Reviewerly/exHarmony huggingface dataset:
A Machine Learning based Scalable Blockchain architecture for a secure Healthcare system

The evolution of the Industrial revolution from 3.0 to 4.0 has transformed the Healthcare environment. Patient Electronic Health Records (EHR) are shared with medical research institutes for clinical research and to manage national disease outbreaks. Healthcare systems implementing centralized machine learning models risk cyberattacks exposing private patient data. Blockchain-based data storage systems enable data security of EHR. However, the low transactions/minute of decentralized systems limit the performance of Healthcare systems and increase network bottleneck concerns. In this paper, we propose a Machine Learning based Blockchain architecture for secure Healthcare systems to preserve patient data privacy using Federated Learning and address Blockchain bottleneck issues by adding sidechains for processing growing transaction requests. A local model using machine learning trains data locally in hospitals and uploads it via Smart Contracts to the Public Healthcare System for global model training. Sidechains enable increased processing speed of Smart Contracts reducing congestions in the network and increasing the transactions per second in the mainchain.
A Mapping Study on Privacy Attacks in Big Data and IoT

Application domains like big data and IoT require a lot of user data collected and analyzed to extract useful information, and those data might include user's sensitive and personal information. Hence, it is strongly required to ensure the privacy of user data before releasing them in the public space. Since the fields of IoT and big data are constantly evolving with new types of privacy attacks and prevention mechanisms, there is an urgent need for new research and surveys to develop an overview of the state-of-art. We conducted a systematic mapping study on selected papers related to user privacy in IoT and big data, published between 2010 to 2021. This study focuses on identifying the main privacy objectives, attacks and measures taken to prevent the attacks in the two application domains. Additionally, a visualized classification of the existing attacks is presented along with privacy metrics to draw similarities and dissimilarities among different attacks.
A metaheuristic-based efficient strategy for multi-unit production planning with unique process constraints

Production planning provides optimal strategic planning to attain economic benefits in the petrochemical industries and helps to maintain competitiveness in the global market. This work proposes an efficient modeling strategy for combinatorial production planning involving unique process constraints in the development stage of the petrochemical industry. The decisions involved are the choice of products to be produced, the product quantity, the suitable process for the selected product, and the number of processing units to be implemented at the operating capacity. The objective is to determine an optimal production plan by maximizing the profit while satisfying all the resource and bound constraints. This work analyzed the strategy in the literature to identify the factors affecting metaheuristic techniques in determining better solutions for production planning involving unique process constraints. The compact and precise strategy proposed in this work reduces the number of decision variables and constraints up to 90% without compromising the rigor of the model. The efficacy of the proposed strategy is demonstrated on four case studies and is solved using eight different metaheuristic techniques, namely (i) Multi-Population based Ensemble of Mutation Strategies Differential Evolution, (ii) Sanitized Teaching Learning Based Optimization, (iii) Artificial Bee Colony, (iv) Dynamic Neighborhood Learning Particle Swarm Optimizer, (v) Sine Cosine Algorithm, (vi) Symbiotic Organisms Search, (vii) Single Phase Multi-Group Teaching Learning Based Optimization with Lévy Flight, and (viii) Harris Hawk Optimization. The results determined by these techniques in solving the case studies using the proposed strategy are better than those provided in the literature. It indicates the suitability of the proposed strategy with metaheuristic techniques and emphasizes the importance of efficient modeling of a problem. The proposed strategy provided up to 5% improvement in profit.
A semantic relation-aware deep neural network model for end-to-end conversational recommendation

Conversational recommendation system (CRS) aims at recommending appropriate items to the user through a multi-turn conversation. The end-to-end CRS is a type of CRS that models the recommendation task and the conversation task simultaneously which has attracted more and more attention in recent years. At the same time, knowledge graph and Transformer are incorporated into the end-to-end CRS to generate better recommendations and better responses to the user, which makes the CRS have state-of-the-art performance. It is known that there exist semantic relations in a conversation. However, we observe that existing end-to-end CRSs in general ignore the semantic relations in the conversation and therefore would likely hinder the performance of CRSs. Motivated by this, we propose a gated cross- and self-attention based CRS utilizing semantic relation information (ASR) model, which can explicitly model and utilize the semantic relations in a conversation. To the best of our knowledge, we are the first to advocate for modelling and utilizing the semantic relations in the end-to-end CRS, which could help to improve the performance of the CRS. Furthermore, to mitigate the class-imbalance problem that most end-to-end CRSs face, we propose a new negative sampling method which could make the proposed CRS learn better. Moreover, we design a Transformer-based dialogue module integrating the semantic relations in a conversation to generate more diversified and precise responses. Extensive experiments on widely used benchmark datasets demonstrate that the proposed ASR model achieves state-of-the-art results in both recommendation and conversation tasks.
ClusterValidityIndices.jl: Batch and Incremental Metrics for Unsupervised Learning

ClusterValidityIndices.jl is a Julia package for evaluating the performance of clustering algorithms without the aid of supervised labels.Cluster Validity Indices (CVI) provide a metric of the over-or under-partitioning of an arbitrary clustering algorithm with only the original data and labels assigned by the clustering algorithm.Furthermore, there exist formulations of every CVI such that they may run incrementally (i.e.Incremental CVIs, or ICVI), streaming alongside the clustering algorithm and producing the same results as in their batch implementations.Using a standard interface, each CVI in this package can be run with any clustering algorithm to produce a metric of that algorithm's performance in scenarios where explicit supervised labels do not exist, which is extremely useful in real-world applications where that is often the case.
Parallel processing of radiation measurements and radiation video optimization

In this study, we propose a parallel processing method for analyzing video-image radiation-response signals and suppressing radiation noise. We studied the linear-representation law of various image-information components on the radiation dose rate. Subsequently, the simulation images were used to examine the response-signal extract and radiation-noise suppression. The results indicate that the majority of response signals in the global image comprise forward superposition. The peak signal-to-noise ratio of the red channel was significantly improved when the noise signal-substitution algorithm and median filter were applied successively. Real-time radiation dose-rate measurements and clear images under irradiation can be obtained simultaneously.
Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel Networks

Payment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment.
TIPS: Transaction Inclusion Protocol With Signaling in DAG-Based Blockchain

Directed Acyclic Graph (DAG) is a popular approach to achieve scalability of blockchain networks. Due to its high efficiency in data communication and great scalability, DAG has been widely adopted in many applications such as Internet of Things (IoT) and Decentralized Finance (DeFi). DAG-based blockchain, nevertheless, faces the key challenge of transaction inclusion collision due to the high concurrency and the network delay. Particularly, the transaction inclusion collision in DAG-based blockchain leads to the revenue and throughput dilemmas, which would greatly degrade the system performance. In this paper, we propose “TIPS”, the Transaction Inclusion Protocol with Signaling, which broadcasts a signal indicating the transactions in the block. We show that with the prompt broadcast of a signal, TIPS substantially reduces the transaction collision and thus resolves these dilemmas. Moreover, we show that TIPS can defend against both the denial-of-service and the delay-of-service attacks. We also conduct intensive experiments to demonstrate the superior performance of the proposed protocol.
A resource for automated search and collation of geochemical datasets from journal supplements

This article presents a resource for automated search, extraction and collation of geochemical and geochronological data from the Figshare repository using web scraping code. To answer fundamental questions about the Earth's evolution, such as spatial and temporal evolution and interrelationships between the planet's solid and surficial reservoirs, researchers must utilize global geochemical datasets. Due to the volume of data being published, these datasets become quickly outdated. We present a resource that allows researchers to rapidly curate and update their own databases from existing published data. We use open-source Python code to web scrape the Figshare repository for journal supplementary files using the application programming interface, allowing for the collection and download of hundreds of supplementary files and metadata in minutes. Use of this web scraping tool is demonstrated here by collation of a zircon geochronology and chemistry database of >150,000 analyses. The database is consistent in reproducing trends in other published zircon compilations. Providing a resource for automated collection of Figshare data files will encourage data sharing and reuse.
Three dimensional deformation measurement method based on image guided point cloud registration

3D geometric capturing technologies based on camera and laser scanning have undergone huge advances. However, accurate 3D displacement measurement especially for in-plane movement based on point clouds remain unsolved problems. To deal with this challenge, a novel 3D deformation measurement method based on image guided point cloud registration is proposed. 2D digital image correlation method is adopted to calculate image displacement field and used to search the corresponding point from deformed point clouds. Then, accurate 3D displacement field can be obtained. Experiments have verified that the proposed method can realize accurate 3D displacement measurement at both short and far distance. The precision of 3D displacement measurement is 0.1 mm and 1 mm with structured light system and camera assisted TLS system separately.
Develop the hybrid Adadelta Stochastic Gradient Classifier with optimized feature selection algorithm to predict the heart disease at earlier stage

The technique of collecting and analyzing a massive quantity of patient data to obtain meaningful information was available in a medical big data analysis. In many fields, including cloud-based medical systems, there are many barriers to big data analysis. The healthcare industry generates a significant amount of heart disease details for the various patients. Most recent research focuses on business models based on big data analysis to improve predictive performance of heart attack data and reduce risk levels for patients. Data storage, however, has been a major challenge; data must be accessed efficiently in multiple locations in a decentralized context. An objective should be to generate a Hybrid Adadelta Stochastic Gradient Classifier-based Healthcare Hash Big Data Storage (HADSGC-HHBS) method of storing and managing clinical information from many places in a distributed setting with the least amount of space and in the shortest amount of time. Data are categorized using a HADSGC-HHBS technique after vast amounts of information have been collected based on certain characteristics. The stochastic Gradient Classification (SGD) algorithm is to classify patient information using a non-convex possible risk target than the Support Vector Machine(SVM) algorithm. A range of data documents is used to assess the proposed HADSGC-HHBS process. Compared to previous approaches, the proposed HADSGC-HHBS process was productive in terms of classification, false positives, and reduced computing complexity.
A stochastic time-series model for solar irradiation

We propose a novel stochastic time series model able to explain the stylized features of daily irradiation level data in 5 cities in Germany. The model is suitable for applications to risk management of photovoltaic power production in renewable energy markets. The suggested dynamics is a low-order autoregressive time series with seasonal level given by an atmospheric clear-sky model. Moreover, we detect a skewness property in the residuals which we explain by a winter–summer regime switch. The stochastic variance is modeled by a seasonally varying GARCH-dynamics. The winter and summer standardized residuals are proposed to be a Gaussian mixture model to capture the bimodal distributions. We estimate the model on the observed data, and perform a validation study. An application to energy markets studying the production at risk for a PV-producer is presented.
Design and user experience analysis of AR intelligent virtual agents on smartphones

Intelligent Virtual Agents (IVAs) can provide users with a friendly experience and have a wide range of applications in the era of artificial intelligence. However, most of existing IVAs are designed for personal computers. Design and user studies of IVAs on smartphones are uncommon. Therefore, developing IVAs for smartphones is an interesting topic. Considering Augmented Reality (AR) technology can provide more potential application value for IVAs, we mainly investigate users' experiences of AR IVAs on smartphones in this paper. To make an IVA more suitable for a smartphone, a lightweight IVA's cognitive architecture is proposed. To find out the factors that affect users' interaction experiences, the effects of humanoid embodiment and emotional expressions of IVAs on users' perceptions and experiences are explored. A museum is used as a specific task scenario to measure users' experiences. Three forms of AR agents are evaluated in this scenario: a voice assistant without an entity, a humanoid IVA without emotional expressions, and a humanoid IVA with emotional expressions. The results show that compared with the voice assistant, a humanoid embodiment can significantly improve the user's experience, and compared with humanoid IVA without emotional expressions, a humanoid IVA with emotional expressions is more welcome. Moreover, we use the cloud model to describe the uncertainty of IVAs' actions (blinking and body orientation). The results show that the uncertainty of actions can increase the believability of IVAs.
CoopFL: Accelerating federated learning with DNN partitioning and offloading in heterogeneous edge computing

Federated learning (FL), a novel distributed machine learning (DML) approach, has been widely adopted to train deep neural networks (DNNs), over massive data in edge computing. However, the existing FL systems often lead to a long training time due to resource limitation and system heterogeneity (e.g., computing, communication and memory) in edge computing. To this end, we design and implement an FL system, called CoopFL, which trains DNNs through the cooperation between devices and edge servers. Specifically, we implement DNN partitioning and offloading techniques in CoopFL, which enables each device to train a partial DNN model and offload the intermediate data outputted by some hidden layers to proper edge servers for cooperative training. However, some empirical partitioning and offloading strategies in previous works may not exploit the system resource well or even slow down the training procedure. To this end, we give a problem definition considering the resource constraints and system heterogeneity, and then propose an efficient algorithm to solve this problem so as to accelerate the training procedure by the developed DNN partitioning and offloading strategy. Extensive experiments on the classical models and datasets show the high effectiveness of our system. For example, CoopFL achieves a speedup of 2.3–4.9×, compared with the baselines, including hierarchical federated learning system (HFL), typical federated learning system (TFL), and two systems with empirical DNN partitioning, i.e., FedMEC and HFLP.
Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator

The improvement of recurrent neural network (RNN) algorithms is one of targets of many researchers, and these algorithms are widely used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete time-variant matrix pseudo-inversion is discussed in this paper. To be more specific, firstly, a DT-RNN algorithm mathematically founded on the second-order Taylor expansion is proposed for dealing with discrete time-variant matrix pseudo-inversion, and it does not require the theoretical support of continuous time-variant RNN (CT-RNN) algorithm. Secondly, the results of theoretical analyses of the proposed DT-RNN algorithm are also presented in this paper. These results demonstrate that the novel DT-RNN algorithm has remarkable computing performance. The efficiency and applicability of the DT-RNN algorithm have been verified through one numerical experiment and two robotic manipulator experiments.
Artistic neural style transfer using CycleGAN and FABEMD by adaptive information selection

Neural Style Transfer (NST) comprises a class of computer vision methods that manipulate digital images to reformulate the visual content of one input image adopting visual features of an another image set. Artistic NST is the particular case of NST where the visual features are extracted from painting images. The combination of Cycle-Consistent Adversarial Networks (CycleGANs) with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to adopt the specific artist’s style on images effectively, where the cycle-consistency loss is modified to incorporate texture information by estimating the corresponding Bidimensional Intrinsic Mode Functions (BIMFs). An adaptive approach for identifying the optimal BIMF number that must be considered in order to manipulate the required amount of the involved texture, is proposed. For this purpose, the computation of a metric is considered for each BIMF to characterise the texture of each image or major intensity alterations at local scale. Experimental results reveal that adaptive comixture of texture features comprises an efficient approach in such artistic applications. Qualitative and quantitative results demonstrate that the proposed framework outperforms state-of-the-art (SoA) methods.
Blockmarking: Hybrid model of blockchain and watermarking technique for copyright protection

Digital contents have become more popular and rapid in recent years. The need of techniques for digital content protection is also quickly required. Various watermarking techniques are proposed for protecting the digital contents. Blockchain technology has also become very popular and applied on many solutions. This paper proposes a new hybrid model based on the combination of blockchain and watermarking method. The main purpose is not only to achieve the goal of image copyright protection but also store the image into the blockchain network. Since our proposed method employs the blockchain mechanism, the digital contents authentication algorithm does not need any third party to join and confirm. Our experimental results show that the our method successfully achieved the purpose of digital copyright protection.
Combining Deep Learning and Kernel PCA for Software Defect Prediction

Software defect prediction aims to automatically determine the most likely location of defective program elements (i.e., statement, method, class, module etc.). Previous studies for software defect prediction mainly focus on exploring designing features such as source code complexity, object oriented design metrics etc. to classify program elements into two categories: (i) defective and (ii) non-defective. Although these approaches have obtained promising results, there exists two significant challenges in this research field: (i) removing irrelevant and redundant information from designing structures ; (ii) reducing the impact of skewed data distribution on learning models. In this paper, we aim to address these two issues by firstly applying kernel PCA to extract essential information from designing features and secondly proposing a deep neural network model which investigates the non-linear relationship among features. In order to mitigate the class imbalance, we apply a weighted loss function combined with a bootstrapping method to handle batch training mechanism of our model. We conducted some experiments to assess the performance of our proposed approach over NASA (with 10 projects) and PROMISE (with 34 projects) datasets. In order to leverage the efficiency of kernel PCA technique in software defect prediction, we compared it to some traditional feature selection approaches over a high-dimensional dataset ECLIPSE. The empirical results showed that our proposed method has outperformed these other state-of-the-art models by effectively predicting defective source files.