Reviewerly/exHarmony huggingface dataset:
Access Control of Healthcare Data using Blockchain Technology

The innovative development in the internet of things is not only giving rapid growth in the development of efficiency of new technologies but also giving a threat relative to the integrity and confidentiality of data. So, it requires security, privacy, data integrity, confidentiality, authentication and authorization which is provided by the Blockchain Technology. This paper mainly represents access control mechanism for different nodes in the smart healthcare environment. For this it uses multiple access control smart contracts. Here the smart contract is implemented using Ethereum Smart Contract increasing the confidentiality and integrity of data by providing Authentication, Access control, Authorization for the management of data sharing and storage of data. Here the smart contracts will provide Node Authentication and authorization of each device, based on the registration to provide the data security which is missing in other Internet of Thing’s applications.
A Homomorphic Cloud Framework for Big Data Analytics Based on Elliptic Curve Cryptography

Homomorphic Encryption (HE) comes as a sophisticated and powerful cryptography system that can preserve the privacy of data in all cases when the data is at rest or even when data is in processing and computing. All the computations needed by the user or the provider can be done on the encrypted data without any need to decrypt it. However, HE has overheads such as big key sizes and long ciphertexts and as a result long execution time. This paper proposes a novel solution for big data analytic based on clustering and the Elliptical Curve Cryptography (ECC). The Extremely Distributed Clustering technique (EDC) has been used to divide big data into several subsets of cloud computing nodes. Different clustering techniques had been investigated, and it was found that using hybrid techniques can improve the performance and efficiency of big data analytic while at the same time data is protected and privacy is preserved using ECC.
Utilization of Landsat-8 data for lithological mapping of neoproterozoic basement rocks in north Qena-Safaga road, North Eastern Desert, Egypt

Mapping an area accurately with semi-variable lithological units of basement rocks is challenging. Although free-of-charge remote sensing data have boosted the lithological mapping studies, the accuracy and validation of the results are still questionable. This work aims to combine remote sensing techniques and petrographical studies with field investigation in order to discriminate the basement rocks at a part of the Arabian Nubian Shield (ANS) near Al-Baroud area, North Eastern Desert of Egypt. The lithological units in the study area were successfully discriminated based on their spectral characteristics. Several processing techniques on Landsat-8 images were applied, such as MNF (3, 2, and 1), FCC (7, 6, and 5), and (7, 5, and 1), and PCA (PC1, PC2, and PC3) and (PC2, PC1, and PC4), in addition to three composite band ratios (6/7, 5/6, and 4/2), (5/7, 6/4x7, and 5/1), and (7/5x3, 6/4x7, and 5/3x4), with the aid of supervised and unsupervised classifications. The results reveal that the rock units in the study area belong to the Late Cryogenian-Ediacaran magmatism of East African Orogeny (EAO). The core idea of this work is to combine several remote sensing techniques with petrographical study and field investigation to provide an innovative and integrated methodology to discriminate the ANS basement rocks, which can be applied elsewhere in similar territories.
TRIGLAV: Remote Attestation of the Virtual Machine's Runtime Integrity in Public Clouds

Trust is of paramount concern for tenants to deploy their security-sensitive services in the cloud. The integrity of virtual machines (VMs) in which these services are deployed needs to be ensured even in the presence of powerful adversaries with administrative access to the cloud. Traditional approaches for solving this challenge leverage trusted computing techniques, e.g., vTPM, or hardware CPU extensions, e.g., AMD SEV. But, they are vulnerable to powerful adversaries, or they provide only load time (not runtime) integrity measurements of VMs. We propose TRIGLAV, a protocol allowing tenants to establish and maintain trust in VM runtime integrity of software and its configuration. TRIGLAV is transparent to the VM configuration and setup. It performs an implicit attestation of VMs during a secure login and binds the VM integrity state with the secure connection. Our prototype's evaluation shows that TRIGLAV is practical and incurs low performance overhead (< 6%).
Blockchain Technology for Healthcare Record Management

The last decade has seen the rise of innovations such as smart computers, wearable devices, smartphones, digital cameras, robots, social media networks, and ICT advancements in day-to-day life, leading to the digitization of numerous activities. Advances in trending technologies have contributed to their adaptation in every part of human’s daily routine. Almost all healthcare-associated domains are now adapting to eHealth. Health professionals have been able to collect medical records, exchange medical data through a network of professionals and patients, provide healthcare through digital media, and track various health-related events, diseases, and disease outbreaks through a range of technologies, such as social media sites, applications, smartphones, and smart objects. This has resulted in an ample amount of knowledge related to patients that is created and needs to be handled effectively for better results. Although big data is the obvious option to handle this vast data collection, data protection and security for public health data is a prime concern. A blockchain is known as a record database located in a distributed personal computer network called nodes. In particular, it is a mutual, permanent record of peer-to-peer transactions constructed from linked blocks of transactions and stored in a digital ledger. No single person owns it, i.e., it is decentralized. The healthcare sector is one of the sectors looking to embrace the blockchain. This chapter aims to reflect the numerous facts leading to the introduction of blockchain technology in general and electronic medical record management of healthcare in particular. The chapter will study the various blockchain implementation frameworks.
Investigating a Virtual Reality Based Subterranean Scenario Examining Augmented Reality Implications for Military Operators

Virtual and augmented reality have made it possible to test new technologies for military missions - such as using augmented reality to support complicated subterranean (sub-t) missions. The military will soon offer operators the ability to wear augmented reality (AR) head-mounted displays (HMDs) while conducting missions. Therefore, it is crucial to understand how this emerging technology will positively and negatively impact Soldiers’ ability to execute missions. This paper describes the development of a virtual reality (VR) scenario designed to support user experience research for evaluating the efficacy of Soldier AR systems during sub-t operations. We describe usability test results that captured feedback and reactions to the VR scenario and the AR interface that presented critical information through a VR HMD. We conclude with lessons learned from our development process and future research plans.
PIPC: Privacy- and Integrity-Preserving Clustering Analysis for Load Profiling in Smart Grids

Generally, power utilities can utilize smart-meter data to extract load patterns through load-profiling technologies, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering. To improve the efficiency of load profiling, both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering and smart-meter data can be outsourced to powerful clouds. However, clouds are not completely trustworthy: private meter data may be used for commercial interests; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering may also be performed with fewer iterations to save computational costs, which violates the integrity of outsourced clustering. In this article, therefore, a secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means-clustering scheme is proposed, called privacy-preserving and integrity-preserving clustering (PIPC), which aims to protect the privacy and integrity of load profiling. To this end, two techniques are designed: 1) encrypted distance measurement, in which a public comparison matrix is constructed by securely embedding a secret key matrix and 2) integrity assurance, in which a specific Stackelberg game is designed to create economic incentives. The former, as the core of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering, can protect the privacy of meter data. The latter ensures that clouds can obtain the maximum utility only when clouds execute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering in an honest manner, thereby preserving the integrity of outsourced computing. Experimental results demonstrate that PIPC reaches high clustering accuracy and computational efficiency for load profiling while retaining smart-meter data privacy and outsourced-clustering integrity.
An Investigation into Performance Factors of Two-Stream I3D Networks

Two-Stream Inflated 3D ConvNet (I3D) is based on 2D convolutional networks. It is inflated into 3D to deal with spatiotemporal feature extraction and classification in videos. I3D network is an efficient solution for video action recognition, and outstanding results have been obtained after applying the model pre-trained with Kinetics dataset. This paper discusses some ideas for improving the efficiency of I3D network. Instead of counting on network architecture improvement, efforts are focused on two aspects: 1) from the point of view of data pre-processing, including training data cleansing and augmentation. A range of data augmentation schemes are investigated to enhance the balance and regularity of input data in the training and testing phases. This idea is inspired by the original I3D model and proposers. 2) from the perspective of network backbones, for example, through the application of ResNet-50 as an alternative backbone model to gain a better perception into key performance factors for Two-Stream I3D networks. Experiment results clearly show that the proposed hybrid improvement strategy brings substantial improvement in recognition accuracy for benchmark and practical datasets.
JPEG steganalysis based on denoising network and attention module

The core objective of image steganalysis is to explore the presence of weak image steganographic signals. Extracting effective steganographic signal features will play an essential role in digital image steganalysis. However, existing networks rely more on spatial rich model kernels or random learnable kernels to obtain noise residuals during the stage of steganographic signal features extraction. In this paper, we proposed a JPEG steganalysis network which based on denoising network and attention module, mainly including a noise extract block, a noise analysis block, and a judgment block. Specifically, a professional denoising convolutional neural network is first introduced in noise extract block to obtain better steganalysis features. The noise analysis block is integrated with the attention module to finely extract the steganographic signals hidden in the complex texture regions, which is quite effective in improving the signal-to-noise ratio of the stego signal. The judgment block is primarily a classifier to distinguish between cover images and stego images. Comprehensive experiments show a significant improvement in performance over the state-of-the-art steganalysis scheme. Moreover, the proposed network has better generalization capability than the compared steganalysis network for the case of cover-source and quality factor mismatch, which is critical for future steganalysis systems.
Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling

We consider federated edge learning (FEEL) over wireless fading channels taking into account the downlink and uplink channel latencies, and the random computation delays at the clients. We speed up the training process by overlapping the communication with computation. With fountain coded transmission of the global model update, clients receive the global model asynchronously, and start performing local computations right away. Then, we propose a dynamic client scheduling policy, called MRTP, for uploading local model updates to the parameter server (PS), which, at any time, schedules the client with the minimum remaining upload time. However, MRTP can lead to biased participation of clients in the update process, resulting in performance degradation in non-iid data scenarios. To overcome this, we propose two alternative schemes with fairness considerations, termed as age-aware MRTP (A-MRTP), and opportunistically fair MRTP (OF-MRTP). In A-MRTP, the remaining clients are scheduled according to the ratio between their remaining transmission time and the update age, while in OF-MRTP, the selection mechanism utilizes the long term average channel rate of the clients to further reduce the latency while ensuring fair participation of the clients. It is shown through numerical simulations that OFMRTP provides significant reduction in latency without sacrificing test accuracy.
FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.
Path planning method for mobile robot with maneuver restrictions

in this report a method of trajectory planning for a mobile robot with maneuver restrictions is developed. The maneuver restrictions depends on robot's velocity. Restrictions are represented by virtual obstacles that close up the inaccessible to the robot sectors when it moves at specific velocity. The proposed method is compatible with any path-planning algorithm. The paper describes trajectory planning system of mobile robot based on rapidly exploring random trees method. Dijkstra's algorithm searches the shortest path in the tree. Motion control system calculates the desired yaw angle and velocity of the robot and automatically reduces the velocity when approaching obstacles and while turning. Simulation results for the wheeled robot in obstructed environment are presented.
Text Classification and Topic Modelling of Web Extracted Data

Text classification and Topic Modelling is the backbone for the text analysis of huge amount of corpus of data. With an increase in unstructured data around us, it is very difficult to analyse the data very easily. There is a need for some methods that can be applied to the data to get the sensitive and semantic information from the corpus. Text classification is categorization of text in organised way for the interpretation of sensitive information from the text, while Topic modelling is finding the abstract topic for the collection of text or document. Topic modelling is used frequently to find semantic information from the textual data. In this paper we applied Parsing techniques on various websites to extract the HTML and XML data which includes the textual data and also applied Preprocessing techniques to clean the data. For the text classification purpose some of the Machine learning based classifiers that we have used in our experiment are Naive Bayes and also Logistic Regression Classifier. The models of the document are built using three different topic modelling methods which are Latent Semantic Analysis, Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation. In the further experiment we have done analysis and also comparison based upon the performance of the models and classifiers on the processed textual data.
Formal Analysis of Neural Network-Based Systems in the Aircraft Domain

Neural networks are being increasingly used for efficient decision making in the aircraft domain. Given the safety-critical nature of the applications involved, stringent safety requirements must be met by these networks. In this work we present a formal study of two neural network-based systems developed by Boeing. The Venus verifier is used to analyse the conditions under which these systems can operate safely, or generate counterexamples that show when safety cannot be guaranteed. Our results confirm the applicability of formal verification to the settings considered.
To Play and To Be Played: Exploring the Design of Urban Machines for Playful Placemaking

Within the paradigm of the smart and playable city, the urban landscape and street furniture have provided a fertile platform for pragmatic and hedonic goals of urban liveability through technology augmentation. Smart street furniture has grown from being a novelty to become a common sight in metropolitan cities, co-opted for improving the efficiency of services. However, as we consider technologies that are increasingly smarter, with human-like intelligence, we navigate towards uncharted waters when discussing the consequences of their integration with the urban landscape. The implications of a new genre of street furniture embedded with artificial intelligence, where the machine has autonomy and is an active player itself, are yet to be fully understood. In this article, we analyse the evolving design of public benches along the axes of smartness and disruption to understand their qualities as playful, urban machines in public spaces. We present a concept-driven speculative design case study, as an exploration of a smart, sensing, and disruptive urban machine for playful placemaking. With the emergence of artificial intelligence, we expand on the potential of urban machines to partake an increasingly active role as co-creators of play and playful placemaking in the cities of tomorrow.
Unsupervised Multi-View Post-OCR Error Correction With Language Models

We investigate post-OCR correction in a setting where we have access to different OCR views of the same document.The goal of this study is to understand if a pretrained language model (LM) can be used in an unsupervised way to reconcile the different OCR views such that their combination contains fewer errors than each individual view.This approach is motivated by scenarios in which unconstrained text generation for error correction is too risky.We evaluated different pretrained LMs on two datasets and found significant gains in realistic scenarios with up to 15% WER improvement over the best OCR view.We also show the importance of domain adaptation for post-OCR correction on out-of-domain documents.
Pixel-Wise Deep Reinforcement Learning Approach for Ultrasound Image Denoising

Ultrasound (US) imaging is widely used for the tissue characterization. However, US images commonly suffer from speckle noise, which degrades perceived image quality. Various deep learning approaches have been proposed for US image denoising, but most of them lack the interpretability of how the network is processing the US image (black box problem). In this work, we utilize a deep reinforcement learning (RL) approach, the pixelRL, to US image denoising. The technique utilizes a set of easily interpretable and commonly used filtering operations applied in a pixel-wise manner. In RL, software agents act in an unknown environment and receive appropriate numerical rewards. In our case, each pixel of the input US image has an agent and state of the environment is the current US image. Agents iteratively denoise the US image by executing the following pixel-wise pre-defined actions: Gaussian, bilateral, median and box filtering, pixel value increment/decrement and no action. The proposed approach can be used to generate action maps depicting operations applied to process different parts of the US image. Agents were pre-trained on natural gray-scale images and evaluated on the breast mass US images. To enable the evaluation, we artificially corrupted the US images with noise. Compared with the reference (noise free US images), filtration of the images with the proposed method increased the average peak signal-to-noise ratio (PSNR) score from 14 dB to 26 dB and increased the structure similarity index score from 0.22 to 0.54. Our work confirms that it is feasible to use pixel-wise RL techniques for the US image denoising.
Addressing Uncertainties within Active Learning for Industrial IoT

Internet of Things (IoT) is a key enabler of Industry 4.0 with networked devices providing sensor data to help manage, automate, streamline and optimize assets, operations and processes. In such industrial IoT settings, reliability and process experts spend a considerable amount of time in creating accurate ground-truth data to assist with the inferencing capabilities of Artificial Intelligence (AI) engines. This process can be time-consuming and sometimes inaccurate, depending on the complexity of data. Accurate expert annotated data is the foundation for many AI applications because data needs to be classified on several bases, for instance into ‘normal’, ‘abnormal’ or ‘pre-abnormal’ states. Such problem formulations can be appropriately addressed using Active Learning (AL) techniques. We propose an AL framework capable of handling two practical challenges: oracle uncertainty and quantification of model performance in the absence of ground truth. Consequently, the proposed approach addresses uncertainties within AL techniques by fusing information pertaining to expertise levels of the human annotators and their confidence levels corresponding to the annotation provided.