Reviewerly/exHarmony huggingface dataset:
CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

Purpose Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI‐CT registration errors. We developed a deep attention‐based segmentation strategy on CT‐based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. Methods and materials We developed a prostate segmentation strategy which employs an sMRI‐aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. Results The segmentation technique was validated with a clinical study of 49 patients by leave‐one‐out experiments and validated with an additional 50 patients by hold‐out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI‐defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave‐one‐out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold‐out test. Conclusions We have proposed a novel CT‐only prostate segmentation strategy using CT‐based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.
Text mining with sentiment analysis on seafarers’ medical documents

Digital health systems contain large amounts of patient records, doctor notes, and prescriptions in text format. This information summarized over the electronic clinical information will lead to an improved quality of healthcare, the possibility of fewer medical errors, and low costs. Besides, seafarers are more vulnerable to have accidents, and prone to health hazards because of work culture, climatic changes, and personal habits. Therefore, text mining implementation in seafarers’ medical documents can generate better knowledge of medical issues that often happened onboard. Medical records are collected from digital health systems of Centro Internazionale Radio Medico (C.I.R.M.) which is an Italian Telemedical Maritime Assistance System (TMAS). Three years (2018–2020) patient data have been used for analysis. Adoption of both lexicon and Naïve Bayes’ algorithms was done to perform sentimental analysis and experiments were conducted over R statistical tool. Visualization of symptomatic information was done through word clouds and 96% of the correlation between medical problems and diagnosis outcome has been achieved. We validate the sentiment analysis with more than 80% accuracy and precision.
Blockchain technology and trust relationships in trade finance

Blockchain technology has been advocated as a possible solution to enduring trust issues among trading partners in trade finance. We conducted in-depth interviews with industry experts to examine how blockchain technology influences the trust relationships among trading partners. Our results show that the technology enhances trust relationships by (1) improving the security of transactions and data exchanges, (2) facilitating the expression of benevolence, (3) enhancing the efficiency and the quality of communication, and (4) increasing the predictability of trading partners. The paper concludes with implications for both research and practice.
Implementing machine learning in medicine

[See related articles at www.cmaj.ca/lookup/doi/10.1503/cmaj.202066][1] and [www.cmaj.ca/lookup/doi/10.1503/cmaj.210036][2] KEY POINTS Machine learning — the process of developing systems that learn from data to recognize patterns and make accurate predictions of future events[1][3] — has
A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.
Variational Pretraining for Semi-supervised Text Classification

We introduce VAMPIRE, 1 a lightweight pretraining framework for effective text classification when data and computing resources are limited.We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier.Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings.We also find that fine-tuning to indomain data is crucial to achieving decent performance from contextual embeddings when working with limited supervision.We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.
DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition

Hand gesture recognition has attracted the attention of many researchers due to its wide applications in robotics, games, virtual reality, sign language and human-computer interaction. Sign language is a structured form of hand gestures and the most effective communication way among hear-impaired people. Developing an efficient sign language recognition system to recognize dynamic isolated gestures encounters three major challenges, namely, hand segmentation, hand shape feature representation and gesture sequence recognition. Traditional sign language recognition methods utilize color-based hand segmentation algorithms to segment hands, hand-crafted feature extraction for hand shape representation and Hidden Markov Model (HMM) for sequence recognition. In this paper, a novel framework is proposed for signer-independent sign language recognition using multiple deep learning architectures comprising hand semantic segmentation, hand shape feature representation and deep recurrent neural network. The recently developed semantic segmentation method called DeepLabv3+ is trained using a set of pixel-labeled hand images to extract hand regions from each frame of the input video. Then, the extracted hand regions are cropped and scaled to a fixed size to alleviate hand scale variations. Extracting hand shape features is achieved using a single layer Convolutional Self-Organizing Map (CSOM) instead of relying on transfer learning of pre-trained deep convolutional neural networks. The sequence of extracted feature vectors are then recognized using deep Bi-directional Long Short-Term Memory (BiLSTM) recurrent neural network. BiLSTM network contains three BiLSTM layers, one fully connected and softmax layers. The performance of the proposed method is evaluated using a challenging Arabic sign language database containing 23 isolated words captured from three different users. Experimental results show that the performance of proposed framework outperforms with large margin the state-of-the-art methods for signer-independent testing strategy.
Bi-Directional Interaction Network for Person Search

Existing works have designed end-to-end frameworks based on Faster-RCNN for person search. Due to the large receptive fields in deep networks, the feature maps of each proposal, cropped from the stem feature maps, involve redundant context information outside the bounding boxes. However, person search is a fine-grained task which needs accurate appearance information. Such context information can make the model fail to focus on persons, so the learned representations lack the capacity to discriminate various identities. To address this issue, we propose a Siamese network which owns an additional instance-aware branch, named Bi-directional Interaction Network (BINet). During the training phase, in addition to scene images, BINet also takes as inputs person patches which help the model discriminate identities based on human appearance. Moreover, two interaction losses are designed to achieve bi-directional interaction between branches at two levels. The interaction can help the model learn more discriminative features for persons in the scene. At the inference stage, only the major branch is applied, so BINet introduces no additional computation. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our BINet achieves state-of-the-art results among end-to-end methods without loss of efficiency.
Overview of Amazon Web Services

With today’s technological boom, the demand for cloud computing services is increasing day by day. Individuals, companies, and multinational businesses are shifting from self-owned web services to cloud services. Among cloud services providers, the most prominent are Amazon Web Services (AWS), Google Cloud Platform GCP, IBM, and Oracle Cloud.
An efficient colour image encryption scheme based on 1-D chaotic maps

Nowadays, internet is the medium through which people are sharing digital images. However, security is an issue in sharing of those images. Encryption techniques play an important role for the security of images. In this paper, an efficient colour image encryption scheme is proposed using multiple piece-wise linear chaotic map (PWLCM) systems. The proposed scheme first performs two-times multi-way block-based rotational permutation operations and then row-column rotational permutations. Finally, it performs row, column, and block diffusion operations. This scheme is simple and secure because it involves only simple rotational permutation operations using only PWLCM systems. Also, this scheme is efficient because it requires less computations and less simulation time to encrypt colour images. The advantages of this scheme are high key space, high security, simplicity and high efficiency in terms of computational complexity and simulation time. The simulation outputs and the security analyses indicate that the proposed method has better performance in terms of encryption efficiency, security, and resistivity for most of the common attacks.
Usability studies on building early stage architectural models in virtual reality

Despite its marked success in recent years, it is still not clear how Virtual Reality (VR) can assist architects at the early stages of ideation and design. In this paper, we approach VR to build and explore maquettes at different scales in early design stages. To this end we developed a VR environment where user interactions are supported by untethered, easy to operate, peripherals, using a mobile virtual reality headset to provide virtual immersion and simplified geometric information to create voxel-based maquettes. Usability studies with laypeople suggest that the proposed system is both easier to use and more effective [better suited] than current CAD software to rapidly create simplified models. Additionally, tests with architects have shown the system's potential to improve their toolset. This is partly due to VR combining real-time performance with immersive exploration of the content, where body-scale relationships become visible to support the creative process, allowing architects to become both builders and explores of spatial constructs.
Drug dealing on Facebook, Snapchat and Instagram: A qualitative analysis of novel drug markets in the Nordic countries

Abstract Introduction and Aims Internet use has changed drug dealing over the past decade owing to the emergence of darknet services. Yet, little is known about drug dealing in public online services. This study reports findings from a Nordic comparative study on social media drug dealing. It is the first in‐depth study on the increase of digitally mediated drug dealing outside the cryptomarkets. Design and Methods A qualitative study using online ethnography and semi‐structured interviews. One hundred and seven participants aged 16–45 years (mean age 23.1 years), with 83.2% being male, were interviewed. Data was coded in NVivo using general themes: modus operandi, trust and risk. Results Ethnographical data shows a high degree of drug‐dealing activity on Facebook, Instagram, Snapchat and Facebook Messenger. Buyers and sellers also use encrypted platforms, such as darknet forums and the Wickr app on their smartphones. The medium used varies across the countries, as well as motivations for usage in connection with risk perceptions. Discussion Despite national differences, social media is a common tool used in selling and buying illegal drugs. Availability affects the prevalence of use of various social media; however, prevalence is also crucial for which media is used. Many of the participants report easily drifting in and out of social media dealing and buying, without being aware of the seriousness of the offence. Conclusion Based on the differences in attachment to the seller career, we advise that policing strategies should be supplemented with—and even stand in the back of—prevention campaigns.
Implementation Matters in Deep RL: A Case Study on PPO and TRPO

We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms, Proximal Policy Optimization and Trust Region Policy Optimization. We investigate the consequences of code-level optimizations: algorithm augmentations found only in implementations or described as auxiliary details to the core algorithm. Seemingly of secondary importance, such optimizations have a major impact on agent behavior. Our results show that they (a) are responsible for most of PPO's gain in cumulative reward over TRPO, and (b) fundamentally change how RL methods function. These insights show the difficulty, and importance, of attributing performance gains in deep reinforcement learning.
Towards Secure FinTech: A Survey, Taxonomy, and Open Research Challenges

Financial Technology (FinTech) has attracted a wide range of attention and is rapidly proliferating. As a result of its consistent growth new terms have been introduced in this domain. The term `FinTech' is one such terminology. This term is used for describing various operations that are being frequently employed in the financial technology sector. These operations are usually practiced in enterprises or organizations and provide requested services by using Information Technology based applications. The term does take into account various other sensitive issues, like, security, privacy, threats, cyber-attacks, etc. This is important to note that the development of FinTech is indebted to the mutual integration of different state of the art technologies, for example, technologies related to a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing etc. However, this technology is facing several security and privacy issues that are much needed to be addressed in order to improve the acceptability of this new technology among its users. In an effort to secure FinTech, this article provides a comprehensive survey of FinTech by reviewing the most recent as well as anticipated financial industry privacy and security issues. It provides a comprehensive analysis of current security issues, detection mechanisms and security solutions proposed for FinTech. Finally, it discusses future challenges to ensure the security and privacy of financial technology applications.
Interpretability in healthcare: A comparative study of local machine learning interpretability techniques

Abstract Although complex machine learning models (eg, random forest, neural networks) are commonly outperforming the traditional and simple interpretable models (eg, linear regression, decision tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new general data protection regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. Hence, interpretability techniques for machine learning models are an area focus of research. In general, the main aim of these interpretability techniques is to shed light and provide insights into the prediction process of the machine learning models and to be able to explain how the results from the prediction was generated. A major problem in this context is that both the quality of the interpretability techniques and trust of the machine learning model predictions are challenging to measure. In this article, we propose four fundamental quantitative measures for assessing the quality of interpretability techniques— similarity , bias detection , execution time , and trust . We present a comprehensive experimental evaluation of six recent and popular local model agnostic interpretability techniques, namely, LIME , SHAP , Anchors , LORE , ILIME “ and MAPLE on different types of real‐world healthcare data. Building on previous work, our experimental evaluation covers different aspects for its comparison including identity , stability , separability , similarity , execution time , bias detection , and trust . The results of our experiments show that MAPLE achieves the highest performance for the identity across all data sets included in this study, while LIME achieves the lowest performance for the identity metric. LIME achieves the highest performance for the separability metric across all data sets. On average, SHAP has the smallest average time to output explanation across all data sets included in this study. For detecting the bias, SHAP and MAPLE enable the participants to better detect the bias. For the trust metric, Anchors achieves the highest performance on all data sets included in this work.
Learning Dynamic Alignment via Meta-filter for Few-shot Learning

Few-shot learning (FSL), which aims to recognise new classes by adapting the learned knowledge with extremely limited few-shot (support) examples, remains an important open problem in computer vision. Most of the existing methods for feature alignment in few-shot learning only consider image-level or spatial-level alignment while omitting the channel disparity. Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes. Therefore, in this paper, we propose to learn a dynamic alignment, which can effectively highlight both query regions and channels according to different local support information. Specifically, this is achieved by first dynamically sampling the neighbourhood of the feature position conditioned on the input few shot, based on which we further predict a both position-dependent and channel-dependent Dynamic Meta-filter. The filter is used to align the query feature with position-specific and channel-specific knowledge. Moreover, we adopt Neural Ordinary Differential Equation (ODE) to enable a more accurate control of the alignment. In such a sense our model is able to better capture fine-grained semantic context of the few-shot example and thus facilitates dynamical knowledge adaptation for few-shot learning. The resulting framework establishes the new state-of-the-arts on major few-shot visual recognition benchmarks, including miniImageNet and tieredImageNet.
Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Content-based image retrieval (CBIR) is one of the fundamental image retrieval primitives. Its applications can be found in various areas, such as art collections and medical diagnoses. With an increasing prevalence of cloud computing paradigm, image owners desire to outsource their images to cloud servers. In order to deal with the risk of privacy leakage of images, images are typically encrypted before they are outsourced to the cloud, which makes CBIR an extremely challenging task. Existing studies focus on the scenario with only a single image owner, leaving the problem of CBIR with multiple image sources (i.e., owners) unaddressed. In this paper, we propose a secure CBIR scheme that supports Multiple Image owners with Privacy Protection (MIPP). We encrypt image features with a secure multi-party computation technique, which allows image owners to encrypt image features with their own keys. This enables efficient image retrieval over images gathered from multiple sources, while guaranteeing that image privacy of an individual image owner will not be leaked to other image owners. We also propose a new method for similarity measurement of images that can avoid revealing image similarity information to the cloud. Theoretical analysis and experimental results demonstrate that MIPP achieves retrieval accuracy and efficiency simultaneously, while preserving image privacy.