Reviewerly/exHarmony huggingface dataset:
Enhancing Privacy and Data Security across Healthcare Applications Using Blockchain and Distributed Ledger Concepts

Nowadays, blockchain is developing as a secure and trustworthy platform for secure information sharing in areas of application like banking, supply chain management, food industry, energy, the Internet, and medical services. Besides, the blockchain can be described in a decentralized manner as an immutable ledger for recording data entries. Furthermore, this new technology has been developed to interrupt a variety of data-driven fields, including the health sector. However, blockchain refers to the distributed ledger technology, which constitutes an innovation in the information recording and sharing without a trusted third party. In this paper, blockchain and Distributed Ledger-based Improved Biomedical Security system (BDL-IBS) has been proposed to enhance the privacy and data security across healthcare applications. Further, our goal is to make it possible for patients to use the data to support their care and to provide strong consent systems for sharing data among different organizations and applications, since this includes managing and accessing a high amount of medical information, and this technology can maintain data to ensure reliability. Finally, results show that new blockchain-based digital platforms allow for fast, easy, and seamless interactions between data suppliers to enhance privacy and data security, including for patients themselves.
Truncation Cross Entropy Loss for Remote Sensing Image Captioning

Recently, remote sensing image captioning (RSIC) has drawn an increasing attention. In this field, the encoder-decoder-based methods have become the mainstream due to their excellent performance. In the encoder-decoder framework, the convolutional neural network (CNN) is used to encode a remote sensing image into a semantic feature vector, and a sequence model such as long short-term memory (LSTM) is subsequently adopted to generate a content-related caption based on the feature vector. During the traditional training stage, the probability of the target word at each time step is forcibly optimized to 1 by the cross entropy (CE) loss. However, because of the variability and ambiguity of possible image captions, the target word could be replaced by other words like its synonyms, and therefore, such an optimization strategy would result in the overfitting of the network. In this article, we explore the overfitting phenomenon in the RSIC caused by CE loss and correspondingly propose a new truncation cross entropy (TCE) loss, aiming to alleviate the overfitting problem. In order to verify the effectiveness of the proposed approach, extensive comparison experiments are performed on three public RSIC data sets, including UCM-captions, Sydney-captions, and RSICD. The state-of-the-art result of Sydney-captions and RSICD and the competitive results of UCM-captions achieved by TCE loss demonstrate that the proposed method is beneficial to RSIC.
Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading channels. Specifically, a Channel Estimation Network (CENet) is designed to replace the conventional interpolation procedure in pilot-aided estimation scheme. Then, based on the outcome of the CENet, a Channel Conditioned Recovery Network (CCRNet) is designed to recover the transmit signal. Experimental results demonstrate that CENet and CCRNet achieve superior performance compared with conventional estimation and detection methods. In addition, both networks are shown to be robust to the variation of parameter chances, which makes them appealing for practical implementation.
DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Discriminative Multi-Scale Deep Features

Albeit great success has been achieved in image defocus blur detection, there are still several unsolved challenges, e.g., interference of background clutter, scale sensitivity and missing boundary details of blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We first fuse the features from different layers of FCN as shallow features and semantic features, respectively. Then, the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions, and the fused semantic features are propagated to shallow layers to assist in better locating blur regions. The fusion and refinement are carried out recurrently. In order to narrow the gap between low-level and high-level features, we embed a feature adaptation module before feature propagating to exploit the complementary information as well as reduce the contradictory response of different feature layers. Since different feature channels are with different extents of discrimination for detecting blur regions, we design a channel attention module to select discriminative features for feature refinement. Finally, the output of each layer at last recurrent step are fused to obtain the final result. We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study. Extensive experiments on two commonly used datasets and our newly collected one are conducted to demonstrate both the efficacy and efficiency of DeFusionNet.
Orientation-Aware Vehicle Detection in Aerial Images via an Anchor-Free Object Detection Approach

Vehicle detection in aerial images is an important and challenging task in the field of remote sensing. Recently, deep learning technologies have yielded superior performance for object detection in remote sensing images. However, the detection results of the existing methods are horizontal bounding boxes that ignore vehicle orientations, thereby having limited applicability in scenes with dense vehicles or clutter backgrounds. In this article, we propose a one-stage, anchor-free detection approach to detect arbitrarily oriented vehicles in high-resolution aerial images. The vehicle detection task is transformed into a multitask learning problem by directly predicting high-level vehicle features via a fully convolutional network. That is, a classification subtask is created to look for vehicle central points and three regression subtasks are created to predict vehicle orientations, scales, and offsets of vehicle central points. First, coarse and fine feature maps outputted from different stages of a residual network are concatenated together by a feature pyramid fusion strategy. Upon the concatenated features, four convolutional layers are attached in parallel to predict high-level vehicle features. During training, task uncertainty learned from the training data is used to weight loss function in the multitask learning setting. For inferencing, oriented bounding boxes are generated using the predicted vehicle features, and oriented nonmaximum suppression (NMS) postprocessing is used to reduce redundant results. Experiments on two public aerial image data sets have shown the effectiveness of the proposed approach.
Blockchain Technology and Neural Networks for the Internet of Medical Things

In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
One shot 3D photography

3D photography is a new medium that allows viewers to more fully experience a captured moment. In this work, we refer to a 3D photo as one that displays parallax induced by moving the viewpoint (as opposed to a stereo pair with a fixed viewpoint). 3D photos are static in time, like traditional photos, but are displayed with interactive parallax on mobile or desktop screens, as well as on Virtual Reality devices, where viewing it also includes stereo. We present an end-to-end system for creating and viewing 3D photos, and the algorithmic and design choices therein. Our 3D photos are captured in a single shot and processed directly on a mobile device. The method starts by estimating depth from the 2D input image using a new monocular depth estimation network that is optimized for mobile devices. It performs competitively to the state-of-the-art, but has lower latency and peak memory consumption and uses an order of magnitude fewer parameters. The resulting depth is lifted to a layered depth image, and new geometry is synthesized in parallax regions. We synthesize color texture and structures in the parallax regions as well, using an inpainting network, also optimized for mobile devices, on the LDI directly. Finally, we convert the result into a mesh-based representation that can be efficiently transmitted and rendered even on low-end devices and over poor network connections. Altogether, the processing takes just a few seconds on a mobile device, and the result can be instantly viewed and shared. We perform extensive quantitative evaluation to validate our system and compare its new components against the current state-of-the-art.
Reversible data hiding exploiting Huffman encoding with dual images for IoMT based healthcare

Continuous technological progressions and huge investments are made for the realization of the various goals in the Internet of Things (IoT) driven networks. Internet of Medical Things (IoMT) being a part of IoT has made human living smarter. It is revolutionizing the healthcare industry and is providing a smarter healthcare framework to the people. The generic IoMT framework consists of the major components i.e., data acquisition, communication gateways, and servers. Once the data is acquired, it is sent over the insecure channel where its authentication is essential before diagnosis. In this work, a dual image reversible data hiding technique with high capacity is proposed for IoMT based networks. First of all, the acquired secret data is preprocessed using the Huffman encoding strategy. Once Huffman coding is applied, a codebook of ‘d’ bits is generated for encoding the converted decimal values using indices. The value of these indices is divided into 2 parts and embedded into two visually similar images to obtain dual stego images. The scheme has provided a very high payload while maintaining good perceptual quality. The results obtained depict significant improvement compared to the state-of-the-art. The scheme provides an average (percentage) improvement in embedding capacity by 33.2%, with the improvisation of Peak Signal to Noise (PSNR) Ratio by 1.32%. The average value of the Structural Similarity Index (SSIM) is found to be 0.8873. The scheme is computationally efficient which makes it a better candidate to be used in IoMT driven networks.
Citizens’ Attitudes to Contact Tracing Apps

Abstract Citizens’ concerns about data privacy and data security breaches may reduce the adoption of COVID-19 contact tracing mobile phone applications, making them less effective. We implement a choice experiment (conjoint experiment) where participants indicate which version of two contact tracing apps they would install, varying the apps’ privacy-preserving attributes. Citizens do not always prioritise privacy and prefer a centralised National Health Service system over a decentralised system. In a further study asking about participants’ preference for digital-only vs human-only contact tracing, we find a mixture of digital and human contact tracing is supported. We randomly allocated a subset of participants in each study to receive a stimulus priming data breach as a concern, before asking about contact tracing. The salient threat of unauthorised access or data theft does not significantly alter preferences in either study. We suggest COVID-19 and trust in a national public health service system mitigate respondents’ concerns about privacy.
Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian–Zhangbaling area, Anhui Province, China

The Zhangbaling–Guandian area is located in the eastern part of Anhui Province, China, and contains several small Au-Cu deposits and occurrences that highlight the prospectivity of this area for future mineral exploration. Recent research has determined that machine learning can identify potentially mineralization-related geochemical anomalies that represent targets for mineral exploration. However, the majority of this previous research has focused on identifying geochemical anomalies based on individual sample points but has not incorporated associated data such as the spatial characteristics of the shape, overlap, and zonation within multivariate geochemical anomalies and haloes. Here, we present a convolutional neural network algorithm based approach to identify areas prospective for Au exploration based on the multielement geochemical maps. This approach considers various spatial characteristics and employs a transfer learning method to reduce the influence of the limited number of known deposits and occurrences in this area, accelerating convergence rates and improving the accuracy of the model. The training results indicate that the accuracy of each training model is >99 and cross-entropy loss values are < 0.1. The results for the entirety of the study area indicate that about 88% of the known mineralization and mineralized occurrences are located in the prospective areas identified during this study. These results indicate that combining geochemical data with an approach employing convolutional neural network algorithms and transfer learning methods can effectively outline the Au mineralization prospectivity of relatively unexplored regions. This indicates that this type of approach could be a useful addition for future mineral exploration using geochemical data. In addition, the use of convolutional neural network approaches yields more accurate identification of geochemical anomalies and can include more geochemical variables, meaning that more geochemical data can be used in convolutional neural network-based approaches to target identification and mineral prospectivity modeling than is used in conventional geochemical anomaly identification.
Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process

The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition.
A secure blockchain-based e-health records storage and sharing scheme

Due to the expeditious evolution of wearable technology and wireless mobile networks, the tele-medicine and mobile medications have turned up into the Telecare Medicine Information System (TMIS). The efficient management of the patient’s Electronic Health Record (EHR) plays a vital role for the telecare of patients that include medication for the chronic patients, long-term telecare for the special patients, analysis of patients infected with certain disease etc. The sharing and distribution of EHRs with medical practitioners can enhance accuracy of the diagnosis; however, privacy and security preservation of patients’ record are the detracting issues of this system. Recently, due to the property of immutability, blockchain technology has been introduced to be as an auspicious solution for achieving EHR sharing with privacy and security preservation. This work puts forward a novel blockchain-based privacy and security preserving EHR sharing protocol for improved diagnosis and efficient treatments in TMIS. Firstly, two types of blockchains, named as private blockchain and the consortium blockchain, are constructed by formulating their consensus, data structures and mechanisms. Private blockchain is in-charge of maintaining EHRs, while consortium blockchain stores EHRs’ secure indexes. In order to attain data security, secure search, access control and privacy preservation, all the EHRs are public-key encrypted with appropriate searchable keyword. Security analysis of our protocol validates that our protocol achieves all the security goals by exempting several security attacks. For achieving efficient computational results, the user and server sides of our scheme are implemented separately. Furthermore, performance analysis of our protocol with other existing protocols reveals that our protocol is relatively better in terms of the communication and computation overhead by providing notable level of the security.
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning

Code completion, one of the most useful features in the Integrated Development Environments (IDEs), can accelerate software development by suggesting the libraries, APIs, and method names in real-time. Recent studies have shown that statistical language models can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from three major drawbacks: a) The hierarchical structural information of the programs is not fully utilized in the program's representation; b) In programs, the semantic relationships can be very long. Existing recurrent neural networks based language models are not sufficient to model the long-term dependency. c) Existing approaches perform a specific task in one model, which leads to the underuse of the information from related tasks. To address these challenges, in this paper, we propose a self-attentional neural architecture for code completion with multi-task learning. To utilize the hierarchical structural information of the programs, we present a novel method that considers the path from the predicting node to the root node. To capture the long-term dependency in the input programs, we adopt a self-attentional architecture based network as the base language model. To enable the knowledge sharing between related tasks, we creatively propose a Multi-Task Learning (MTL) framework to learn two related tasks in code completion jointly. Experiments on three real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods.
Convergence of Blockchain, IoT, and AI

Blockchain, IoT, and AI are key technologies driving the next wave of the digital transformation. We argue that these technologies will converge and will allow for new business models: Autonomous agents (i.e., sensors, cars, machines, trucks, cameras, and other IoT devices) will in the future act as own profit centers that (1) have a digital twin leveraging IoT, (2) send and receive money leveraging blockchain technology on their own and (3) autonomously make decisions as independent economic agents leveraging AI and data analytics. We further argue that this convergence will drive the development of such autonomous business models and, with it, the digital transformation of industrial corporations.
A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

Leukemia is an abnormal proliferation of leukocytes in the bone marrow and blood and it is usually diagnosed by the pathologists by observing the blood smear under a microscope. The count of various cells and their morphological features are used by the pathologists to identify and classify leukemia. An abnormal increase in the count of immature leukocytes along with a reduced count of other blood cells may be an indication of leukemia. The Pathologist may then recommend for bone marrow examination to confirm and identify the specific type of leukemia. These conventional methods are time consuming and may be affected by the skill and expertise of the medical professionals involved in the diagnostic procedures. Image processing based methods can be used to analyze the microscopic smear images to detect the incidence of leukemia automatically and quickly. Image segmentation is one of the very important tasks in processing and analyzing medical images. In the proposed paper an attempt has been made to review the available works in the area of medical image processing of blood smear images, highlighting automated detection of leukemia. The available works in the related area are reviewed based on the segmentation method used. It is learnt that even though there are many studies for detection of acute leukemia only a very few studies are there for the detection of chronic leukemia. There are a few related review studies available in the literature but, none of the works classify the previous studies based on the segmentation method used.