Reviewerly/exHarmony huggingface dataset:
The Empirical study and analysis of the Blockchain Technology

Blockchain is the emerging technological field for the current era due to its advantages such as distributed computing, transparency, cryptography enabled secure communication and message as well as source integrity. This technology is the innovation which can prompt critical changes the existing business climate and will have unexpected outcome. Blockchain can change the existing system and processes in which the organizations and business works, and able to modify our economy and financial system. It is distributed, decentralized and disseminated record novelty that expects to guarantee truthfulness. Since it can't be transformed or counterfeit most of the current focus in the research area as of cryptocurrency like BITCOIN. Blockchain Technology is something beyond digital currency, and it can have a few applications in government, money and banking industry, bookkeeping and Business Process Management. Hence, this examination endeavors to research and investigate its chances and difficulties for the momentum or future uses of Blockchain Technology. In this way, an enormous number of distributed investigations were painstakingly checked on and broke down dependent on their commitments to the Blockchain's assortment of information.
Deep Generative Graph Learning for Power Grid Synthesis

Power system studies require the topological structures of real-world power networks; however, such data may be confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this paper, we cast PGS into a graph distribution learning (GDL) problem where the probability density functions (PDFs) of the nodes (buses) and edges (lines) are captured. A novel deep GDL (DeepGDL) algorithm is proposed to learn the topological patterns of buses/lines with their physical features (e.g., power injection and line impedance). Having a deep nonlinear recurrent structure, DeepGDL understands complex nonlinear topological properties and captures the graph PDF. Sampling from the obtained PDF, we are able to create a large set of realistic networks that all resemble the original power grid. Simulation results show the significant accuracy of our created synthetic power grids in terms of various topological metrics and power flow measurements.
Cal-Net: Jointly Learning Classification and Calibration On Imbalanced Binary Classification Tasks

Datasets in critical domains are often class imbalanced, with a minority class far rarer than the majority class, and classification models face challenges to produce calibrated predictions on these datasets. A common approach to address this issue is to train classification models in the first step and subsequently use post-processing parametric or non-parametric calibration techniques to re-scale the model's outputs in the second step without tuning any underlying parameters in the model to improve calibration. In this study, we have shown that these common approaches are vulnerable to class imbalanced data, often producing unstable results that do not jointly optimize classification or calibration performance. We have introduced Cal-Net, a “self-calibrating” neural network architecture that simultaneously optimizes classification and calibration performances for class imbalanced datasets in a single training phase, thereby eliminating the need for any post-processing procedure for confidence calibration. Empirical results have shown that Cal-Net outperforms far more complex neural networks and post-processing calibration techniques in both classification and calibration performances on four synthetic and four benchmark class imbalanced binary classification datasets. Furthermore, Cal-Net can readily be extended to more complicated learning tasks, online learning and can be incorporated in more complex architectures as the final state.
Recommendations in Smart Devices Using Federated Tensor Learning

Recommendations based on prediction of user preferences from partial information are widely used in various applications. However, recommendations using smart devices have some challenges related to limited data device resources, data sparsity, and data privacy. Since there are many multidimensional data in smart devices, recommendations may collect a large amount of user private data. In this article, we study privacy-preserving recommendations with high-dimensional tensor data in smart devices. First, we propose a federated tensor completion scheme to infer the user’s preferences and we prove that this scheme satisfies the differential privacy guarantee. Our scheme consists of a global update and a local update, which reduce information exposure and guarantee local data privacy. Second, we mathematically analyze the privacy and utility of the proposed algorithm. Third, we provide empirical evaluations on synthetic data sets and real-world data sets. Results show that our scheme has a low recovery error and provides strong privacy protection.
Blockchain and IoT based inventory monitoring system

Blockchain Technology has certain inherent characteristics that can prove particularly useful to the field of IoT. It has the potential to add a layer of trust and transparency to the data reported by IoT based applications due to its decentralized and immutable nature. An application that utilizes these aspects of Blockchain has been explored in this paper. A smart inventory monitoring system that focuses on shipped goods was developed. The proposed system monitors the temperature and humidity of the goods' container. It also keeps track of whether the lid of the container was opened at a certain point or not and the duration for which it was left opened. These details are periodically uploaded to Ethereum Blockchain's Rinkeby Test Network along with a timestamp and location history. These details can then be retrieved from the Ethereum Blockchain and monitored for signs of tampering or undesirable conditions. The system was implemented on a Raspberry Pi 4. Details such as transaction construction and upload times have also been reported in the paper. The proposed system was able to provide an immutable and decentralized historical log of the shipped inventory as it passed through different stages of the delivery process.
Oblivious Message Retrieval

Anonymous message delivery systems, such as private messaging services and privacy-preserving payment systems, need a mechanism for recipients to retrieve the messages addressed to them without leaking metadata or letting their messages be linked. Recipients could download all posted messages and scan for those addressed to them, but communication and computation costs are excessive at scale. We show how untrusted servers can detect messages on behalf of recipients, and summarize these into a compact encrypted digest that recipients can easily decrypt. These servers operate obliviously and do not learn anything about which messages are addressed to which recipients. Privacy, soundness, and completeness hold even if everyone but the recipient is adversarial and colluding (unlike in prior schemes). Our starting point is an asymptotically-efficient approach, using Fully Homomorphic Encryption and homomorphically-encoded Sparse Random Linear Codes. We then address the concrete performance using bespoke tailoring of lattice-based cryptographic components, alongside various algebraic and algorithmic optimizations. This reduces the digest size to a few bits per message scanned. Concretely, the servers’ cost is $${\sim }\$1$$ per million messages scanned, and the resulting digests can be decoded by recipients in under $${\sim }$$ 20 ms. Our schemes can thus practically attain the strongest form of receiver privacy for current applications such as privacy-preserving cryptocurrencies.
Language Processing Differences Between Blind and Sighted Individuals and the Abstract Versus Concrete Concept Difference

In the property listing task (PLT), participants are asked to list properties for a concept (e.g., for the concept dog, "barks," and "is a pet" may be produced). In conceptual property norming (CPNs) studies, participants are asked to list properties for large sets of concepts. Here, we use a mathematical model of the property listing process to explore two longstanding issues: characterizing the difference between concrete and abstract concepts, and characterizing semantic knowledge in the blind versus sighted population. When we apply our mathematical model to a large CPN reporting properties listed by sighted and blind participants, the model uncovers significant differences between concrete and abstract concepts. Though we also find that blind individuals show many of the same processing differences between abstract and concrete concepts found in sighted individuals, our model shows that those differences are noticeably less pronounced than in sighted individuals. We discuss our results vis-a-vis theories attempting to characterize abstract concepts.
Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis.Cervical cancer is a vital reason for increased women's mortality rate.Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer.Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches.This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical pap smear images.The proposed IDCNN-CDC model involves four major processes such as preprocessing, segmentation, feature extraction, and classification.Initially, the Gaussian filter (GF) technique is applied to enhance data through noise removal process in the Pap smear image.The Tsallis entropy technique with the dragonfly optimization (TE-DFO) algorithm determines the segmentation of an image to identify the diseased portions properly.The cell images are fed into the DL based SqueezeNet model to extract deeplearned features.Finally, the extracted features from SqueezeNet are applied to the weighted extreme learning machine (ELM) classification model to detect and classify the cervix cells.For experimental validation, the Herlev database is employed.The database was developed at Herlev University Hospital (Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity, specificity, accuracy, and F-Score.
AKIRA—A Voice Based Virtual Assistant with Authentication

Virtual voice assistance executes the natural language commands and reciprocates its response using synthesized voice. Adoption of voice-based assistance is increasing at a greater speed. Google assistant, Alexa from Amazon, Siri from Apple, Cortana from Microsoft are the leading voice assistants. Various surveys have been conducted to review the popularity and correctness of these voice assistance and results displayed that these virtual assistances were leading by gaining more popularity especially among the youngsters. Studies have also highlighted that these devices have a history of gaining unauthorized access to the device and caused damage to the owner. The current paper is an attempt to address the security aspect by categorizing the commands to either permission or permission-less. The permission commands have to undergo an authentication procedure to ensure high security and privacy of the owner.
A Graph-Based Approach for Inferring Semantic Descriptions of Wikipedia Tables

There are millions of high-quality tables available in Wikipedia. These tables cover many domains and contain useful information. To make use of these tables for data discovery or data integration, we need precise descriptions of the concepts and relationships in the data, known as semantic descriptions. However, creating semantic descriptions is a complex process requiring considerable manual effort and can be error prone. In this paper, we present a novel probabilistic approach for automatically building semantic descriptions of Wikipedia tables. Our approach leverages hyperlinks in a Wikipedia table and existing knowledge in Wikidata to construct a graph of possible relationships in the table and its context, and then it uses collective inference to distinguish genuine and spurious relationships to form the final semantic description. In contrast to existing methods, our solution can handle tables that require complex semantic descriptions of n-ary relations (e.g., the population of a country in a particular year) or implicit contextual values to describe the data accurately. In our empirical evaluation, our approach outperforms state-of-the-art systems on the SemTab2020 dataset and outperforms those systems by as much as 28% in F1 score on a large set of Wikipedia tables.
Firefly algorithm for instance selection

The paper focuses on the problem of instance selection. Instance selection is currently crucial to enhance the efficacy and efficiency of machine-learning tools when they are used to solve a data-mining task and when the data are large and they are seen through the prism of the big data phenomenon. Instance selection eliminates redundant instances and thus reduces the size of the training data set. The training data, with redundant cases removed, can be more useful and ensure better performance of the final classification models. The instance selection problem belongs to the NP-hard class, so it can be solved with an approximation tool. In this paper the firefly algorithm is proposed for solving the instance selection problem. This paper is one paper, where the firefly algorithm has been used to solve a discrete optimisation problem, when in more cases previously it has been used for solving continuous optimisation problems. The firefly-based instance selection algorithm is presented and its validation is carried out. The results of the computational experiment show that the algorithm is competitive with others. The results obtained are discussed and conclusions are formulated.
Convergence properties of radial basis functions networks in function learning

In this article we consider asymptotic properties of the normalized radial basis function networks with one hidden layer trained by independent patterns with arbitrary distributions. Convergence and rates of convergence are investigated and the choice of the radial basis functions and the network parameters are discussed.
A new end-to-end image dehazing algorithm based on residual attention mechanism

Traditional image dehazing algorithms based on prior knowledge and deep learning rely on the atmospheric scattering model and are easy to cause color distortion and incomplete dehazing. To solve these problems, an end-to-end image dehazing algorithm based on residual attention mechanism is proposed in this paper. The network includes four modules: encoder, multi-scale feature extraction, feature fusion and decoder. The encoder module encodes the input haze image into feature map, which is convenient for subsequent feature extraction and reduces memory consumption; the multi-scale feature extraction module includes residual smoothed dilated convolution module, residual block and efficient channel attention, which can expand the receptive field and extract different scale features by filtering and weighting; the feature fusion module with efficient channel attention adjusts the channel weight dynamically, acquires rich context information and suppresses redundant information so as to enhance the ability to extract haze density image of the network; finally, the encoder module maps the fused feature nonlinearly to obtain the haze density image and then restores the haze free image. The qualitative and quantitative tests based on SOTS test set and natural haze images show good objective and subjective evaluation results. This algorithm improves the problems of color distortion and incomplete dehazing effectively.
Thinking Fast and Slow in AI: The Role of Metacognition

Artificial intelligence (AI) still lacks human capabilities, like adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. Humans achieve some of these capabilities by carefully combining their thinking "fast" and "slow". In this work we define an AI architecture that embeds these two modalities, and we study the role of a "meta-cognitive" component, with the role of coordinating and combining them, in achieving higher quality decisions.
Information Security Awareness and Behaviors of Health Care Professionals at Public Health Care Facilities

This study investigated information security behaviors of professionals working in the public health sector to guide policymakers toward focusing their investments in infrastructure and training on the most vulnerable segments. We sought to answer the following questions: (1) Are certain professional demographics more vulnerable to cybersecurity threats? (2) Do professionals in different institution types (i.e., hospitals vs. primary care clinics) exhibit different cybersecurity behaviors? (3) Can Internet usage behaviors by professionals be indicative of their cybersecurity awareness and the risk they introduce?A cross-sectional, anonymous, paper-based survey was distributed among professionals working in public health care organizations in Kuwait. Data were collected about each professional's role, experience, work environment, cybersecurity practices, and understanding to calculate a cybersecurity score which indicates their level of compliance to good cybersecurity practices. We also asked about respondents' internet usage and used K-means cluster analysis to segment respondents into three groups based on their internet activities at work. Ordinary least squares regression assessed the association between the collected independent variables in question on the overall cybersecurity behavior.A total of 453/700 (64%) were responded to the survey. The results indicated that professionals with more work experience demonstrated higher compliance with good cybersecurity practices. Interestingly, nurses demonstrate higher cybersecurity aptitude relative to physicians. Professionals that were less inclined to use the internet for personal use during their work demonstrated higher cybersecurity aptitude.Our findings provide some guidance regarding how to target health care professional training to mitigate cybersecurity risks. There is a need for ensuring that physicians receive adequate cybersecurity training, despite the opportunity costs and other issues competing for their attention. Additionally, classifying professionals based on their internet browsing patterns may identify individuals vulnerable to cybersecurity incidents better than more discrete indicators such as age or gender.
The impact of cognitive style on the level of satisfaction and image search behavior in the textual and content search system of Google Images

Purpose The purpose of this study aims to identify the impact of verbal-visual cognitive styles on the level of satisfaction and behavior in the textual and content search of Google Images. Design/methodology/approach “Riding” cognitive style test and satisfaction questionnaire were used as data collection tools. Also, to collect data related to the image search behavior, the subjects’ transaction files were recorded using Camtasia software and then the files observed and reviewed. The research sample was 90 postgraduate students of Shiraz University. Findings The results showed that cognitive styles in interaction with the text-based and content-based search system of “Google Images” affected user’s satisfaction. Text-based image retrieval, in which vocabulary-based information needs were expressed, was more compatible with the verbal cognitive style and resulted in greater satisfaction. In contrast, in content-based image retrieval, where it was possible to express information needs in the form of images, users were more satisfied with the visual cognitive style. Verbal users performed more positively in text-based search and visual users in content-based search. Originality/value Considering the research gap, which has identified the performance of visual text-based and content-based systems in terms of satisfaction and cognitive style search behavior, the present study could be considered a small effort to promote science.
NoiER: An Approach for Training More Reliable Fine-Tuned Downstream Task Models

The recent development in pretrained language models that are trained in a self-supervised fashion, such as BERT, is driving rapid progress in natural language processing. However, their brilliant performance is based on leveraging syntactic artefacts of the training data rather than fully understanding the intrinsic meaning of language. The excessive exploitation of spurious artefacts is a problematic issue: the distribution collapse problem, which is the phenomenon that the model fine-tuned on downstream tasks is unable to distinguish out-of-distribution sentences while producing a high-confidence score. In this paper, we argue that the distribution collapse is a prevalent issue in pretrained language models and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">noise entropy regularisation (NoiER)</i> as an efficient learning paradigm that solves the problem without auxiliary models and additional data. The proposed approach improved traditional out-of-distribution detection evaluation metrics by 55% on average compared to the original fine-tuned models.
Secured Data Transfer Between Fog Nodes Using Blockchain

Fog computing, being the underlying network between edge devices and cloud data centre, has high security vulnerability. In the existing literature, security concerns in fog computing are been specified in terms of user authentication, secured data exchange, data privacy, and DoS attack. In this paper to address the security issues between fog nodes, a novel approach to introducing blockchain technology has been proposed. Usage of blockchain in fog nodes brings novel architecture as well as storing and transmitting data to trusted fog nodes and keep their references as blocks in all the connected fog nodes in the network. Results from analytical evaluation demonstrate that our proposed work provides security features that include tamper-proof and attacks resiliency in the fog computing environment.
Energy Minimization in a Cloud Computing Environment

Energy efficiency is progressively necessary for ICT because of the improved and increased usage of ICT which increases the cost of energy and carbon footprint into the environment which further calls for technologies that are energy efficient that reduces the consumption of energy, storage, and communication. Cloud computing gives us access to the data from a centralized pool of resources and uses it as per the requirement. The cloud data centers that are hosting cloud computing applications use higher volumes of energy thus adding up to the increased functioning cost and carbon footprint in the environment which is one of the main disadvantages of employing cloud computing which raises the need for energy-efficient data centers. With the shortage of energy and climate changes all over the globe being the main concerns during this time, the power consumption of data centers still remains the crucial concern. This paper provides an overview on the importance of implementing green cloud computing. We have summarized the crucial challenges and have studied their corresponding solutions. The main aim of this paper is to help understand the concept and necessity of saving energy and having energy-efficient data centers which helps in decreasing the power bills and help in making the environment eco-friendly.