Reviewerly/exHarmony huggingface dataset:
Residual Convolutional Neural Network for Cardiac Image Segmentation and Heart Disease Diagnosis

Deep learning (DL) has been widely used in biomedical image segmentation and automatic disease diagnosis, leading to state-of-the-art performance. However, automated cardiac disease diagnosis heavily relies on cardiac segmentation maps from cardiac magnetic resonance (CMR), most current DL segmentation methods, such as 2D convolution on planes, 3D convolution, are not fully applicable to CMR due to loss of spatial structure information or large gap between slices. To make better exploit spatial aspects of the CMR data to improve cardiac segmentation accuracy, we propose a new DL segmentation structure, which consists of a residual convolution neural network for compressing the intra-slice information, and a bidirectional-convolutional long short term memory (Bi-CLSTM) for leveraging the inter-slice contexts. Moreover, automatic disease diagnosis has been conducted using the segmentation maps. Experimental results of the automatic cardiac diagnosis challenge (ACDC) show that our cardiac segmentation structure and disease diagnosis methods have achieved promising results and it can be widely extended to computer-aided diagnosis.
End-to-End Learning for Omnidirectional Stereo Matching With Uncertainty Prior

In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with ultra-wide field-of-view cameras on an omnidirectional rig are processed by the feature extraction module, and then the deep feature maps are warped onto the concentric spheres swept through all candidate depths using the calibrated camera parameters. The 3D encoder-decoder block takes the aligned feature volume to produce an omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information. For more accurate depth estimation we also propose an uncertainty prior guidance in two ways: depth map filtering and guiding regularization. In addition, we present large-scale synthetic datasets for training and testing omnidirectional multi-view stereo algorithms. Our datasets consist of 13K ground-truth depth maps and 53K fisheye images in four orthogonal directions with various objects and environments. Experimental results show that the proposed method generates excellent results in both synthetic and real-world environments, and it outperforms the prior art and the omnidirectional versions of the state-of-the-art conventional stereo algorithms.
A New Approach for Security in Cloud Data Storage for IOT Applications Using Hybrid Cryptography Technique

People store their data on cloud storage very commonly now a day. Security is a major issue in storing data on clouds. Cryptography techniques are very useful to impose security on data. In this paper a hybrid cryptography system is proposed to provide better security on the data which is stored on cloud storage. The proposed approach use RSA algorithm and DES algorithm and provide a hybrid of the two algorithms to provide more security on the data before storing it on cloud. The proposed algorithm is implemented in JAVA and test on a sample plain text. The paper will be very useful for IOT applications storing data on cloud. It is verified that the proposed algorithm is working well to provide more security on data.
An asymmetric knowledge representation learning in manifold space

The knowledge graph is constructed to acquire a large amount of structured knowledge that can be read by computers. To effectively complete the knowledge graph, embedding representation models that encode entities and relations in real number tensor space are proposed, such as the TransE model and its extension models, namely, the flexible translation model (FT), dynamic translation model (DT) and manifold-based representation model (OrbitE). These above baselines are proposed to alleviate the problem of inaccurate triple representation in complex relations and have achieved good results. However, with the improvement of the effect, the number of parameters increases. Therefore, an asymmetric knowledge representation learning model in manifold space (MAKR) is proposed in this paper. The position of the golden triple is expanded to the manifold based on the OrbitE model. Then, the embedded representations of the head and tail entities are separately weighted by the corresponding different embedded relations, which are the same manifold space in the same triples instead of the same points. The MAKR alleviates the asymmetry and imbalance of relations and the unsatisfactory precise prediction. Moreover, the time and space complexities of the proposed MAKR are low. Finally, the effectiveness of the MAKR is verified by four datasets: FB15K, WN18, FB13 and WN11. Compared to other baselines, the MAKR has achieved better performance in both triple classification and link prediction tasks.
Evaluation of Earthquake Magnitude Estimation and Event Detection Thresholds for Real-Time GNSS Networks: Examples from Recent Events Captured by the Network of the Americas

Abstract Several studies have shown that real-time (RT) Global Navigation Satellite Systems (GNSS) measurements can provide an estimate of an earthquake’s moment magnitude (Mw) using scaling laws that relate peak ground displacements (PGDs) and hypocentral distance with Mw. In this study, we use data from GNSS stations operated by UNAVCO as part of the National Science Foundation Geodetic Facility for the Advancement of Geoscience (GAGE) that comprises the Network of the Americas (NOTA) to show that precise point positioning (PPP) solutions distributed in RT during five recent earthquakes could be used to calculate Mw rapidly and reliably. We analyze solutions distributed by UNAVCO during the 8 September 2017 Mw 8.2 Tehuantepec, Mexico, 10 January 2018 Mw 7.5 Great Swan Island, Honduras, 23 January 2018 Mw 7.9 Gulf of Alaska earthquake, and the 4 July 2019 Mw 6.4 and 6 July 2019 Mw 7.1 Ridgecrest, California, earthquakes. We find that RT-GNSS Mw estimates consistent with Advanced National Seismic System Comprehensive Earthquake Catalog values are available tens of seconds to a few minutes after the event origin time. The speed with which an estimate is available is dependent on the proximity to the epicenter of the closest NOTA stations. The results demonstrate that RT-GNSS networks could be used to mitigate the problem of magnitude saturation observed in seismic-based earthquake early warning (EEW) systems. RT-GNSS effectively expands the spectrum of events for which a seismic EEW system can provide accurate warnings of impending ground shaking and provides an independent verification of seismically derived magnitudes. We also analyze the RT-PPP solutions from more than 800 RT-GNSS stations to determine the ambient-noise levels of each NOTA station and combine that with PGD magnitude scaling laws to construct regional network sensitivity maps for the NOTA network. Such maps may be used to determine “blind spots” or regions of lower sensitivity in RT-GNSS networks under consideration for EEW.
Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning

While convolutional neural networks (CNNs) have achieved remarkable performance on various supervised and unsupervised learning tasks, they typically consist of a massive number of parameters. This results in significant memory requirements as well as a computational burden. Consequently, there is a growing need for filter-level pruning approaches for compressing CNN based models that not only reduce the total number of parameters but reduce the overall computation as well. We present a new min-max framework for the filter-level pruning of CNNs. Our framework jointly prunes and fine-tunes CNN model parameters, with an adaptive pruning rate, while maintaining the model's predictive performance. Our framework consists of two modules: (1) An adaptive filter pruning (AFP) module, which minimizes the number of filters in the model; and (2) A pruning rate controller (PRC) module, which maximizes the accuracy during pruning. In addition, we also introduce orthogonality regularization in training of CNNs to reduce redundancy across filters of a particular layer. In the proposed approach, we prune the least important filters and, at the same time, reduce the redundancy level in the model by using orthogonality constraints during training. Moreover, unlike most previous approaches, our approach allows directly specifying the desired error tolerance instead of the pruning level. We perform extensive experiments for object classification (LeNet, VGG, MobileNet, and ResNet) and object detection (SSD, and Faster-RCNN) over benchmarked datasets such as MNIST, CIFAR, GTSDB, ImageNet, and MS-COCO. We also present several ablation studies to validate the proposed approach. Our compressed models can be deployed at run-time, without requiring any special libraries or hardware. Our approach reduces the number of parameters of VGG-16 by an impressive factor of 17.5X, and the number of FLOPS by 6.43X, with no loss of accuracy, significantly outperforming other state-of-the-art filter pruning methods.
A Constant Time Complexity Spam Detection Algorithm for Boosting Throughput on Rule-Based Filtering Systems

Along with the barbarous growth of spams, anti-spam technologies including rule-based approaches and machine-learning thrive rapidly as well.In antispam industry, the rule-based systems (RBS) becomes the most prominent methods for fighting spam due to its capability to enrich and update rules remotely.However, the antispam filtering throughput is always a great challenge of RBS.Especially, the explosively spreading of obfuscated words leads to frequent rule update and extensive rule vocabulary expansion.These incremental obfuscated words make the filtering speed slow down and the throughput decrease.This paper addresses the challenging throughput issue and proposes a constant time complexity rule-based spam detection algorithm.The algorithm has a constant processing speed, which is independent of rule and its vocabulary size.A new special data structure, namely, Hash Forest, and a rule encoding method are developed to make constant time complexity possible.Instead of traversing each spam term in rules, the proposed algorithm manages to detect spam terms by checking a very small portion of all terms.The experiment results show effectiveness of proposed algorithm.
A Pre-Large Weighted-Fusion System of Sensed High-Utility Patterns

Within the current transportation infrastructure, we have seen a steady increase in the use of sensor technologies. These sensors, individually produce large amounts of data that then need to be fused and understood. Data commingling and data integration are difficult tasks when it comes to processing such data centrally, which can require costly hardware and software techniques. Over the past few years, high-utility pattern mining (HUPM) has gained popularity due to its growing capability in identifying useful information and knowledge from stored database data, as compared to the traditional frequent pattern mining. Existing works of HUPM mostly focus on mining the set of HUPs from one data source, which cannot be implemented in real-world scenarios. In this paper, we present a pre-large weighted high-utility pattern (PWHUP) fusion framework for integrating HUPs from different sensed data sources. The proposed PWHUP algorithm considers the size of the data source for discovering more relevant HUPs for integration, which is more applicable to real-life applications and scenarios in transportation and also within other data fusion scenarios. Moreover, the pre-large concept is applied to maintain the suggested pattern for later integration, which greatly improves the effectiveness of the proposed algorithm. Our in-depth experiments show that the designed approach has good performance for knowledge integration and outperforms existing non-integration solutions in precision, recall, and runtime.
Information security awareness in a developing country context: insights from the government sector in Saudi Arabia

Purpose The purpose of this paper is to increase understanding of employee information security awareness in a government sector setting and illuminate the problems that public sector organisations in a developing context face when seeking to establish an information security awareness programme. Design/methodology/approach An interpretive research design was followed to develop an empirically enriched understanding of information security awareness perceptions, aspirations, challenges and enablers in the context of Saudi Arabia as a developing country. The study adopts a single-case study approach, including face-to-face interviews with senior employees, as well as document analysis. Findings The paper theorises the importance of individual information security awareness, knowledge and behaviour and identifies a number of facilitating conditions: customisation to employee and organisational needs, interactivity, innovation, frequency, integration of both electronic and physical learning resources and rewarding the acquisition of in-depth security-related actionable knowledge. Originality/value This study is one of the first to examine information security awareness as a socio-technical process within a government sector organisation in a developing country context.
Building Area Estimation in Drone Aerial Images Based on Mask R-CNN

In rural areas where disasters occur frequently, the calculation of building areas is crucial in property assessment. In the segmentation algorithm, Mask R-CNN can distinguish the adjacent objects and extract the outline of an object. Based on this observation, we propose a novel method to calculate the building areas based on Mask R-CNN and adopt the concept of transfer learning to train our model, which can achieve good results with a small number of drone aerial images as training samples. The proposed method involves three main steps: 1) pretraining using open-source satellite remote sensing images; 2) fine-tuning with a small number of drone aerial images; and 3) testing with new images and area calculation based on the number of building pixels. The experiments show that the proposed method can achieve good results in terms of F1 score and intersection over union.
Fuzzy Learning for Multi-Dimensional Adaptive Physical Layer Authentication: A Compact and Robust Approach

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.
Filter Distillation for Network Compression

In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between filter responses within network layers to recommend a smaller network that maintain as much as possible the accuracy of the full model. We propose two algorithms: the first allows users to target compression to specific network property, such as number of trainable variable (footprint), and produces a compressed model that satisfies the requested property while preserving the maximum amount of spectral energy in the responses of each layer, while the second is a parameter-free heuristic that selects the compression used at each layer by trying to mimic an ideal set of uncorrelated responses. Since PFA compresses networks based on the correlation of their responses we show in our experiments that it gains the additional flexibility of adapting each architecture to a specific domain while compressing. PFA is evaluated against several architectures and datasets, and shows considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8×, 3×, and 1.4× with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. Our tests show that PFA is competitive with state-of-the-art approaches while removing adoption barriers thanks to its practical implementation, intuitive philosophy and ease of use.
Automatic Detection of Cybersickness from Physiological Signal in a Virtual Roller Coaster Simulation

Virtual reality (VR) systems often induce motion sickness like discomfort known as cybersickness. The standard approach for detecting cybersickness includes collecting both subjective and objective measurements, while participants are exposed to VR. With the recent advancement of machine learning, we can train deep neural networks to detect cybersickness severity from subjective (e.g., self-reported sickness periodically) and objective measurements. In this study, we collected physiological data from 31 participants while they were immersed in VR. Self-reported verbal sickness was collected at each minute interval for labeling the physiological data. Finally, a simple neural network was proposed to detect cybersickness severity.
An automatic software vulnerability classification framework using term frequency-inverse gravity moment and feature selection

Vulnerability classification is an important activity in software development and software quality maintenance. A typical vulnerability classification model usually involves a stage of term selection, in which the relevant terms are identified via feature selection. It also involves a stage of term-weighting, in which the document weights for the selected terms are computed, and a stage for classifier learning. Generally, the term frequency-inverse document frequency (TF-IDF) model is the most widely used term-weighting metric for vulnerability classification. However, several issues hinder the effectiveness of the TF-IDF model for document classification. To address this problem, we propose and evaluate a general framework for vulnerability severity classification using the term frequency-inverse gravity moment (TF-IGM). Specifically, we extensively compare the term frequency-inverse gravity moment, term frequency-inverse document frequency, and information gain feature selection using five machine learning algorithms on ten vulnerable software applications containing a total number of 27,248 security vulnerabilities. The experimental result shows that: (i) the TF-IGM model is a promising term weighting metric for vulnerability classification compared to the classical term-weighting metric, (ii) the effectiveness of feature selection on vulnerability classification varies significantly across the studied datasets and (iii) feature selection improves vulnerability classification.
Lightweight Proof of Game (LPoG): A Proof of Work (PoW)’s Extended Lightweight Consensus Algorithm for Wearable Kidneys

In healthcare, interoperability is widely adopted in the case of cross-departmental or specialization cases. As the human body demands multiple specialized and cross-disciplined medical experiments, interoperability of business entities like different departments, different specializations, the involvement of legal and government monitoring issues etc. are not sufficient to reduce the active medical cases. A patient-centric system with high capability to collect, retrieve, store or exchange data is the demand for present and future times. Such data-centric health processes would bring automated patient medication, or patient self-driven trusted and high satisfaction capabilities. However, data-centric processes are having a huge set of challenges such as security, technology, governance, adoption, deployment, integration etc. This work has explored the feasibility to integrate resource-constrained devices-based wearable kidney systems in the Industry 4.0 network and facilitates data collection, liquidity, storage, retrieval and exchange systems. Thereafter, a Healthcare 4.0 processes-based wearable kidney system is proposed that is having the blockchain technology advantages. Further, game theory-based consensus algorithms are proposed for resource-constrained devices in the kidney system. The overall system design would bring an example for the transition from the specialization or departmental-centric approach to data and patient-centric approach that would bring more transparency, trust and healthy practices in the healthcare sector. Results show a variation of 0.10 million GH/s to 0.18 million GH/s hash rate for the proposed approach. The chances of a majority attack in the proposed scheme are statistically proved to be minimum. Further Average Packet Delivery Rate (ADPR) lies between 95% to 97%, approximately, without the presence of outliers. In the presence of outliers, network performance decreases below 80% APDR (to a minimum of 41.3%) and this indicates that there are outliers present in the network. Simulation results show that the Average Throughput (AT) value lies between 120 Kbps to 250 Kbps.
A multi-kernel method of measuring adaptive similarity for spectral clustering

Accurate representation of similarities between data points is an important determinant in the success of many clustering approaches. Previous studies have shown that kernel methods are effective in this representation. However, using multi-kernels to merge probabilistic neighborhoods in data still needs further research. In this paper, a multi-kernel method of measuring adaptive similarity for spectral clustering is proposed. Kernels with more accurate adaptive similarity measure on the data are assigned bigger weights and an optimum combined kernel that truly reflects the internal structure of the data points is obtained. The proposed method ascribes adaptive and optimal neighbors to each data point based on the local structure using the combined kernels. The combined similarity measure and data clustering are learnt simultaneously to obtain optimal clustering results. We rank constraint the Laplacian matrix of the data similarity matrix to ensure that the connected components in the similarity matrix are exactly equal to the cluster number. The presented technique is significant in the sense that it is able to search the underlying similarity relationships amongst data points and is robust to complex data. Compared with other state-of-the-art spectral clustering methods, our proposed method achieves better performance in terms of NMI and accuracy in experiments performed on both synthetic and real datasets.