Reviewerly/exHarmony huggingface dataset:
Towards Interpretable Reinforcement Learning with State Abstraction Driven by External Knowledge

Advances in deep reinforcement learning have demonstrated its effectiveness in a wide variety of domains. Deep neural networks are capable of approximating value functions and policies in complex environments. However, deep neural networks inherit a number of drawbacks. Lack of interpretability limits their usability in many safety-critical real-world scenarios. Moreover, they rely on huge amounts of data to learn efficiently. This may be suitable in simulated tasks, but restricts their use to many real-world applications. Finally, their generalization capability is low, the ability to determine that a situation is similar to one encountered previously. We present a method to combine external knowledge and interpretable reinforcement learning. We derive a rule-based variant version of the Sarsa(λ) algorithm, which we call Sarsa-rb(λ), that augments data with prior knowledge and exploits similarities among states. We demonstrate that our approach leverages small amounts of prior knowledge to significantly accelerate the learning in multiple domains such as trading or visual navigation. The resulting agent provides substantial gains in training speed and performance over deep q-learning (DQN), deep deterministic policy gradients (DDPG), and improves stability over proximal policy optimization (PPO).
A Light-weight Deep Feature based Capsule Network

Capsule Network (CapsNet) has motivated researchers to work on it due to its distinct capability of retaining spatial correlations between image features. However, its applicability is still limited because of its intensive computational cost, memory usage and bandwidth requirement. This paper proposes a computationally efficient, lightweight CapsNet which paves its way forward for deployment in constrained edge devices as well as in web based applications. The proposed framework consists of Capsule layers and a deep feature representation layer as an input for capsules. The deep feature representation layer comprises of a series of feature blocks, containing convolution with a 3 × 3 kernel followed by batch normalization and convolution with a 1 × 1 kernel. The deeper or better represented input features help to improve recognition performance even with lesser number of capsules, making the network computationally more efficient. The efficacy of the proposed framework is validated by performing rigorous experimental studies on different datasets, such as CIFAR-10, FMNIST, MNIST and SVHN which include images of object classes as well as text characters. A comparative analysis has also been done with the state-of-the-art technique CapsNet. The comparison with recognition accuracy ensures that, the proposed architecture with deep input features provides more efficient routing between the capsules as compared to CapsNet. The proposed lightweight network has scaled down the number of parameters up to 60% of CapsNet, which is another significant contribution. This is achieved by collaborative effect of deep feature generation module and parametric changes performed in the primary capsule layer.
Frame Rate Up-Conversion Using Bi-Directional Optical Flows With Dual Regularization

In this paper, we present a frame rate up-conversion (FRUC) scheme that uses optical flows in forward and backward directions and with two different regularization parameters to derive alternative motion vector fields. The motion vectors are then assessed for their relevance, resulting in prediction weights assigned to each candidate motion vector. Multiple hypothesis reconstruction is then performed, where overlapping patches of pixels associated with multiple motion vectors are fused using their corresponding weights to yield pixels of an interpolated frame. Four flavors of our approach are proposed having different tradeoffs between quality and computational complexity. Experiments conducted using standard test set show that all our schemes yield significantly higher quality compared to existing FRUC algorithms.
Ontology Learning Approaches to Provide Domain-Specific Knowledge Base

Ontology learning techniques are based on extensive basic knowledge, from unstructured data to structured data. Creating an ontology manually is undoubtedly a time consuming process. In addition, manually-curated background knowledge is a scarce resource for many domains. Ontology learning is a relatively new research area that draws from related fields. However, a number of approaches are still rising up and, what is more, they vary and evolve across different aspects. Therefore, the purpose of this article is to provide comprehensive knowledge about selected groups of methods, tools and approaches supporting ontology. Presenting different approaches to learning ontology and including them in one publicly available summary, aims to provide an overview of the most appropriate methods, tools and approaches to building ontologies from various sources. Besides, the proposed solution in this article will expand the already offered ontology of learning methods with additional, more detailed functions. In addition to the domain ontology offered, this is another goal of this article. The correctness of the proposed ontology was verified by the constructed competence questions and implemented using Description Logic query mechanism.
SeCrowd: Efficient secure interactive crowdsourcing via permission-based signatures

Crowdsourcing is a powerful paradigm to provide services by soliciting help from a pool of contributors. However, the security threats in crowdsourcing, especially in interactive crowdsourcing where contributors interact with each other for collaboration, are not well studied. External adversaries who may inject malicious results are the major concerns of all involved parties in crowdsourcing. In this paper, an efficient secure interactive crowdsourcing framework, called SeCrowd, is proposed to protect the authenticity in the crowdsourcing platform against external adversaries. The framework is characterized by a recently proposed cryptographic primitive, called permission-based signature, which reveals the signer's identity to permissive verifiers only, while safeguards it against others. In this way, contributors can identify each other based on their signed contributions, but their identities remain unknown to other users. Moreover, users can verify whether the signed contributions are made by legal contributors without being aware of the actual signer's identities, which guarantees that the contributions cannot be tampered or forged. We prove that SeCrowd achieves unforgeability in the random oracle model. Comprehensive analysis and experiments showed that our framework is secure and highly efficient in terms of computation and communication cost.
U-Net and Active Contour Methods for Brain Tumour Segmentation and Visualization

Gliomas are a type of brain and spinal tumour. They originate from glial cells that form the stroma of nerve tissue. Gliomas constitute about 70% of all intracranial tumours and are perceived as the leading cause of death due to brain tumours. This paper presents a comparison of three approaches for glioma detection, volume computation and 3D visualization. The classic approaches based on thresholding and active contour methods were compared with a deep learning implementation. The state-of-the-art model, named U-Net, was used to segment biomedical images which effectively removed bone images from computed tomography (CT) scans. To portion the tumour area, the Morphological Geodesic Active Contour method was applied. Model training was enriched by implementing a data augmentation strategy. Numerical results of tumour volume in cm3 were presented as well as a 3D visualization example.
Short-Term Photovoltaic Power Forcasting Based on PSO-BP Neural Network

With the continuous development of photovoltaic power generation technology, its power generation process is characterized by fluctuation and intermittence. In order to make photovoltaic power more stable and grid operation more safe and reliable, it is necessary to predict the output power of photovoltaic power generation. On the basis of analyzing the influencing factors of photovoltaic output, the traditional Back Propagation neural network (BP) power prediction model is optimized and upgraded, and a back propagation neural network power prediction model based on Particle Swarm Optimization (PSO) is proposed. The effectiveness of the algorithm is proved by case studies.
Low Latency And Low-Level Sensor Fusion For Automotive Use-Cases

This work proposes a probabilistic low level automotive sensor fusion approach using LiDAR, RADAR and camera data.The method is stateless and directly operates on associated data from all sensor modalities.Tracking is not used, in order to reduce the object detection latency and create existence hypotheses per frame.The probabilistic fusion uses input from 3D and 2D space.An association method using a combination of overlap and distance metrics, avoiding the need for sensor synchronization is proposed.A Bayesian network executes the sensor fusion.The proposed approach is compared with a state of the art fusion system, which is using multiple sensors of the same modality and relies on tracking for object detection.Evaluation was done using low level sensor data recorded in an urban environment.The test results show that the low level sensor fusion reduces the object detection latency.
An online learning algorithm for a neuro-fuzzy classifier with mixed-attribute data

General fuzzy min–max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn directly from mixed-attribute data without using encoding techniques. While categorical feature encoding methods can be used with the GFMMNN learning algorithms, they exhibit many shortcomings. Other improved approaches proposed in the literature are not suitable for online learning algorithms working in the dynamically changing environments without ability to retrain or access full historical data, which are usually required for many real world applications. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. It uses the change in the entropy values of categorical features of the samples contained in a hyperbox to determine if the current hyperbox can be expanded to include the categorical values of a new training instance. An extended architecture of the original GFMMNN and its new membership function are introduced for mixed-attribute data. Important mathematical properties of the proposed learning algorithms are also presented and proved in this paper. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms for the GFMM model.
Learning Filterbanks from Raw Waveform for Accent Classification

Most of the applications in speech use mel-frequency spectral coefficients (MFSC) as features as they match the human perceptual mechanism, where the emphasis is given to vocal tract characteristics. But in accent classification, mel-scale distribution of filters may not always be the best representations, e.g., pitch accented languages where the emphasis should be on vocal source information too. Motivated by this, we use end-to-end classification of accents directly from waveforms which will reduce the effort of designing features specific to each corpus. The convolution neural network (CNN) model architecture is designed in such a way that the initial layers exhibit similar operation as in MFSC by initializing the weights using time approximate of MFSC. The entire network along with initial layers is trained to learn accent classification. We observed that learning directly from waveform improved the performance of accent classification when compared to CNN trained on hand-engineered features by 10.94% UAR on the test dataset of common voice corpus. Analyzing the filters after learning, we observed changes in distribution and bandwidths of center frequencies. We further observed the importance of appropriately initializing CNN filters.
A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts

Programming screencasts can be a rich source of documentation for developers. However, despite the availability of such videos, the information available in them, and especially the source code being displayed is not easy to find, search, or reuse by programmers. Recent work has identified this challenge and proposed solutions that identify and extract source code from video tutorials in order to make it readily available to developers or other tools. A crucial component in these approaches is the Optical Character Recognition (OCR) engine used to transcribe the source code shown on screen. Previous work has simply chosen one OCR engine, without consideration for its accuracy or that of other engines on source code recognition. In this paper, we present an empirical study on the accuracy of six OCR engines for the extraction of source code from screencasts and code images. Our results show that the transcription accuracy varies greatly from one OCR engine to another and that the most widely chosen OCR engine in previous studies is by far not the best choice. We also show how other factors, such as font type and size can impact the results of some of the engines. We conclude by offering guidelines for programming screencast creators on which fonts to use to enable a better OCR recognition of their source code, as well as advice on OCR choice for researchers aiming to analyze source code in screencasts.
FPA-DNN: A Forward Propagation Acceleration based Deep Neural Network for Ship Detection

Ship detection in optical satellite images has played an important role in the field of remote sensing for a long time. Many detection methods have been proposed to address the ship detection problem, and most of them mainly focus on the improvement of detection accuracy but rarely pay attention to the detection speed. In this paper, we not only consider the improvement of detection accuracy, but also try to speed up the detection process. Based on the YOLOv2 model, we propose a forward propagation acceleration-based deep neural network model (FPA-DNN) to enhance the performance of the ship detection. The FPA-DNN model is a hybrid learning model, in which the deep neural network model LSDN can effectively reduce the number of parameters and improve the detection speed with no accuracy loss, and the pruning based forward propagation acceleration algorithm can remove the redundant convolution kernels and further speed up the detection process. Experimental results on the optical remote sensing image dataset show that, compared with several state-of-the-art deep learning models, 1) the LSDN model outperforms the others on the detection accuracy and detection speed; and 2) the FPA-DNN model can further improve the detection accuracy and speed up the detection process significantly.
Localizing Firearm Carriers By Identifying Human-Object Pairs

Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes. In a given image, human and firearms are separately detected. Each detected human is paired with each detected firearm, allowing us to create a paired bounding box that contains both object and the human. A network is trained to classify these paired-bounding-boxes into human carrying the identified firearm or not. Extensive experiments were performed to evaluate the effectiveness of the algorithm, including exploiting full pose of the human, hand-keypoints, and their association with the firearm. The knowledge of spatially localized features is key to the success of our method by using multi-size proposals with adaptive average pooling. We have also extended a previously existing firearm detection dataset, by adding more images and tagging in the extended dataset the human-firearm pairs (including bounding boxes for firearms and gunmen). The experimental results $({78.5 AP}_{hold})$ demonstrate effectiveness of the proposed method.
Visual sketching

Writing code is difficult and time consuming. This vision paper proposes Visual Sketching, a synthesis technique that produces code implementing the likely intent associated with an image. We describe potential applications of Visual Sketching, how to realize it, and implications of the technology.
Entity–Relation Extraction—A Novel and Lightweight Method Based on a Gate Linear Mechanism

Entity–relation extraction has attracted considerable attention in recent years as a fundamental task in natural language processing. The goal of entity–relation extraction is to discover the relation structures of entities from a natural language sentence. Most existing models approach this task using recurrent neural nets (RNNs); however, given the sequential nature of RNNs, the states cannot be computed in parallel, which slows the machine comprehension. In this paper, we propose a new end-to-end model based on dilated convolutional units and the gate linear mechanism as an alternative to those recurrent models. We find that relation extraction becomes more difficult as the sentence length increases. In this paper, we introduce dynamic convolutions based on lightweight convolutions to process long sequences, which thus reduces the number of parameters to a low level. Another challenge in relation extraction is relation spans potentially overlapping in a sentence, representing a bottleneck for the detection of multiple relational triplets. To alleviate this problem, we design an entirely new prediction scheme to extract relational pairs and additionally boost performance. We conduct experiments on two widely used datasets, and the results show that our model outperforms the baselines by a large margin.
Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images

Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the x-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient.We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels.Across a range of low-dose x-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose x-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained.The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose x-ray images when algorithms designed for additive Gaussian noise are applied.
A Method of Using News Sentiment for Stock Investment Strategy

This study evaluates the sentiment of Japanese news and attempts to apply it to investment strategies in individual stocks. When the investment was made at daily, weekly, and monthly frequencies, the effectiveness of the investment was found in daily, but its effectiveness was lost in weekly and monthly frequencies. This reveals that the validity period of sentiment in individual stocks is as short as daily.
Towards Automated Diagnosis with Attentive Multi-modal Learning Using Electronic Health Records and Chest X-Rays

Jointly learning from Electronic Health Records (EHR) and medical images is a promising area of research in deep learning for medical imaging. Using the context available in EHR together with medical images can lead to more efficient data usage. Recent work has shown that jointly learning from EHR and medical images can indeed improve performance on several tasks. Current methods are however still not independent of clinician input. To obtain an automated method only prior patient information should be used together with a medical image, without the reliance on further clinician input. In this paper we propose an automated multi-modal method which creates a joint feature representation based on prior patient information from EHR and associated X-ray scan. This feature representation, which joins the two different modalities through attention leverages the contextual relationship between the modalities. This method is used to perform two tasks: diagnosis classification and free-text diagnosis generation. We show the benefit of the multi-modal approach over single-modality approaches on both tasks.
A Modified Fourier-Mellin Approach For Source Device Identification On Stabilized Videos

To decide whether a digital video has been captured by a given device, multimedia forensic tools usually exploit characteristic noise traces left by the camera sensor on the acquired frames.This analysis requires that the noise pattern characterizing the camera and the noise pattern extracted from video frames under analysis are geometrically aligned.However, in many practical scenarios this does not occur, thus a re-alignment or synchronization has to be performed.Current solutions often require time consuming search of the realignment transformation parameters.In this paper, we propose to overcome this limitation by searching scaling and rotation parameters in the frequency domain.The proposed algorithm tested on real videos from a well-known state-of-the-art dataset shows promising results.
Robust Adaptive Linear Discriminant Analysis with Bidirectional Reconstruction Constraint

Linear discriminant analysis (LDA) is a well-known supervised method for dimensionality reduction in which the global structure of data can be preserved. The classical LDA is sensitive to the noises, and the projection direction of LDA cannot preserve the main energy. This article proposes a novel feature extraction model with l 2,1 norm constraint based on LDA, termed as RALDA. This model preserves within-class local structure in the latent subspace according to the label information. To reduce information loss, it learns a projection matrix and an inverse projection matrix simultaneously. By introducing an implicit variable and matrix norm transformation, the alternating direction multiple method with updating variables is designed to solve the RALDA model. Moreover, both computational complexity and weak convergence property of the proposed algorithm are investigated. The experimental results on several public databases have demonstrated the effectiveness of our proposed method.