Reviewerly/exHarmony huggingface dataset:
Modular Decomposition-Based Graph Compression for Fast Reachability Detection

Fast reachability detection is one of the key problems in graph applications. Most of the existing works focus on creating an index and answering reachability based on that index. For these approaches, the index construction time and index size can become a concern for large graphs. More recently query-preserving graph compression has been proposed, and searching reachability over the compressed graph has been shown to be able to significantly improve query performance as well as reducing the index size. In this paper, we introduce a multilevel compression scheme for DAGs, which builds on existing compression schemes, but can further reduce the graph size for many real-world graphs. We propose an algorithm to answer reachability queries using the compressed graph. Extensive experiments with four existing state-of-the-art reachability algorithms and 12 real-world datasets demonstrate that our approach outperforms the existing methods. Experiments with synthetic datasets ensure the scalability of this approach. We also provide a discussion on possible compression for k-reachability.
Efficient Human Activity Classification from Egocentric Videos Incorporating Actor-Critic Reinforcement Learning

In this paper, we introduce a novel framework to significantly reduce the computational cost of human temporal activity recognition from egocentric videos while maintaining the accuracy at the same level. We propose to apply the actor-critic model of reinforcement learning to optical flow data to locate a bounding box around region of interest, which is then used for clipping a sub-image from a video frame. We also propose to use one shallow and one deeper 3D convolutional neural network to process the original image and the clipped image region, respectively. We compared our proposed method with another approach using 3D convolutional networks on the recently released Dataset of Multimodal Semantic Egocentric Video. Experimental results show that the proposed method reduces the processing time by 36.4% while providing comparable accuracy at the same time.
Quality assessment of images with multiple distortions based on phase congruency and gradient magnitude

In image communication systems, images are often contaminated by multiple types of distortions. However, most existing image quality assessment (IQA) methods mainly focused on a single type of distortions. In this paper, we proposed a no-reference (NR) IQA method for images with multiple distortions. Image distortions not only destroy the intensity of low-level image features, but also alter their distribution, to both of which the human vision system (HVS) is sensitive. Based on these observations, low-level features are represented by phase congruency (PC) which is consistent with human perception. The distribution of low-level features is extracted using local binary pattern (LBP) in PC domain at multiple scales, which can effectively characterize the impact of multiple distortions on images. Given that PC is contrast invariant while the contrast does affect perceptual image quality of the HVS, image gradient magnitude (GM) is employed as a weighting factor for LBP histogram creation. Finally a support vector regression model is trained to map the gradient-weighted LBP histograms in PC domain at multi-scale to quality scores. Experimental results on two benchmark databases demonstrate that the proposed method achieves high consistency with subjective perception and performs better than other state-of-the-art full-reference (FF) and NR IQA methods.
Fast Image Enhancement Based on Maximum and Guided Filters

Retinex theory gives an effective tool for adjusting the brightness of low-light image by factorizing its values in terms of illumination and reflectance. In this paper, a Retinex image enhancement algorithm is proposed based on maximum and guided filters to improve the brightness of low-light images. The illumination is calculated by the maximum filter and edge-preserving smoothed by the guided filter. Both subjective and objective tests show that our method can achieve better visual quality than other state-of-the-art. Furthermore, because of the high computational efficiency of the maximum and guided filters, our method runs more than 7× faster than other state-of-the-art.
Generative Adversarial Networks (GAN) based Anomaly Detection in Industrial Software Systems

Adopting an accurate anomaly detection mechanism is crucial for industrial software systems in order to prevent system outages that can deteriorate system availability. However, employing a supervised machine learning technique to detect anomalies in large production scale industrial software systems is highly impractical due to the requirement of annotated data. This raises the need for comprehensive semi-supervised and unsupervised anomaly detection mechanisms. This paper presents the application of Generative Adversarial Network (GAN) based models to detect system anomalies using semi-supervised one-class learning. We show that the use of a variant of GAN known as bidirectional GAN (BiGAN) gives augmented results when compared to the traditional GAN based anomaly detection, for the selected industrial system. Moreover, the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training. The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection, Variational Autoencoders (VAEs) when the size of the feature space increases.
PCCN:POINT Cloud Colorization Network

This paper investigates into the colorization problem which generates color value to point clouds. It is difficult to apply image colorization methods to three dimension directly. Currently, Generative Adversarial Network (GAN) is the most reliable solution to the colorization problem. However, it cannot directly process point cloud data with geometric data structures. In this paper, we propose a scheme for point cloud colorization which can effectively solve the permutation invariance of points as well as produce realistic color effect. In order to achieve a better result, a well-designed network that aggregates the point features as global feature is built to guide color generation. After computing the global point cloud feature vector, we integrates both the local and global information by concatenating the global feature with each of the point features. Extensive experimental results demonstrate the effectiveness of the proposed algorithm.
Decoding Parallel Program Execution by using Java Interactive Visualization Environment (JIVE): Behavioral and Performance Analysis

With the proliferation of multi-core systems in the last decade or so even the personal computers have acquired the capability of supporting parallel programs. However, most applications are simply not designed to take advantage of this capability. This is firstly due to the difficulty in comprehending parallel programs. Secondly, the speed-up achieved due to parallelism is diminished by the overhead incurred. We study both these aspects in the context of fork/join, the parallel programming framework supported by Java and Java Interactive Visualization Environment (JIVE), a dynamic analysis framework for debugging and visualizing Java programs. In this paper, we demonstrate how JIVE can be used to decode parallel program execution and their behavior on single, dual and quad core systems. We also present the results of the performance study undertaken to compare the performance of parallel programs against their sequential and multi-threaded counterparts for small, medium and large sized executions.
Salient Object Detection in Low Contrast Images via Global Convolution and Boundary Refinement

Benefit from the powerful features created by using deep learning technology, salient object detection has recently witnessed remarkable progresses. However, it is difficult for a deep network to achieve satisfactory results in low contrast images, due to the low signal to noise ratio property, thus previous deep learning based saliency methods may output maps with ambiguous salient objects and blurred boundaries. To address this issue, we propose a deep fully convolutional framework with a global convolutional module (GCM) and a boundary refinement module (BRM) for saliency detection. Our model drives the network to learn the local and global information to discriminate pixels belonging to salient objects or not, thus can produce more uniform saliency map. To refine the localization and classification performance of the network, five GCMs are integrated to preserve more spatial knowledge of feature maps and enable the densely connections with classifiers. Besides, to propagate saliency information with rich boundary content, a BRM is embed behind each convolutional layer. Experiments on six challenging datasets show that the proposed saliency model achieves state-of-the-art performance compared to nine existing approaches in terms of nine evaluation metrics.
Three-Dimensional Convolutional Neural Network Pruning with Regularization-Based Method

Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs) are restricted by their massive computation and storage consumption. To solve this problem, we propose a three-dimensional regularization-based neural network pruning method to assign different regularization parameters to different weight groups based on their importance to the network. Further we analyze the redundancy and computation cost for each layer to determine the different pruning ratios. Experiments show that pruning based on our method can lead to 2× theoretical speedup with only 0.41% accuracy loss for 3D-ResNet18 and 3.28% accuracy loss for C3D. The proposed method performs favorably against other popular methods for model compression and acceleration.
A Lightweight Security Protocol for IoT Using Merkle Hash Tree and Chaotic Cryptography

Security is one of the primaryNesa, Nashreen concerns in an Internet of things (IoT) environmentBanerjee, Indrajit as they are deployed in critical applications that directly affect human lives. For this purpose, a security protocol that involves both authentication of deployed IoT devices and encryption of generated data is proposed in this paper. Encryption algorithms based on chaos are known to satisfy the basic requirements of the cryptosystem such as high sensitivity, high computational speed and high security. The chaos-based encryption algorithm is built upon a modified quadratic map named as quadratic sinusoidal map which exhibits better array of chaotic regime when compared to the traditional logistic map. The authentication protocol, on the other hand, is based on Merkle hash tree that has been improved to adapt to an IoT environment. The control parameters and the initial condition for the map are derived from the Merkle hash tree. The proposed algorithm involves cryptographic operations that incur very low computational cost and requires relatively little storage and at the same time are highly resilient to security attacks.
Adaptive Labeling for Deep Learning to Hash

Hash function learning has been widely used for large-scale image retrieval because of the efficiency of computation and storage. We introduce AdaLabelHash, a binary hash function learning approach via deep neural networks in this paper. In AdaLabelHash, class label representations are variables that are adapted during the backward network training procedure. We express the labels as hypercube vertices in a K-dimensional space, and the class label representations together with the network weights are updated in the learning process. As the label representations (or referred to as codewords in this work), are learned from data, semantically similar classes will be assigned with the codewords that are close to each other in terms of Hamming distance in the label space. The codewords then serve as the desired output of the hash function learning, and yield compact and discriminating binary hash representations. AdaLabelHash is easy to implement, which can jointly learn label representations and infer compact binary codes from data. It is applicable to both supervised and semi-supervised hash. Experimental results on standard benchmarks demonstrate the satisfactory performance of AdaLabelHash.
Mouse Behavior as an Index of Phishing Awareness

Phishing attacks are one of the most common security challenges faced by individuals and organizations today. Although many techniques exist to filter out phishing emails, they are not always effective leaving humans as the most vulnerable links in the information security chain. This paper presents a study investigating how human behavior, especially mouse movements, may reflect cybersecurity awareness, in particular to phishing emails. Using an email sorting task, we examined three key mouse movement features: hover, slow movement, and response time. The results suggest that slow mouse movements indicate high awareness of phishing emails and could be used to determine the likelihood of users falling victim to phishing attacks. However, contrary to intuition, response time and mouse hovering behaviors do not correlate with phishing awareness.
LSTM-Based Speech Segmentation for TTS Synthesis

This paper describes experiments on speech segmentation for the purposes of text-to-speech synthesis. We used a bidirectional LSTM neural network for framewise phone classification and another bidirectional LSTM network for predicting the duration of particular phones. The proposed segmentation procedure combines both outputs and finds the optimal speech-phoneme alignment by using the dynamic programming approach. We introduced two modifications to increase the robustness of phoneme classification. Experiments were performed on 2 professional voices and 2 amateur voices. A comparison with a reference HMM-based segmentation with additional manual corrections was performed. Preference listening tests showed that the reference and experimental segmentation are equivalent when used in a unit selection TTS system.
Image Super-Resolution Using CNN Optimised By Self-Feature Loss

Despite the success of state-of-the-art single image superresolution algorithms using deep convolutional neural networks in terms of both reconstruction accuracy and speed of execution, most proposed models rely on minimizing the mean square reconstruction error. More recently, inspired by transfer learning, Mean Square Error (MSE)-based content loss estimation has been replaced with loss calculated on feature maps of the pre-trained networks, e.g. VGG-net used for ImageNet classification. We demonstrate that this alternative approach is sub-optimal and adds false colour and mosaicking artefacts in the reconstructed images. In this paper, we present the first Convolutional Neural Network (CNN) capable of optimizing its parameters by minimizing the loss of in-network, self-features. To achieve this, we propose a new loss function for a light CNN architecture, which contains a mapping of residual blocks between the low and high resolution images. Our proposed method performs better than existing methods that apply perceptual loss by effectively suppressing false colour-effects. We show that the in-network features used for the determination of the loss function will give new insights for future research and applications when designing deep learning networks for other computer vision tasks such as demosaicing and denoising.
A Hierarchical Learning Approach for Detection of Clustered Microcalcifications in Mammograms

In computerized detection of clustered microcalcifications (MCs), the individual MCs in a mammogram image are typically identified by a pattern classifier based on the local image features at a location under consideration. In this work we investigate a hierarchical learning approach for detection of clustered MCs in which we exploit the property that the individual MCs in a cluster region tend to appear in close vicinity of each other. In the experiments we demonstrated the proposed approach on a set of 542 mammogram images and evaluated the detection performance by using free-response receiver operating characteristic (FROC) analysis. The results show that the proposed approach could effectively improve the detection accuracy by reducing the level of false positives (FPs).
Multi-agent Pathfinding Based on Improved Cooperative A* in Kiva System

In the Multi-Agent Pathfinding problem, a set of agents with distinct start and goal ports are assigned to finish different jobs. The problem's task is to use a typical algorithm to compute a path for each agent, so that every agent can finish their jobs with satisfied performance. The study of this problem can improve the efficiency of robots in warehouse material transportation and logistics sorting. In this paper, we propose an algorithm named Improved Cooperative A*(ICA*) by introducing addition pathfinding cost for less turns and overlapping among paths. Furthermore, we propose a principle called Dynamic Weight Guidance that can dynamically provide a guiding strategy for each agent. Experimental results demonstrate that our algorithm can reduce the total number of moving steps, makespan and waiting times of all agents to a certain extent.
BSC Participation in the WMT Translation of Biomedical Abstracts

This paper describes the machine translation systems developed by the Barcelona Supercomputing (BSC) team for the biomedical translation shared task of WMT19.Our system is based on Neural Machine Translation unsing the OpenNMT-py toolkit and Transformer architecture.We participated in four translation directions for the English/Spanish and English/Portuguese language pairs.To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS.
Consumer perceptions on smart wearable devices for medical and wellness purposes

This study examined Smart Wearable Devices (SWDs) in the form of smartwatches, fitness trackers, smart textiles and jewelry for medical and wellness purposes in the IoT era. Forty-five volunteers participated in a semi-structured interview. The data collected from respondents was used to study consumer perceptions of SWDs. Although many of the participants in the study had heard of SWDs, almost none knew what Body Sensor Networks (BSNs) were. An investigation was also conducted to find a solution to the problem of abandoning the use of SWDs. In the results, many respondents stated a `real need' for the device as the most important feature for not abandoning it. Some of the smartwatches, fitness trackers, smart textiles and jewelry exhibited at CES 2019 - Las Vegas were listed in the text to highlight the advances in the SWD market. In the authors' opinions, very important steps forward have been made with smart jewelry.
Optical Flow Estimation Using Spatial-Channel Combinational Attention-Based Pyramid Networks

Recently, learning to estimate optical flow via deep convolutional networks is attracting significant attention. In this paper, we introduce a spatial-channel attention module into optical flow estimation, which infers attention maps along two separated dimensions, channel and spatial, and then integrates these separated attention maps into a fusion attention map for feature refinement. We embed this module into spatial pyramid network, which can adaptively learn the channel and spatial attention maps at each level for modifying the different scaled features and can further improve the accuracy of optical flow estimation. Our network is trained on FlyingChairs and FlyingThings3D datasets with a supervised manner, and is further tested on MPI-Sintel benchmark. The experimental results show that using the spatial-channel attention unit is beneficial for dense flow estimation and our approach is comparable with the state-of-the-art methods.
Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework

To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible. However, radars, one of the sensor modalities autonomous cars heavily rely on, often only provide sparse, noisy detections. These have to be accumulated over time to reach a high enough confidence about the static parts of the environment. For radars, the state is typically estimated by accumulating inverse detection models (IDMs). We employ the recently proposed evidential convolutional neural networks which, in contrast to IDMs, compute dense, spatially coherent inference of the environment state. Moreover, these networks are able to incorporate sensor noise in a principled way which we further extend to also incorporate model uncertainty. We present experimental results which show that this approach leads to a denser environment perception in only one time step while at the same time reducing the false positive and negative rates.
Design and Development of Multimedia and Multi-Marker Detection Techniques in Interactive Augmented Reality Colouring Book

The aim of this paper is to the design and develop multimedia and multi-markers detection techniques in interactive Augmented Reality (AR) colouring book application for aquarium museum. This study is conducted to create entertaining AR colouring mobile application on Android Operating System which allows users to express, create and interact with their creativity through colouring activities. It allows users to engage and relish the stimulating colouring book content by switching between a reality and augmented world. Conversely, users may tend to lose interest in the colouring activities, but with AR technology it keeps colouring relaxing and inspiring. The design and development of this project was carried out using Unity3D integrates with Vuforia Engine. The multimedia and multi-markers scripting was written in C# programming language.
Remote Tongue Based Control of a Wheelchair Mounted Assistive Robotic Arm – a proof of concept study

Assistive robotic arms (ARM) have the potential to make individuals with tetraplegia more independent by enabling them to perform physical activities. Usually the ARMs are used within reach of the user while the user is sitting in a powered wheelchair. In this proof of concept study, we demonstrated for the first time a tongue based method to provide an individual with tetraplegia the ability to fetch remote objects such as beverages while being bedridden. This is done through tongue-based control of a mobile and remote assistive robotic manipulator (ARM) which was mounted on a powered wheelchair. The tongue-computer interface was used by two subjects to remotely navigate an ARM carrying wheelchair towards a table and control the ARM to pick up a bottle of water placed on the table. The subjects were given approximately 40 min to become familiar with the system and then they performed the task 10 times. Subject 1 succeeded the task in 9 out of 10 trials and subject 2 in 8 out of 10 trials. The trials lasted for 132±38 s and 195±64 s for subject 1 and 2, respectively. Thus, the system has the potential to further empower individuals with tetraplegia by enabling them to remotely pick-up objects when e.g. lying in bed, making them more independent and reducing the need for care. Future work will include further development of the system (better feedback, semi automation) as well as experimental assessment with more subjects and tasks.