Reviewerly/exHarmony huggingface dataset:
Reassembling the elderly consumption ensemble: retaining independence through smart assisted living technologies

Smart assisted living technologies are often touted as the solution to the challenges associated with an ageing population. Viewing elderly consumers, their relatives, and technologies as comprising an assemblage, this article aims to understand how smart objects actively reshape the everyday practices in families with elderly consumers. Interviews with and observations of users of smart alarm systems indicate a stratification of the paired experiences of users and systems and identify a tension between enabling experiences of the elderly and constraining experiences of the relatives. This article contributes to views of families with elderly as assemblages by providing insights into joint and disjoint consumer experiences in multiple consumer-object assemblages, identity negotiations of the elderly and their relatives, and the hidden costs of smart assisted living technologies.
Framing the Central Bank Digital Currency (CBDC) revolution

As global cooperation to develop and launch CBDCs further unfolds, the revolutionary innovation presents an emerging research field. This paper aims to provide a framework of CBDC by stressing its differences from other available digital currencies and cash in terms of advantages and disadvantages. The CBDC outlook, in its current and future, is presented. Additionally, an exploration of the prevalent themes in a cross-sectional analysis of tweets posted between 17 and 25 March 2021 with the #CBDC hashtags are presented to complement the discussion on the emerging landscape for informing the policy – and decision-makers on the opportunities and challenges involved.
The Effects of Earthquake Experience on Intentions to Respond to Earthquake Early Warnings

Warning systems are essential for providing people with information so they can take protective action in response to perils. Systems need to be human-centered, which requires an understanding of the context within which humans operate. Therefore, our research sought to understand the human context for Earthquake Early Warning (EEW) in Aotearoa New Zealand, a location where no comprehensive EEW system existed in 2019 when we did this study. We undertook a survey of people's previous experiences of earthquakes, their perceptions of the usefulness of a hypothetical EEW system, and their intended responses to a potential warning (for example, Drop, Cover, Hold (DCH), staying still, performing safety actions). Results showed little difference in perceived usefulness of an EEW system between those with and without earthquake experience, except for a weak relationship between perceived usefulness and if a respondent's family or friends had previously experienced injury, damage or loss from an earthquake. Previous earthquake experience was, however, associated with various intended responses to a warning. The more direct, or personally relevant a person's experiences were, the more likely they were to intend to take a useful action on receipt of an EEW. Again, the type of experience which showed the largest difference was having had a family member or friend experience injury, damage or loss. Experience of participation in training, exercises or drills did not seem to prompt the correct intended actions for earthquake warnings; however, given the hypothetical nature of the study, it is possible people did not associate their participation in drills, for example, with a potential action that could be taken on receipt of an EEW. Our analysis of regional differences highlighted that intentions to mentally prepare on receipt of a warning were significantly higher for Canterbury region participants, most likely related to strong shaking and subsequent impacts experienced during the 2010–11 Canterbury Earthquake Sequence. Our research reinforces that previous experience can influence earthquake-related perceptions and behaviors, but in different ways depending on the context. Public communication and interventions for EEW could take into consideration different levels and types of experiences of the audience for greater success in response.
Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Statistical partitioning and publishing is commonly used in location-based big data services to address queries such as the number of points of interest, available vehicles, traffic flows, infected patients, etc., within a certain range. Adding noise perturbation to the location-based statistical data according to the differential privacy model can reduce various risks caused by location privacy leakage while keeping the statistical characteristics of the published data. The traditional statistical partitioning and publishing methods realize the decomposition and indexing of 2D space from top to bottom. However, they can easily cause the over-partitioning or under-partitioning phenomenon, and therefore need multiple times of data scan. This paper proposes a grid clustering and differential privacy protection method for location-based statistical big data publishing scenarios. We implement location-based big data statistics in units of equal-sized grids and perform density classification on uniformly distributed grids by discrete wavelet transform. A bottom-up grid clustering algorithm is designed to perform on the blank and the uniform grids of the same density level based on neighborhood similarity. The Laplacian noise is incorporated into the clustering results according to the differential privacy model to form the published statistics. Experimental comparison of the real-world datasets manifests that the grid clustering and differential privacy publishing method proposed in this paper is superior to other existing partition publishing methods in terms of range querying accuracy and algorithm operating efficiency.
Dynamic-balanced double-attention fusion for image captioning

Image captioning has received significant attention in the cross-modal field in which spatial and channel attentions play a crucial role. However, such attention-based approaches ignore two issues: (1) errors or noise in the channel feature map amplifies in the spatial feature map, leading to a lower model reliability; (2) image spatial feature and channel feature provide different contributions to the prediction both function words (e.g., "in", "out" and "on") and notional words (e.g., "girl", "teddy" and "bear"). To alleviate the above issues, in this paper we propose the Dynamic-Balanced Double-Attention Fusion (DBDAF) for image captioning task that novelly exploits the attention variation and enhances the overall performance of the model. Technically, DBDAF first integrates a parallel Double Attention Network (DAN) in which channel attention is capitalized on as a supplement to the region attention, enhancing the model reliability. Then, a attention variation based Balancing Attention Fusion Mechanism (BAFM) module is devised. When predicting function words and notional words, BAFM makes a dynamic balance between channel attention and region attention based on attention variation. Moreover, to achieve the richer image description, we further devise a Doubly Stochastic Regularization (DSR) penalty and integrate it into the model loss function. Such DSR makes the model equally focus on every pixel and every channel in generating entire sentence. Extensive experiments on the three typical datasets show our DBDAF outperforms the related end-to-end leading approaches clearly. More remarkably, DBDAF achieves 1.04% and 1.75% improvement in terms of BLEU4 and CIDEr on the MSCOCO datasets.
TSadv: Black-box adversarial attack on time series with local perturbations

Deep neural networks (DNNs) for time series classification have potential security concerns due to their vulnerability to adversarial attacks. Previous work that perturbs time series globally requires gradient information to generate adversarial examples, leading to being perceived easily. In this paper, we propose a gradient-free black-box method called TSadv to attack DNNs with local perturbations. First, we formalize the attack as a constrained optimization problem solved by a differential evolution algorithm without any inner information of the target model. Second, with the assumption that time series shapelets provide more discriminative information between different classes, the range of perturbations is designed based on their intervals. Experimental results show that our method can effectively attack DNNs on time series datasets that have potential security concerns and generate imperceptible adversarial samples flexibly. Besides, our approach decreases the mean squared error by approximately two orders of magnitude compared with the state-of-the-art method while retaining competitive attacking success rates.
Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy

Abstract Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original selection method, and add chaotic local search with selecting chaotic map based on memory to further improve the search performance of the algorithm. A chaotic wind driven optimization with fitness-distance balance strategy is proposed, called CFDBWDO. In the experimental section, we find the optimal parameter settings for the proposed algorithm. To verify the effect of the algorithm, we conduct comparative experiments on the CEC 2017 benchmark functions. The experimental results denote that the proposed algorithm has superior performance. Compared with WDO, CFDBWDO can gradually converge in function optimization. We further verify the practicality of the proposed algorithm with six real-world optimization problems, and the obtained results are all better than other algorithms.
Application of an Improved DCGAN for Image Generation

With the rapid development of deep learning, image generation technology has become one of the current hot research areas. A deep convolutional generative adversarial network (DCGAN) can better adapt to complex image distributions than other methods. In this paper, based on a traditional generative adversarial networks (GANs) image generation model, first, the fully connected layer of the DCGAN is further improved. To solve the problem of gradient disappearance in GANs, the activation functions of all layers of the discriminator are LeakyReLU functions, the output layer of the generator uses the Tanh activation function, and the other layers use ReLU. Second, the improved DCGAN model is verified on the MNIST dataset, and simple initial fraction (ISs) and complex initial fraction (ISc) indexes are established from the two aspects of image quality and image generation diversity, respectively. Finally, through a comparison of the two groups of experiments, it is found that the quality of images generated by the DCGAN model constructed in this paper is 2.02 times higher than that of the GANs model, and the diversity of the images generated by the DCGAN is 1.55 times higher than that of GANs. The results show that the improved DCGAN model can solve the problem of low-quality images being generated by the GANs and achieve good results.
Adjustable driving force based particle swarm optimization algorithm

Particle swarm optimization algorithm (PSO) is a popular optimizer, in which each particle selects its learning exemplars relying on their fitness. Thus, the search process of each particle can be seen as driven by a fitness-based force. Intuitively, the driving force is conducive to the optimizing process. However, it may bring a premature convergence of a population. In this work, a novelty-based driving force is put forward to overcome deficiencies of the fitness-based driving force. In the new proposed adjustable driving force based PSO, named as ADFPSO, two types of exemplars respectively with high fitness and high novelty are saved in two archives. In each generation, a particle respectively chooses two exemplars from the two archives to update its velocity. In addition, three time-varying parameters are introduced to adjust the particle's learning weights for the two exemplars aiming to satisfy distinct requirements of different evolution stages. Comprehensive properties of ADFPSO are extensively testified by a set of experiments, in which nine PSO variants are adopted as peer algorithms and two CEC test suites are selected as optimization problems. Moreover, distinct characteristics of the proposed novelty-based driving force are also analyzed based on a few experiments.
Machine Learning Based Healthcare Chatbot

Health care is very significant for you to live a well and healthy life. Often people do not know all the treatments or symptoms associated with a particular disease. For young people, users with the problem go to the hospital in person for a laborious test and it is very problematic to get a doctor's appointment for any health problems, and managing phone calls. These types of problems can be solved by using Healthcare/Medicinal Chatbot by providing appropriate guidance regarding healthy living. The notion is to create a medical chatbot using Neural Networks that can provide with the info and diagnose the disease and deliver basic information about the disease and when and where to consult a doctor. The effectiveness of medical chatbots rest on Natural language processing technique that helps users post their concerns about any disease and also their health. The user can ask any private question related to health care via chatbot without being actually present at the clinic or hospital. This will help reduce the cost of health care and improve access to medical information through medical chat-bot. The program development plan is to analyze customer feelings.
Facial Emotion Recognition Using Keras and CNN

Deep Learning is penetrating most of the recognition and classification tasks performed through a computer or machine. Human facial emotion recognition plays an important and vital role in the interpersonal relationship. Deep learning (DL) based emotion detection gives much better and exact results than traditional methods with image processing. It has been recognized for a many year and it is a vital topic in the fields of computer vision and machine learning. Artificial Intelligence (AI) system is capable to emotion discovery through facial expressions. By using Neural Network classifier training, five kinds of different emotional orders are attained from pics. Through this we proposed a Computer Vision (CV) based deep learning architecture for emotion detection from images. We collect the datasets from social media platforms and various websites. In this dataset, data will be presented in the form of image type with some facial expressions. The performance of the proposed method is evaluated using datasets for restaurants which are collected from different social media platforms and websites. Our aim is to understand the emotions of different people through their expressions.
Phishing Website Detection Using Machine Learning

Phishing is an internet scam in which an attacker sends out fake messages that look to come from a trusted source. A URL or file will be included in the mail, which when clicked will steal personal information or infect a computer with a virus. Traditionally, phishing attempts were carried out through wide-scale spam campaigns that targeted broad groups of people indiscriminately. The goal was to get as many people to click on a link or open an infected file as possible. There are various approaches to detect this type of attack. One of the approaches is machine learning. The URL’s received by the user will be given input to the machine learning model then the algorithm will process the input and display the output whether it is phishing or legitimate. There are various ML algorithms like SVM, Neural Networks, Random Forest, Decision Tree, XG boost etc. that can be used to classify these URLs. The proposed approach deals with the Random Forest, Decision Tree classifiers. The proposed approach effectively classified the Phishing and Legitimate URLs with an accuracy of 87.0% and 82.4% for Random Forest and decision tree classifiers respectively.
A Comparative Review of Expert Systems, Recommender Systems, and Explainable AI

Previously Expert Systems (ES) dominated Artificial Intelligence (AI) applications and various ES were developed in multiple domains. However, due to knowledge acquisition bottlenecks, these systems fell out of use. With the rise in Machine Learning (ML) and Deep Learning (DL) approaches, another category of systems called Recommender Systems (RS) is now developed for various application domains. As ML/DL systems acted like black boxes, explainable AI (XAI) came into the picture to provide explanations for the recommendations or predictions made. In this paper, we review the architectural similarities and differences between these three approaches along with applications and future directions. It is important to study these to predict the future of RS and any possible resurgence of ES, developments in XAI and application domains.
Novel Decentralized Security Architecture for the Centralized Storage System in Hadoop using Blockchain Technology

Big Data is huge in volume, diverse in information, and growing at flourishing rates. The major distributed file systems in the current market in Big Data Analysis includes Apache Hadoop, Storm, Cassandra, Flink, Cloudera, and many more. Hadoop is an open-source framework divided into Hadoop Distributed File System (HDFS) and Map-Reduce. Hadoop plays a leading role in storing and processing Big Data in contemporary society as it is cost- effective and can manage large volumes of data in low-cost commodity hardware. HDFS is a type of Data Warehouse which is scalable and has fast access to the information. Metadata is the information about the data, such as which block is storing on what datanode, how many replications are that particular block has, and on which datanodes those replications reside. In HDFS, this metadata is stored at a fixed place in namenode, and attackers can access the metadata and modify it without notice. Also, the metadata is mutable, which means that the attacker can erase his presence easily. To resolve this issue, in this paper we have provided a mechanism using blockchain technology that follows a decentralized architecture against the centralized architecture followed by HDFS. Hyperledger Fabric (HLF) is the blockchain proposed to be effective and trusted for such a purpose. HLF is a private blockchain with a distributed immutable ledger. The metadata will be stored in the ledger. If an attacker tries to modify the data, he cannot erase his presence as the ledger is immutable, unlike HDFS. Further, the work proposed in this paper can be extended in real-time HDFS with the secure ledger and multiple nodes.
A Comprehensive Survey on Ensemble Methods

Imbalance dataset is one of the challenge in machine learning to predict the correct class and one state of art solution is Ensemble method. Ensemble method predicts the correct class by combining the predictions of several models rather than prediction based on a particular model. The objective of this paper is to review the traditional ensemble methods like Bagging, Boosting and Stacking generalization and their use to handle the current challenges of the imbalanced dataset. We also present and compare of different modification of these traditional technique to address the limitations of these methods like low diversification in Bagging and overfitting in Boosting. We discuss some new directions in the field of classifier ensembles based on a range of recently published studies. A variety of prior theoretical works have been reviewed in the paper in order to provide a deeper insight into the ensembles themselves.
A Semi-Supervised Modulation Identification in MIMO Systems: A Deep Learning Strategy

Accurate modulation identification of the received signals is undoubtedly a central component in multiple-input multiple-output (MIMO) communication systems, facilitating the demodulation task.This study presents a flexible and semi-supervised deep learning-driven strategy for automatic modulation identification.To this end, the multiclass classification problem is treated as multiple binary discrimination problems to address modulation identification challenges.Here, we merge the features extraction ability of the Generative Adversarial Network (GAN) model and the semi-supervised anomaly detection scheme, the one-class Support Vector Machine (1SVM).Essentially, a single GAN-based 1SVM detector is trained using training data of each class, with the samples of that class as inlier and all other samples as anomalies (i.e., one-vs.-rest).The 1SVM is trained using the features learned by the GAN model.A dataset consisting of three digital modulations (i.e., BFSK, CPFSK, and PAM4) and three analog modulations (i.e., AM-DSB, AM-SSB, and WB-FM), widely used in wireless communications systems, is employed to demonstrate the performance of the considered deep learning-based methods.Compared to Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN)-based 1SVM, the conventional GAN, DBN, and RBM with softmax layer as discriminator layer, the proposed GAN-based 1SVM detector offers superior discrimination performance of modulation types by achieving an averaged accuracy of 0.951 and F1-Score of 0.954.Results also showed that the GAN-1SVM detector dominates the state-of-the-art modulation classification techniques.
A Survey of Pretrained Language Models

With the emergence of Pretrained Language Models (PLMs) and the success of large-scale PLMs such as BERT and GPT, the field of Natural Language Processing (NLP) has achieved tremendous development. Therefore, nowadays, PLMs have become an indispensable technique for solving problems in NLP. In this paper, we survey PLMs to help researchers quickly understand various PLMs and determine the appropriate ones for their specific NLP projects. Specifically, first, we brief on the main machine learning methods used by PLMs. Second, we explore early PLMs and discuss the main state-of-art PLMs. Third, we review several Chinese PLMs. Fourth, we compare the performance of some mainstream PLMs. Fifth, we outline the applications of PLMs. Finally, we give an outlook on the future development of PLMs.
Blockchain and Onion-Routing-Based Secure Message Exchange System for Edge-Enabled IIoT

M2M communication in the Industrial Internet of Things is still in its infancy as the information exchange between machines is hindered by various modern security challenges and threats. An attacker can leverage the M2M communication by exploiting it with resource exhaustion, data integrity, and injection attacks. In this article, to address the aforementioned security issues, we first employed a long short-term memory based AI model on the edge servers to classify the machines' malicious and nonmalicious message requests and forwarded them to the onion routing (OR) network. Then, to enhance the security and reliability of the conventional OR network, we have associated it with blockchain technology by incorporating two additional fields along with the original message requests, i.e., verifying token and time to live that validates the incoming message requests. Additionally, the OR network, along with blockchain, is simulated inside a discrete simulator, i.e., a shadow simulator. Finally, the performance of the proposed system is evaluated with different performance metrics, such as F1 score, precision, recall, and false-negative rate. The empirical results show that the proposed OR network outperforms the conventional OR in terms of throughput, decryption time (computationally inexpensive), and OR circuit compromised rate.