Reviewerly/exHarmony huggingface dataset:
Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data

Aortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in large-scale population-based studies. Using 5100 scans from UK Biobank, this study sought to develop and validate a fully automated method to 1) detect and locate the ROIs of AA and PDA, and 2) provide a quality control mechanism.The automated AA and PDA detection-localization algorithm followed these steps: 1) foreground segmentation; 2) detection of candidate ROIs by Circular Hough Transform (CHT); 3) spatial, histogram and shape feature extraction for candidate ROIs; 4) AA and PDA detection using Random Forest (RF); 5) quality control based on RF detection probability. To provide the ground truth, overall image quality (IQ = 0-3 from poor to good) and aortic locations were visually assessed by 13 observers. The automated algorithm was trained on 1200 scans and Dice Similarity Coefficient (DSC) was used to calculate the agreement between ground truth and automatically detected ROIs.The automated algorithm was tested on 3900 scans. Detection accuracy was 99.4% for AA and 99.8% for PDA. Aorta localization showed excellent agreement with the ground truth, with DSC ≥ 0.9 in 94.8% of AA (DSC = 0.97 ± 0.04) and 99.5% of PDA cases (DSC = 0.98 ± 0.03). AA×PDA detection probabilities could discriminate scans with IQ ≥ 1 from those severely corrupted by artefacts (AUC = 90.6%). If scans with detection probability < 0.75 were excluded (350 scans), the algorithm was able to correctly detect and localize AA and PDA in all the remaining 3550 scans (100% accuracy).The proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.
Experimental study on compressive buckling behavior of J-stiffened composite panels

Fringe projection profilometry was used to study compressive buckling behavior of composite panels stiffened by J-shaped ribs. The basic principles of dynamic four-step phase shifting technique and multi-frequency unwrapping method in the fringe projection profilometry are introduced. These techniques were used to monitor the morphological changes of the composite panel under in-plane compression. At the same time, strain gauges were utilized to monitor strain history at each point on the surface of the panel. Optical measurement shows the full-field buckling deflection and buckling mode at different moments, and it produces richer data than strain gauges. The experimental results show that the local buckling increases with the loading until the ribs are unstable, which is verified by the finite element calculations.
An Agile Methodology for Ontology Development

Ontology is defined as an explicit specification of a shared conceptualization.Currently, ontology is used in semantic web, information retrieval, artificial intelligence, information systems, knowledge management, etc.The development of ontology involves a structural and logical complexity that is comparable to the development of software artifacts.Therefore, ontology building requires a methodology to ensure its reliability.In this context, there are several methodologies proposed for building ontologies.However, most of the existing methodologies failed to provide sufficient details for the activities and techniques employed in them with a defined ontology lifecycle.To build ontologies that are reliable, long lived and continually adapted, the ontology engineering (OE) should be supported by the software engineering (OE).But, SE was not initially meant to support the development of software artifacts such as ontologies.There is a significant gap between them in terms of popularity and maturity level.The aim of this paper is to bridge this gap by proposing an Agile Methodology for Ontology Development (AMOD).AMOD adopts the agile principles and practices in the ontology development.The final framework of AMOD fits the various ontology activities into the phases of the Scrum agile methodology.It has three phases: pre-game, development and post-game.AMOD was applied to develop ontology for software project time management.Additionally, a compliance analysis of different ontology methodologies with respect to the IEEE Standard was made.Results showed that AMOD resulted in 56% satisfaction for IEEE standard processes.This resembles 22% enhancement in the satisfaction against the other methodologies.
Automatic ultrasound image segmentation based on local entropy and active contour model

It is still a challenging task to segment ultrasound medical images automatically and accurately because of the poor quality of images. To address these problems, a two-stage automatic segmentation scheme based on local entropy and a proposed active contour model for ultrasound images are put forward in this paper. First, a new region-based active contour model in the level set formulation, driven by global and local intensity information, is established for the segmentation. Furthermore, for automatically segmenting the ultrasound images, a coarse segmentation is performed utilizing local entropy information of the ultrasound images, then the coarse segmentation result is used as the initial value of the explicit segmentation based on the proposed model. Several experiments on real ultrasound images demonstrate that the proposed method outperforms other methods on both visual perception and objective evaluation metrics.
Deeply self-supervised contour embedded neural network applied to liver segmentation

Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.
Self Residual Attention Network for Deep Face Recognition

Discriminative feature embedding is of essential importance in the field of large scale face recognition. In this paper, we propose a self residual attention-based convolutional neural network (SRANet) for discriminative face feature embedding, which aims to learn the long-range dependencies of face images by decreasing the information redundancy among channels and focusing on the most informative components of spatial feature maps. More specifically, the proposed attention module consists of the self channel attention (SCA) block and self spatial attention (SSA) block which adaptively aggregates the feature maps in both channel and spatial domains to learn the inter-channel relationship matrix and the inter-spatial relationship matrix; moreover, matrix multiplications are conducted for a refined and robust face feature. With the attention module we proposed, we can make standard convolutional neural networks (CNNs), such as ResNet-50 and ResNet-101, which have more discriminative power for deep face recognition. The experiments on Labelled Faces in the Wild (LFW), Age Database (AgeDB), Celebrities in Frontal Profile (CFP), and MegaFace Challenge 1 (MF1) show that our proposed SRANet structure consistently outperforms naive CNNs and achieves state-of-the-art performance.
Object Tracking for a Smart City Using IoT and Edge Computing

As the Internet-of-Things (IoT) and edge computing have been major paradigms for distributed data collection, communication, and processing, smart city applications in the real world tend to adopt IoT and edge computing broadly. Today, more and more machine learning algorithms would be deployed into front-end sensors, devices, and edge data centres rather than centralised cloud data centres. However, front-end sensors and devices are usually not so capable as those computing units in huge data centres, and for this sake, in practice, engineers choose to compromise for limited capacity of embedded computing and limited memory, e.g., neural network models being pruned to fit embedded devices. Visual object tracking is one of many important elements of a smart city, and in the IoT and edge computing context, high requirements to computing power and memory space severely prevent massive and accurate tracking. In this paper, we report on our contribution to object tracking on lightweight computing including (1) using limited computing capacity and memory space to realise tracking; (2) proposing a new algorithm region proposal correlation filter fitting for most edge devices. Systematic evaluations show that (1) our techniques can fit most IoT devices; (2) our techniques can keep relatively high accuracy; and (3) the generated model size is much less than others.
SANA: Sentiment analysis on newspapers comments in Algeria

It is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who’s the purpose is finding the behind phenomenon masked in opinionated texts. We are interested in our work by comments in Algerian newspaper websites. For this end, two corpora were used; SANA and OCA. SANA corpus is created by collection of comments from three Algerian newspapers, and annotated by two Algerian Arabic native speakers, while OCA is a freely available corpus for sentiment analysis. For the classification we adopt Supports vector machines, naïve Bayes and k-nearest neighbors. Obtained results are very promising and show the different effects of stemming in such domain, also k-nearest neighbors gives important improvement comparing to other classifiers unlike similar works where SVM is the most dominant. From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works.
Blockchain Token Economics: A Mean-Field-Type Game Perspective

This paper studies the blockchain cryptographic tokens by means of mean-field-type game theory.It introduces the variance-aware utility function per decision-maker to capture the risk of cryptographic tokens associated with the uncertainties of technology adoption, network security, regulatory legislation, and market volatility.We establish a relationship between the network characteristics, token price, number of token holders, and token supply.Both in-chain diversification and cross-chain diversification among tokens are examined by using a mean-variance approach.The results suggest that the number of tokens in circulation needs to be adjusted in order to capture risk-awareness and self-regulatory behavior in blockchain token economics.The Sharpe and Modigliani ratios for cryptographic tokens are revisited.
Adversarial Machine Learning Beyond the Image Domain

Machine learning systems have had enormous success in a wide range of fields from computer vision, natural language processing, and anomaly detection. However, such systems are vulnerable to attackers who can cause deliberate misclassification by introducing small perturbations. With machine learning systems being proposed for cyber attack detection such attackers are cause for serious concern. Despite this the vast majority of adversarial machine learning security research is focused on the image domain. This work gives a brief overview of adversarial machine learning and machine learning used in cyber attack detection and suggests key differences between the traditional image domain of adversarial machine learning and the cyber domain. Finally we show an adversarial machine learning attack on an industrial control system.
Secure Encrypted Data With Authorized Deduplication in Cloud

In this paper, we propose a novel secure role re-encryption system (SRRS), which is based on convergent encryption and the role re-encryption algorithm to prevent the privacy data leakage in cloud and it also achieves the authorized deduplication and satisfies the dynamic privilege updating and revoking.Meanwhile, our system supports ownership checking and achieves the proof of ownership for the authorized users efficiently.Specifically, we introduce a management center to handle with the authorized request and establish a role authorized tree (RAT) mapping the relationship of the roles and keys.With the convergent encryption algorithm and the role re-encryption technique, it can be guaranteed that only the authorized user who has the corresponding role re-encryption key can access the specific file without any data leakage.Through role re-encryption key updating and revoking, our system achieves the dynamic updating of the authorized user's privilege.Furthermore, we exploit the dynamic count filters (DCF) to implement the data updating and improve the retrieval of ownership verifying effectively.We conduct the security analysis and the simulation experiment to demonstrate the security and efficiency of our proposed system.
MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors

Modern convolutional object detectors have improved the detection accuracy significantly, which in turn inspired the development of dedicated hardware accelerators to achieve real-time performance by exploiting inherent parallelism in the algorithm. Non-maximum suppression (NMS) is an indispensable operation in object detection. In stark contrast to most operations, the commonly-adopted GreedyNMS algorithm does not foster parallelism, which can be a major performance bottleneck. In this paper, we introduce MaxpoolNMS, a parallelizable alternative to the NMS algorithm, which is based on max-pooling classification score maps. By employing a novel multi-scale multi-channel max-pooling strategy, our method is 20x faster than GreedyNMS while simultaneously achieves comparable accuracy, when quantified across various benchmarking datasets, i.e., MS COCO, KITTI and PASCAL VOC. Furthermore, our method is better suited for hardware-based acceleration than GreedyNMS.
The Video Authentication and Camera Identification Database: A New Database for Video Forensics

Modern technologies have made the capture and sharing of digital video commonplace; the combination of modern smartphones, cloud storage, and social media platforms have enabled video to become a primary source of information for many people and institutions. As a result, it is important to be able to verify the authenticity and source of this information, including identifying the source camera model that captured it. While a variety of forensic techniques have been developed for digital images, less research has been conducted toward the forensic analysis of videos. In part, this is due to a lack of standard digital video databases, which are necessary to develop and evaluate state-of-the-art video forensic algorithms. In this paper, to address this need, we present the video authentication and camera identification (video-ACID) database, a large collection of videos specifically collected for the development of camera model identification algorithms. The video-ACID database contains over 12 000 videos from 46 physical devices representing 36 unique camera models. Videos in this database are hand collected in a diversity of real-world scenarios are unedited and have known and trusted provenance. In this paper, we describe the qualities, structure, and collection procedure of video-ACID, which includes clearly marked videos for evaluating camera model identification algorithms. Finally, we provide baseline camera model identification results on these evaluation videos using the state-of-the-art deep-learning techniques. The Video-ACID database is publicly available at misl.ece.drexel.edu/video-acid.
The evaluation of reanalysis and analysis products of solar radiation for Sindh province, Pakistan

The quality of the surface solar irradiance (SSI) data from three numerical meteorological reanalysis products (NCEP-NCAR, NCEP-DOE and JRA-55) and two analysis products (NCEP-FNL and NCEP-GFS) are analysed by comparing with in-situ high-quality SSI measurement. The validation of estimates of SSI against measured data is done based on mean bias error (MBE), root mean squared error (RMSE), relative MBE, relative RMSE and coefficient of determination (R2). The rMBE, rRMSE and R2 for five estimated SSI datasets for both stations range from −10.5–28.0%, 19.2–41.4% and 0.870 to 0.969 respectively. The measured clearness index shows that the cloud fraction is not accurately incorporated while estimating SSI from datasets, which is the main reason for errors, especially higher errors in the summer season because of Monsoon. The NCEP-FNL and NCEP-GFS predict cloudy conditions where the actual conditions are clear-sky, whereas the NCEP-NCAR predicts clear-sky conditions where the actual conditions are cloudy. The overestimation of clear-sky conditions leads to the overestimation of the SSI and the clearness index and vice versa. The overall results show that the estimates from NCEP-NCAR are the worst among all the datasets whereas NCEP-FNL and NCEP-DOE predict well for Karachi and Hyderabad respectively.
Adaptation of Convolution and Batch Normalization Layer for CNN Implementation on FPGA

The article presents integration process of convolution and batch normalization layer for further implementation on FPGA. The convolution kernel is binarized and merged with batch normalization into a core and implemented on single DSP. The concept is proven on custom binarized convolutional neural network (CNN) that is trained in Matlab to solve object localization task. 16 b precision gives 1.3 % error on the output of joined convolution and batch normalization core. The localization accuracy decreases in average by 7 % from 74 % to 67 %, and it is still tolerable in embedded systems applications.
Generation of Policy-Level Explanations for Reinforcement Learning

Though reinforcement learning has greatly benefited from the incorporation of neural networks, the inability to verify the correctness of such systems limits their use. Current work in explainable deep learning focuses on explaining only a single decision in terms of input features, making it unsuitable for explaining a sequence of decisions. To address this need, we introduce Abstracted Policy Graphs, which are Markov chains of abstract states. This representation concisely summarizes a policy so that individual decisions can be explained in the context of expected future transitions. Additionally, we propose a method to generate these Abstracted Policy Graphs for deterministic policies given a learned value function and a set of observed transitions, potentially off-policy transitions used during training. Since no restrictions are placed on how the value function is generated, our method is compatible with many existing reinforcement learning methods. We prove that the worst-case time complexity of our method is quadratic in the number of features and linear in the number of provided transitions, O(|F|2|tr samples|). By applying our method to a family of domains, we show that our method scales well in practice and produces Abstracted Policy Graphs which reliably capture relationships within these domains.
Deep Neural Representation Guided Face Sketch Synthesis

Face sketch synthesis shows great applications in a lot of fields such as online entertainment and suspects identification. Existing face sketch synthesis methods learn the patch-wise sketch style from the training dataset containing photo-sketch pairs. These methods manipulate the whole process directly in the field of RGB space, which unavoidably results in unsmooth noises at patch boundaries. If denoising methods are used, the sketch edges would be blurred and face structures could not be restored. Recent researches of feature maps, which are the outputs of a certain neural network layer, have achieved great success in texture synthesis and artistic image generation. In this paper, we reformulate the face sketch synthesis problem into a neural network feature maps based optimization task. Our results accurately capture the sketch drawing style and make full use of the whole stylistic information hidden in the training dataset. Unlike former feature map based methods, we utilize the Enhanced 3D PatchMatch and cross-layer cost aggregation methods to obtain the target feature maps for the final results. Multiple experiments have shown that our approach imitates hand-drawn sketch style vividly, and has high-quality visual effects on CUHK, AR, XM2VTS and CUFSF face sketch datasets.
Secure blockchain in the enterprise: A methodology

The adoption of blockchain in a software architecture should be perceived as a double-edged sword: if on the one hand, it allows to achieve benefits in terms of auditability, strong integrity protection and virtual tokens management, on the other hand they come together with peculiarities, like immutability, that need to be properly considered; for example, in the interaction with personal data, blockchain adoption may result particularly critical. Therefore, in order to guide software architects wishing to leverage the potential of an existing blockchain technology for their solutions, we propose a list of security threats specific to blockchain, integrated in a multi-staged architecture analysis approach based on threat modeling. It comprises methods for the evaluation and identification of the most suitable blockchain technology (if at all viable) for the scenario, coupled with design analysis processes which consider web application — as well as our blockchain-specific security threats. We illustrate our approach through a paradigmatic example.
Secure Mobile Computing Authentication Utilizing Hash, Cryptography and Steganography Combination

In this paper, we propose a modification for security authentication systems in mobile devices. Our enhancement is designed to secure information transformation over the internet by combining hash, cryptography and steganography mechanisms. We used the combination for authentication, to secure mobile computing to transfer data in a trusted manner. The proposed work will use hash function to store the secret password to provide increased security. The hashed password is encrypted using AES encryption then hidden inside an image to be called back for authentication whenever needed. The security services provided by this combination mechanisms can assure authenticity, confidentiality, and integrity. Results and comparisons to different options of available mobile computing methods proved that our proposed technique is a promising research direction for real mobile security.