-III-ARM-V2; and MOEAD-ARM-V1, MOEAD-ARM-V2) are proposed to find association rules from transactional datasets. The first framework uses Non-dominated sorting genetic algorithm III (NSGA-III) and the second uses Decomposition based multi-objective evolutionary algorithm (MOEA/D) to find the association rules which are diverse, non-redundant and non-dominated (having high objective function values). In both these frameworks, there is no need to specify minimum support and minimum confidence. In the first variant, support, confidence, and lift are considered as objective functions while in second, confidence, lift, and interestingness are considered as objective functions. These frameworks are tested on seven different kinds of datasets including two real-life bank datasets. Our study suggests that NSGA-III-ARM framework works better than MOEAD-ARM framework in both the variants across majority of the datasets.
Sample Complexity Result for Multi-category Classifiers of Bounded Variation
Venue: arXiv cs.LG 2020
Year: 2020

We control the probability of the uniform deviation between empirical and generalization performances of multi-category classifiers by an empirical L1 -norm covering number when these performances are defined on the basis of the truncated hinge loss function. The only assumption made on the functions implemented by multi-category classifiers is that they are of bounded variation (BV). For such classifiers, we derive the sample size estimate sufficient for the mentioned performances to be close with high probability. Particularly, we are interested in the dependency of this estimate on the number C of classes. To this end, first, we upper bound the scale-sensitive version of the VC-dimension, the fat-shattering dimension of sets of BV functions defined on R^d which gives a O(1/epsilon^d ) as the scale epsilon goes to zero. Secondly, we provide a sharper decomposition result for the fat-shattering dimension in terms of C, which for sets of BV functions gives an improvement from O(C^(d/2 +1)) to O(Cln^2(C)). This improvement then propagates to the sample complexity estimate.
Parallel Intent and Slot Prediction using MLB Fusion
Venue: arXiv cs.CL 2020
Year: 2020

Intent and Slot Identification are two important tasks in Spoken Language Understanding (SLU). For a natural language utterance, there is a high correlation between these two tasks. A lot of work has been done on each of these using Recurrent-Neural-Networks (RNN), Convolution Neural Networks (CNN) and Attention based models. Most of the past work used two separate models for intent and slot prediction. Some of them also used sequence-to-sequence type models where slots are predicted after evaluating the utterance-level intent. In this work, we propose a parallel Intent and Slot Prediction technique where separate Bidirectional Gated Recurrent Units (GRU) are used for each task. We posit the usage of MLB (Multimodal Low-rank Bilinear Attention Network) fusion for improvement in performance of intent and slot learning. To the best of our knowledge, this is the first attempt of using such a technique on text based problems. Also, our proposed methods outperform the existing state-of-the-art results for both intent and slot prediction on two benchmark datasets
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time
Venue: arXiv cs.LG 2020
Year: 2020

Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals. Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance. This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings. As a data input method it and can be applied to most machine learning models. The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records. Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.
Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods
Venue: arXiv cs.CV 2020
Year: 2020

This study presents early phase detection of Coronavirus (COVID-19), which is named by World Health Organization (WHO), by machine learning methods. The detection process was implemented on abdominal Computed Tomography (CT) images. The expert radiologists detected from CT images that COVID-19 shows different behaviours from other viral pneumonia. Therefore, the clinical experts specify that COVİD-19 virus needs to be diagnosed in early phase. For detection of the COVID-19, four different datasets were formed by taking patches sized as 16x16, 32x32, 48x48, 64x64 from 150 CT images. The feature extraction process was applied to patches to increase the classification performance. Grey Level Co-occurrence Matrix (GLCM), Local Directional Pattern (LDP), Grey Level Run Length Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), and Discrete Wavelet Transform (DWT) algorithms were used as feature extraction methods. Support Vector Machines (SVM) classified the extracted features. 2-fold, 5-fold and 10-fold cross-validations were implemented during the classification process. Sensitivity, specificity, accuracy, precision, and F-score metrics were used to evaluate the classification performance. The best classification accuracy was obtained as 99.68% with 10-fold cross-validation and GLSZM feature extraction method.
Deep Sets for Generalization in RL
Venue: arXiv cs.LG 2020
Year: 2020

This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent. This is done using a combination of object-wise permutation invariant networks inspired from Deep Sets and gated-attention mechanisms. In a 2D procedurally-generated world where agents targeting goals in natural language navigate and interact with objects, we show that these architectures demonstrate strong generalization capacities to out-of-distribution goals. We study the generalization to varying numbers of objects at test time and further extend the object-centered architectures to goals involving relational reasoning.
Optimal Local Explainer Aggregation for Interpretable Prediction
Venue: arXiv cs.LG 2020
Year: 2020

A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One proposed set of methods is training surrogate explainer models which approximate the more complex model. Explainer methods are generally classified as either local or global, depending on what portion of the data space they are purported to explain. The improved coverage of global explainers usually comes at the expense of explainer fidelity. One way of trading off the advantages of both approaches is to aggregate several local explainers into a single explainer model with improved coverage. However, the problem of aggregating these local explainers is computationally challenging, and existing methods only use heuristics to form these aggregations. In this paper we propose a local explainer aggregation method which selects local explainers using non-convex optimization. In contrast to other heuristic methods, we use an integer optimization framework to combine local explainers into a near-global aggregate explainer. Our framework allows a decision-maker to directly tradeoff coverage and fidelity of the resulting aggregation through the parameters of the optimization problem. We also propose a novel local explainer algorithm based on information filtering. We evaluate our algorithmic framework on two healthcare datasets---the Parkinson's Progression Marker Initiative (PPMI) data set and a geriatric mobility dataset---which is motivated by the anticipated need for explainable precision medicine. Our method outperforms existing local explainer aggregation methods in terms of both fidelity and coverage of classification and improves on fidelity over existing global explainer methods, particularly in multi-class settings where state-of-the-art methods achieve 70% and ours achieves 90%.
A generic ontology and recovery protocols for Human-Robot Collaboration (HRC) systems
Venue: arXiv cs.RO 2020
Year: 2020

Humans are considered as integral components of Human-Robot Collaboration (HRC) systems, not only as object (e.g. in health care), but also as operators and service providers in manufacturing. Sophisticated and complex tasks are to be collaboratively executed by devices (robots) and humans. We introduce a generic ontology for HRC systems. Description of humans is a part of the ontology. Critical and hazardous (for humans) situations, as well as corresponding safeguards are defined on the basis of the ontology. The ontology is an extension of the ontology introduced in Skarzynski et al. (2018) arXiv:1709.03300. The architecture of SO-MRS (see arXiv:1709.03300), a software platform for automatic task accomplishment, is extended to HRC systems. Ongoing experiments, carried out in a simulated HRC system, are to verify the ontology and the architecture.
Hazard recognition in an immersive virtual environment: Framework for the simultaneous analysis of visual search and EEG patterns
Venue: arXiv cs.HC 2020
Year: 2020

Unmanaged hazards in dangerous construction environments proved to be one of the main sources of injuries and accidents. Hazard recognition is crucial to achieve effective safety management and reduce injuries and fatalities in hazardous job sites. Still, there has been lack of effort that can efficiently assist workers in improving their hazard recognition skills. This study presents virtual safety training in an Immersive Virtual Environment (IVE) to enhance worker's hazard recognition skills. A worker wearing a Virtual Reality (VR) device, that is equipped with an eye-tracker, virtually recognizes hazards on simulated construction sites while a brainwave-sensing device records brain activities. This platform can analyze the overall performance of the workers in a visual hazard recognition task and identify hazards that need additional intervention for each worker. This study provides novel insights on how a worker's brain and eye act simultaneously during a visual hazard recognition process. The presented method can take current safety training programs into another level by providing personalized feedback to the workers.
Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework
Venue: arXiv cs.NE 2020
Year: 2020

Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-based evolutionary algorithm, discrete local search and continuous global optimisation. For assessing the performance of the proposed hybrid method, we compare it with a wide variety of state-of-the-art optimisation approaches, including six continuous evolutionary algorithms, four discrete search techniques and three hybrid optimisation methods. The results show that the proposed method performs considerably better in terms of convergence speed and farm output.
Soft-Error and Hard-fault Tolerant Architecture and Routing Algorithm for Reliable 3D-NoC Systems
Venue: arXiv cs.AR 2020
Year: 2020

Network-on-Chip (NoC) paradigm has been proposed as an auspicious solution to handle the strict communication requirements between the increasingly large number of cores on a single multi and many-core chips. However, NoC systems are exposed to a variety of manufacturing, design and energetic particles factors making them vulnerable to permanent (hard) faults and transient (soft) errors. In this paper, we present a comprehensive soft error and hard fault tolerant 3D-NoC architecture, named 3D-Hard-Fault-Soft-Error-Tolerant-OASIS-NoC (3D-FETO). With the aid of adaptive algorithms, 3D-FETO is capable of detecting and recovering from soft errors occurring in the routing pipeline stages and is leveraging on reconfigurable components to handle permanent faults occurrence in links, input buffers, and crossbar. In-depth evaluation results show that the 3D-FETO system is able to work around different kinds of hard faults and soft errors while ensuring graceful performance degradation, minimizing the additional hardware complexity and remaining power-efficient.
Resilience in Collaborative Optimization: Redundant and Independent Cost Functions
Venue: arXiv cs.DC 2020
Year: 2020

This report considers the problem of Byzantine fault-tolerance in multi-agent collaborative optimization. In this problem, each agent has a local cost function. The goal of a collaborative optimization algorithm is to compute a minimum of the aggregate of the agents' cost functions. We consider the case when a certain number of agents may be Byzantine faulty. Such faulty agents may not follow a prescribed algorithm, and they may send arbitrary or incorrect information regarding their local cost functions. A reasonable goal in presence of such faulty agents is to minimize the aggregate cost of the non-faulty agents. In this report, we show that this goal can be achieved if and only if the cost functions of the non-faulty agents have a minimal redundancy property. We present different algorithms that achieve such tolerance against faulty agents, and demonstrate a trade-off between the complexity of an algorithm and the properties of the agents' cost functions. Further, we also consider the case when the cost functions are independent or do not satisfy the minimal redundancy property. In that case, we quantify the tolerance against faulty agents by introducing a metric called weak resilience. We present an algorithm that attains weak resilience when the faulty agents are in the minority and the cost functions are non-negative.
Geometrically Mappable Image Features
Venue: arXiv cs.CV 2020
Year: 2020

Vision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gaining popularity due to recent advances in machine learning. Features that uniquely describe the visual contents of images have a wide range of applications, including image retrieval and understanding. In this work, we propose a method that learns image features targeted for image-retrieval-based localization. Retrieval-based localization has several benefits, such as easy maintenance and quick computation. However, the state-of-the-art features only provide visual similarity scores which do not explicitly reveal the geometric distance between query and retrieved images. Knowing this distance is highly desirable for accurate localization, especially when the reference images are sparsely distributed in the scene. Therefore, we propose a novel loss function for learning image features which are both visually representative and geometrically relatable. This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional. In our experiments we show that our features not only offer significantly better localization accuracy, but also allow to estimate the trajectory of a query sequence in absence of the reference images.