Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Older Adult’s Voice Assistant Use for Health Information Seeking

Although voice assistants are increasingly being adopted by older adults, we lack empirical research on how they interact with these devices for health information seeking. Also, prior work shows how voice assistant responses can provide misleading or inaccurate information and be harmful particularly in health contexts. Because of increased health needs while aging, this paper studies older adult’s (ages 65+) health-related voice assistant interactions. Motivated by a lack of empirical evidence for how older adults approach information seeking with emerging technologies, we first conducted a survey of n = 201 older adults to understand how they engage voice assistants compared to a range of offline and digital sources for health information seeking. Findings show how voice assistants were used for confirmatory health queries, with users showing signs of distrust. As much prior work focuses on perceptions of voice assistant use, we conducted scenario-based interviews with n = 35 older adults to study health-related voice assistant behavior. In interviews, participants engaged with different health topics (flu, migraine, high blood pressure) and scenario types (symptom-driven, behavior-driven) using a voice assistant. Findings show how conversational and human-like expectations with voice assistants lead to information breakdowns between the older adult and voice assistant. This paper contributes a nuanced query-level analysis of older adults’ voice-based health information seeking behaviors. Further, data provide evidence for how query reformulation happens with complex topics in voice-based information seeking. We use our findings to discuss how voice interfaces can better support older adults’ health information seeking behaviors and expectations.
Effective and Secure Transmission of Health Information Using Advanced Morphological Component Analysis and Image Hiding

The using morphological component analysis, deep learning, and steganography, this study examines the secure transmission, identification, and validation of textual pictures via this Internet of Things-based channel. To extract characteristics from text-based pictures, morphological component analysis is utilised. Each of these traits has a distinct morphological component. Without losing visual quality, the morphological component technique lowers duplicative but also uncorrelated characteristics, as well as the size in bytes of a text-based picture. The capacity to obtain full text-based picture texture characteristics, rather than relying on a single spatial texture feature, is considered to be a significant aspect of the proposed approach. Before being transferred over Internet of Things-based networks, the morphological portions of a concealed text-based picture have been further divided and generally implanted interested in the least significant pieces of the cover pixels using spatial steganography. To retrieve the message from stored-images, the hidden key of an embedding algorithm can be exchanged with a text recovery approach on the recipient's side. To recognise an embedded text-based message, a hybrid convolution neural network technique was eventually employed. Aside from that an optimization approach is utilised to improve the hybrid convolution neural network’s performance (HCNN’s).
Secure and Efficient Blockchain-Assisted Authentication for Edge-Integrated Internet-of-Vehicles

Edge-Integrated Internet-of-Vehicles (IoV) sinks service to edge nodes, which responses quickly to vehicles' requests and alleviates the burden of cloud servers. In such scenario, the entities of IoV need to be mutually authenticated since potential attacks can impersonate edge nodes to send false instructions to vehicles, or impersonate legitimate service subscribers to get free-ride IoV services. Furthermore, due to the high mobility characteristics of vehicles, frequent authentication is required between vehicles and edge nodes. We hope that the authentication process can be conducted efficiently to ensure the continuous service. Existing works cannot balance the security and efficiency well. This is still an issue worthy of discussion. In this article, we propose SEA, a secure and efficient blockchain-assisted authentication scheme for IoV. SEA achieves mutual authentication among vehicles, edge nodes and cloud servers. Specifically, the cloud server is only involved when vehicles are initially authenticated. And edge nodes realize the authentication of vehicles by querying the authentication result recorded by cloud on the blockchain, which significantly reduces the cryptographic computation overhead and eliminates network communication delay. Besides, session keys between any two entities involved are negotiated, which can secure sensitive data of vehicles. Extensive experiments have been conducted to show the security and efficiency of SEA.
Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness

Digital currencies such as Ethereum and XRP allow for all transactions to be carried out online. To emphasize the decentralized nature of fiat currency, we can refer, for example, to the fact that all virtual currency users may access services without third-party involvement. Cryptocurrency price swings are non-stationary and highly erratic, similarly to the price changes of conventional stocks. Owing to the appeal of cryptocurrencies, both investors and researchers have paid more attention to cryptocurrency price forecasts. With the rise of deep learning, cryptocurrency forecasting has gained great importance. In this study, we present a long short-term memory (LSTM) algorithm that can be used to forecast the values of four types of cryptocurrencies: AMP, Ethereum, Electro-Optical System, and XRP. Mean square error (MSE), root mean square error (RMSE), and normalize root mean square error (NRMSE) analyses were used to evaluate the LSTM model. The findings obtained from these models showed that the LSTM algorithm had superior performance in predicting all forms of cryptocurrencies. Thus, it can be regarded as the most effective algorithm. The LSTM model provided promising and accurate forecasts for all cryptocurrencies. The model was applied to forecast the future closing prices of cryptocurrencies over a period of 180 days. The Pearson correlation metric was applied to assess the correlation between the prediction and target values in the training and testing processes. The LSTM algorithm achieved the highest correlation values in training (R = 96.73%) and in testing (96.09%) in predicting XRP currency prices. Cryptocurrency prices could be accurately predicted using the established LSTM model, which displayed highly efficient performance. The relevance of applying these models is that they may have huge repercussions for the economy by assisting investors and traders in identifying trends in the sales and purchases of different types of cryptocurrencies. The results of the LSTM model were compared with those of existing systems. The results of this study demonstrate that the proposed model showed superior accuracy based on the low prediction errors of the proposed system.
Floating photovoltaic site selection using fuzzy rough numbers based LAAW and RAFSI model

This study presents a quantitative methodology for Floating Photovoltaic (FPV) power plant site selection in Turkey using Geographical Information Systems (GIS) and fuzzy sets, which is one of the Multi-Criteria Decision Making (MCDM) methods. In this study, we propose a new hybrid framework which combines fuzzy rough number (FRN) based decision making model including LAAW (Logarithmic Additive Assessment of the Weight coefficients) and RAFSI (Ranking of Alternatives through Functional mapping of criterion subintervals into a Single Interval). The fuzzy rough number is applied for handling the uncertainty and inaccuracy of experts’ opinions in the evaluation process. Firstly, FRN based LAAW method is used to determine the weighting coefficients of the criteria. Secondly, FRN based RAFSI method is used to rank the alternatives. The proposed decision making model is applied to determine feasible site for Floating Photovoltaic (FPV) system in Southern part of Turkey. Out of the five alternative sites, Manavgat - Antalya is concluded to be the most suitable site, and the second-best alternative is Göksun - Karaman. The results show the rationality and applicability of the proposed model.
ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection

With the advent of image generative technologies, there is a huge growth in the development of facial manipulation techniques that allow people to easily modify media data like videos and images by changing the identity or facial expression of the target person with another person’s face. Colloquially, these manipulated videos and images are termed “deepfakes”. As a result, every piece of content in digital media comes with a question — is this authentic? Hence, there is an unprecedented need for a competent deepfakes detection method. The rapid changes in forging methods make this a very challenging task and thus generalization of the detection methods is also of utmost required. However, the generalization strengths of the prevailing deepfakes detection methods are not satisfactory. In other words, these models perform well when trained and tested on the same dataset but fail to perform satisfactorily when models are trained on one dataset and tested on another. The most modern deep learning aided deepfakes detection techniques looked for a consistent pattern among the leftover artifacts in specific facial regions of the target face rather than the entire face. To this end, we propose a Vision Transformer with Xception Network (ViXNet) to learn the consistency of these almost imperceptible artifacts left by deepfaking methods on the entire facial region. The ViXNet comprises two branches — one tries to learn inconsistencies among local face region specifics by combining patch-wise self-attention module and vision transformer, and the other generates global spatial features using a deep convolutional neural network. To assess the performance of ViXNet, we evaluate it using two different experimental setups — intra-dataset and inter-dataset when using three standard deepfakes video datasets, namely FaceForensics++, and Celeb-DF (V2) and one deepfakes image dataset called Deepfakes. We have attained 98.57% (83.60%), 99.26% (74.78%), and 98.93% (75.13%) AUC scores using intra(inter)-dataset experimental setups on FaceForensics++, Celeb-DF (V2), and Deepfakes datasets respectively. Additionally, we have evaluated ViXNet on the Deepfake Detection Challenge (DFDC) dataset and we have obtained 86.32% AUC score and 79.06% F1-score on the said dataset. Performances of the proposed model are comparable to state-of-the-art methods. Besides, the obtained results ensure the robustness and the generalization ability of the proposed model.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).
Impact of convolutional neural network and FastText embedding on text classification

Abstract Efficient word representation techniques (word embeddings) with modern machine learning models have shown reasonable improvement on automatic text classification tasks. However, the effectiveness of such techniques has not been evaluated yet in terms of insufficient word vector representation for training. Convolutional Neural Network has achieved significant results in pattern recognition, image analysis, and text classification. This study investigates the application of the CNN model on text classification problems by experimentation and analysis. We trained our classification model with a prominent word embedding generation model, Fast Text on publically available datasets, six benchmark datasets including Ag News, Amazon Full and Polarity, Yahoo Question Answer, Yelp Full, and Polarity. Furthermore, the proposed model has been tested on the Twitter US airlines non-benchmark dataset as well. The analysis indicates that using Fast Text as word embedding is a very promising approach.
Chatbots in customer service: Their relevance and impact on service quality

Chatbots are increasingly finding their way into e-commerce and e-services, as their implementation opens up promising opportunities to improve customer service. The present paper examines chatbots in this context, elaborating on their functional aspects that are rapidly leading to significant improvements in service quality. First, based on a literature review of recent publications in this field, an overview of their key features and functionalities underlining the relevance of chatbots for customer service is provided. Second, a further contribution is made by introducing two categories of chatbots’ objectives based on their functional dedication, i.e. “improvement of service performance” and “fulfillment of customer’s expectations”. The considered chatbots’ customer-related functions are interaction, entertainment, problem-solving, trendiness, and customization. The chatbot categories are discussed in detail. Their positive influence on service quality, constituting the chatbots’ functional goal, as well as the potential of chatbots in customer service are pointed out.
Analysis of Blockchain in the Healthcare Sector: Application and Issues

The emergence of blockchain technology makes it possible to address disparate distributed system security concerns in formerly ridiculous practices. A key factor of this ability is the decentralization of the symmetrically distributed ledgers of blockchain. Such decentralization has replaced several security functionalities of centralized authority with the use of cryptographic systems. That is, public or asymmetric cryptography is the key part of what makes blockchain technology possible. Recently, the blockchain experience introduces the chance for the healthcare field to implement these knowhows in their electronic records. This adoption supports retaining and sharing the symmetrical patient records with the appropriate alliance of hospitals and healthcare providers in a secure decentralized system, using asymmetric cryptography like hashing, digitally signed transactions, and public key infrastructure. These include specialized applications for drug tracking, applications for observing patients, or Electronic Health Records (EHR). Therefore, it is essential to notice that the principled awareness of the healthcare professionals is the leading point of the right perception ethics. In this work, we provide a thorough review of the issues and applications of utilizing blockchain in the healthcare and medical fields emphasizing the particular challenges and aspects. The study adopted a systematic review of secondary literature in answering the research question. Specifically, this paper aims to investigate how blockchain technology can be applied to improve the overall performance of the healthcare sector and to explore the various challenges and concerns of the application of blockchain in the healthcare system.
A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research

The combination of class imbalance and overlap is currently one of the most challenging issues in machine learning. While seminal work focused on establishing class overlap as a complicating factor for classification tasks in imbalanced domains, ongoing research mostly concerns the study of their synergy over real-word applications. However, given the lack of a well-formulated definition and measurement of class overlap in real-world domains, especially in the presence of class imbalance, the research community has not yet reached a consensus on the characterisation of both problems. This naturally complicates the evaluation of existing approaches to address these issues simultaneously and prevents future research from moving towards the devise of specialised solutions. In this work, we advocate for a unified view of the problem of class overlap in imbalanced domains. Acknowledging class overlap as the overarching problem – since it has proven to be more harmful for classification tasks than class imbalance – we start by discussing the key concepts associated to its definition, identification, and measurement in real-world domains, while advocating for a characterisation of the problem that attends to multiple sources of complexity. We then provide an overview of existing data complexity measures and establish the link to what specific types of class overlap problems these measures cover, proposing a novel taxonomy of class overlap complexity measures. Additionally, we characterise the relationship between measures, the insights they provide, and discuss to what extent they account for class imbalance. Finally, we systematise the current body of knowledge on the topic across several branches of Machine Learning (Data Analysis, Data Preprocessing, Algorithm Design, and Meta-learning), identifying existing limitations and discussing possible lines for future research.
Current Challenges and Future Research Directions in Augmented Reality for Education

The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy.
Intelligent authentication of 5G healthcare devices: A survey

The dynamic nature of wireless links and the mobility of devices connected to the Internet of Things (IoT) over fifth-generation (5G) networks (IoT-5G), on the one hand, empowers pervasive healthcare applications. On the other hand, it allows eavesdroppers and other illegitimate actors to access secret information. Due to the poor time efficiency and high computational complexity of conventional cryptographic methods and the heterogeneous technologies used, it is easy to compromise the authentication of lightweight wearable and healthcare devices. Therefore, intelligent authentication, which relies on artificial intelligence (AI), and sufficient network resources are extremely important for securing healthcare devices connected to IoT-5G. This survey considers intelligent authentication and includes a comprehensive overview of intelligent authentication mechanisms for securing IoT-5G devices deployed in the healthcare domain. First, it presents a detailed, thoughtful, and state-of-the-art review of IoT-5G, healthcare technologies, tools, applications, research trends, challenges, opportunities, and solutions. We selected 20 technical articles from those surveyed based on their strong overlaps with IoT, 5G, healthcare, device authentication, and AI. Second, IoT-5G device authentication, radio-frequency fingerprinting, and mutual authentication are reviewed, characterized, clustered, and classified. Third, the review envisions that AI can be used to integrate the attributes of the physical layer and 5G networks to empower intelligent healthcare devices. Moreover, methods for developing intelligent authentication models using AI are presented. Finally, the future outlook and recommendations are introduced for IoT-5G healthcare applications, and recommendations for further research are presented as well. The remarkable contributions and relevance of this survey may assist the research community in understanding the research gaps and the research opportunities relating to the intelligent authentication of IoT-5G healthcare devices.
Reassessment of the potential for centralized and distributed photovoltaic power generation in China: On a prefecture-level city scale

The successful development of solar energy primarily depends on the scientific and effective evaluation of the photovoltaic power generation potential. This study re-estimated the installed potential of centralized large-scale and distributed small-scale photovoltaic power stations in 449 prefecture-level cities in China based on a geographic information system and Google Earth Engine combined with Baidu map data and related geographic information data. The factors considered in selecting the areas suitable for photovoltaic power generation were economy, terrain, environment for the centralized stations; illumination time, roof type, and shadow obstacles of the building for distributed systems. The results showed that a total area of 583 thousand km2 in China can be utilized to install solar panels, with a technical potential of approximately 55.1 TW. In particular, the technical potential of the centralized power stations was approximately 42.8 TW, primarily distributed in Naqu, Tibet and Haixi, Qinghai. The potential of the distributed systems was approximately 12.3 TW (including 4.9 TW for flat roof, 3.5 TW for pitched roof, and 3.9 TW for building facade), primarily concentrated in Yulin and Guigang, Guangxi; and Meizhou, Guangdong. The findings can clarify the spatio-temporal distribution of photovoltaic potential nationwide and provide decision support for regional planning.