Reviewerly/exHarmony huggingface dataset:
ESB-FL: Efficient and Secure Blockchain-Based Federated Learning with Fair Payment

Federated learning is a technique that enables multiple parties to collaboratively train a model without sharing raw private data, and it is ideal for smart healthcare. However, it raises new privacy concerns due to the risk of privacy-sensitive medical data leakage. It is not until recently that the privacy-preserving FL (PPFL) has been introduced as a solution to ensure the privacy of training processes. Unfortunately, most existing PPFL schemes are highly dependent on complex cryptographic mechanisms or fail to guarantee the accuracy of training models. Besides, there has been little research on the fairness of the payment procedure in the PPFL with incentive mechanisms. To address the above concerns, we first construct an efficient non-interactive designated decryptor function encryption (NDD-FE) scheme to protect the privacy of training data while maintaining high communication performance. We then propose a blockchain-based PPFL framework with fair payment for medical image detection, namely ESB-FL, by combining the NDD-FE and an elaborately designed blockchain. ESB-FL not only inherits the characteristics of the NDD-FE scheme, but it also ensures the interests of each participant. We finally conduct extensive security analysis and experiments to show that our new framework has enhanced security, good accuracy, and high efficiency.
New ETL Process for a Smart Approach of Data Migration from Relational System to MongoDB System

Today, data is experiencing a real explosion in terms of quantity and nature. They also play a very important role in reading the past, managing the present, and planning for the future. To this end, organizations consider them a treasure and are always looking for a veritable way to manage and exploit them. And since the old data management system has some weaknesses compared to Bigdata, and designers have designed systems called NoSQL to overcome these weaknesses, there is a critical need for migrating data to the new NoSQL system, to keep their old data and take advantage of the power of NoSQL systems. To meet this need, several approaches have been developed by researchers to ensure this migration. The problem is that these approaches transform data, structures, or both. These approaches at best mimic a relational database with its limitations and constraints in another NoSQL environment, which loses much of its efficiency when applying relational processing in the migration result database. In this article, we will develop a smart approach that aims to migrate the three essential parts of relational databases: the first is data, the second is structure, and the third is the semantic component, using an ETL to be defined. The solution respects the analytical part of the relational systems that we have developed and the transformations of the structure and semantics of the relational system towards the NoSQL system, to develop our model of our ETL, which consists of three phases: data extraction, transforming this data, and uploading it to the destination system.
The Spread of Cultural Heritage in Second Life: Case Study Amiais

The possibility of transposing a rooted place into a virtual world makes it possible to promote the heritage and cultural heritage of a respective place. Through the virtual approach, a connection to the past and present is generated, as well as a continuity to the future, in which anyone interested in getting to know a territory can do so easily. In this context the research project LOCUS – playfuL cOnneCted rUral territorieS were created with the intention of promoting the cultural heritage of the Village of Amiais in the Second Life environment for use by residents of the environment itself, stakeholders, and visitors with an interest in playing. The objective of the article is to contextualize how the transposition of the physical territory will be developed in a 3D virtual environment, being interactivity and playfulness key concepts for the creation of gameplay in an environment in which there are no goals or rules. The methodology used in the article will be the analysis of what is being implemented in Second Life, namely at the level of architectural transposition and what is planned to be developed regarding the interactivity of objects and Non-Player Character (NPC’s), as well as the playability of the environment.
A New Modified MD5-224 Bits Hash Function and an Efficient Message Authentication Code Based on Quasigroups

In this paper, we have proposedUmesh Kumar V. Ch. Venkaiah (i) a hash function and (ii) an efficient message authentication code based on quasigroup. We refer to these as QGMD5 and QGMAC, respectively. The proposed new hash function QGMD5 is an extended version of MD5 that uses an optimal quasigroup along with two operations named as QGExp and QGComp. The operations quasigroup expansion (QGExp) and the quasigroup compression (QGComp) are also defined in this paper. QGMAC is designed using the proposed hash function QGMD5 and a quasigroup of order 256 as the secret key. The security of QGMD5 is analyzed by comparing it with both the MD5 and the SHA-244. It is found that the proposed QGMD5 hash function is more secure. Also, QGMAC is analyzed against the brute-force attack. It is resistant to this attack because of the exponential number of quasigroups of its order. It is also analyzed for the forgery attack, and it is found to be resistant. In addition, we compared the performance of the proposed hash function to that of the existing MD5 and SHA-224. Similarly, the performance of the proposed QGMAC is compared with that of the existing HMAC-MD5 and HMAC-SHA-224. The results show that the proposed QGMD5 would take around 2 $$\mu s$$ additional execution time from that of MD5 but not more than SHA-224, while QGMAC always takes less time than that of both the HMAC-MD5 and the HMAC-SHA-224. So, our schemes can be deployed in all the applications of hash functions, such as in blockchain and for verifying the integrity of messages.
LEAST: The Smart Grocery App

This app is going to be a smart assistant for your grocery shopping lists. The app is going to monitor the user's needs and the frequency of the product he buys. Furthermore, it will also check if the following product has expired, such as milk, and suggest it to the user. For convenience, we have introduced the functionality of image processing with which users have to scan the product’s barcode, and it will be added to the list. A smart suggestion system will also be there that will recommend the items he would be missing. This will make a user’s life hassle-free and save his time remembering what to buy and creating a handmade list, which he might lose.
Design and Verification of a Smart Home Management System for Making a Smart Home Composable and Adjustable by the Elderly

As the aging population continues to increase globally, the development of methods to support the independent living of the elderly is becoming increasingly essential. Although smart home technology is expected to be used to support independent living in the homes of the elderly, the barriers to introducing this technology into the homes of elderly people are high. To support the continuation of independent living tailored to each elderly person, we aim to realize a system that can be composed and adjusted by the elderly themselves to turn their homes into smart homes using an easy-to-understand framework and an age-friendly interface that visualizes the framework. In this study, we developed a simple system described by a combination of trigger and action and a straightforward iPad configuration application. To evaluate the usability of the developed system, we performed an experiment in which young people experienced the configuration and adjustment of the system in a simulated environment. The results showed that the average time required for setting up the system was approximately 2 min, and the average of the system usability scale to evaluate the configuration system was 78.5, which was sufficiently high, indicating that the system was acceptable. In the future, we will verify whether this system can be accepted by the elderly as well.
Knowledge-Based Dialogue System for the Ageing Support on Daily Activities

With the increasing digitalization of society, we need to use a wide range of digitalized services in our daily activities such as searching for events in a calendar, checking the weather forecast, receiving guidance for completing certain tasks or recommendations for certain topics. Assistance for digital services is often needed, and particularly in the ageing stages, support for these tasks from a coach can become valuable. We introduce our work on a dialogue system that is part of a digital coach providing interactive support for elder adults in their daily activities. The work centers on using knowledge graphs to improve coaching interventions and is part of a larger project that focuses on supporting elder people and their healthy active living. Knowledge graphs are models of the domain content, defined by the domain experts, and they are used in the dialogue system to understand the content of the user utterances and to generate appropriate system responses. The dialogue coach can thus personalize conversations with the elder users and provide empathic and informative responses.
Blockchain in Dubai: Toward a Sustainable Digital Future

As governments across the world examine blockchain technology, there are growing concerns about how to implement, enforce, and maximize the benefits of the technology. Many countries have to do extensive research and analysis before coming up with proposals. The future of the Emirates Economy is getting less foggy as COVID-19 pushes many government sectors to dip their toes in digital blockchains. It is just a matter of time until blockchain technology is fully implemented in both the public and private sectors. This chapter’s purpose is to first understand what is blockchain? Second, to explore blockchain technology in Dubai, and third, to describe the sustainable aspects generated by blockchain by looking at different projects implemented in Dubai. The paper summarizes several measures for implementing, promoting, and regulating the advancement of blockchain technology taking the example of Dubai. Finance, supply chain, digital identity, energy, healthcare, real estate, transportation, and crowdfunding are only a few industries that have well-embraced blockchain technology. This chapter uses an exploratory approach by reviewing past studies and literature reviews on the topic of blockchain in Dubai.
Multi-level Wavelet Packet Prediction for Image Super-Resolution

Convolutional neural network (CNN) has made great progress on image super-resolution (SR) tasks. However, most SR models work on spatial domain and try to reconstruct context pixels. In the paper, we present a multi-level wavelet packet CNN (MWPCNN), which enhances the performance of CNN in SR tasks through residual-learning, in which residual blocks and wavelet residuals are used in the middle layer and last layer to predict wavelet coefficients. With the modified Deep Wavelet Super-Resolution (DWSR) architecture, wavelet packet transform is introduced to mine the advantages of wavelet packet transform, which makes full use of knowledge embedded in different levels from the wavelet spectrum. A key benefit of such an introduction is that it has an active influence on the image reconstruction performance, especially for reconstructing more detailed image content. Experiments show that MWPCNN is effective for SR tasks and produces better visual results than state-of-the-art methods.
Joint Feature and Labeling Function Adaptation for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Although having achieved remarkable progress, most existing methods only focus on learning domain-invariant features and achieving a small source error. They ignore the discrepancy between labeling functions which will also cause discrepancy across domains. Inspired by this observation, we propose a novel method to simultaneously perform feature adaptation and labeling function adaptation. Specifically, for the feature adaptation, a domain discriminator is trained to reduce the discrepancy between feature distributions across domains. For the labeling function adaptation, we introduce a target predictor and a predictor discriminator. The target predictor is trained on target samples with pseudo-labels. The predictor discriminator is a novel component and is trained to distinguish whether the prediction output is from the source or the target predictor while the feature extractor and the label predictors try to confuse the predictor discriminator in an adversarial manner. Additionally, the intrinsic characteristics of the target domain are expected to be exploited thanks to the task-specific training. Comprehensive experiments are conducted and results validate the effectiveness of labeling function adaptation and demonstrate that our approach outperforms state-of-the-art methods.
Few-Shot Knowledge Graph Entity Typing

Knowledge graph entity typing, which is an important way to complete knowledge graphs (KGs), aims at predicting the associating type of certain given entities without any external knowledge. However, previous methods suppose that many (entity, entity type) pairs (ETPs) can be obtained for each entity type, performing poorly on entity types that only have a few associative entities and do not fully utilize the internal information in KGs. In this work, we propose a novel model named Meta Entity Typing (MET) for few-shot knowledge graph entity typing. In MET, we achieve knowledge graph entity typing by meta-learning with three sub-tasks formed by the hierarchical entity type tree in its meta-training stage. In this way, MET can focus on transferring type-specific meta information to learn the most important knowledge for entity typing. Besides, to fully employ the internal information in KGs given limited ETPs, inspired by Factorization Machines, we design a novel Relation To Relation Graph Convolutional Networks (R2R-GCN), in which we consider different relation combinations could have distinct influence on its corresponding entity, R2R-GCN can explicitly model the interactions between different relations. Empirically, our model achieves state-of-the-art results on few-shot entity typing KG benchmarks.
Spontaneous Detection of Potholes and Humps

Roads are the major means of transportation and it supports the nation’s economy only if they are well maintained. It is necessary to identify holes and potholes so the accidents can be avoided and the damage caused to the vehicle is less. It also contributes in saving fuel. Here is a simple and effective solution regarding the problem of accidents by detection of potholes and humps and help drivers. Detection of potholes will be done by image processing technique and humps would be detected by ultrasonic sensor. Raspberry Pi is the controlling device. Wi-Fi will be used to acquire geographical position of potholes and will be send to the authority to take appropriate measures.
Didactic Effectiveness of Distance Learning in the Context of Digitalization of Higher Education

The paper focuses on one of the topical aspects of the development of digital didactics in higher education, which is determined by the need to identify the pedagogical conditions for improving the use of tools and opportunities of distance learning. The paper aims to identify the pedagogical conditions of the didactic effectiveness of an online lesson in the current educational processes of universities. The study involved students and teachers of the Department of Management at the Vladimir Branch of the Russian Presidential Academy of National Economy and Public Administration (training program 38.03.04 “State and municipal administration”). The research uses a systematic approach, which involves a set of research methods such as survey, observation, comparison, analysis, and the methods of modeling and descriptive statistics. Implicating these methods, the authors obtained data for a comparative analysis of the activity of university students in various academic disciplines during distance learning. The authors determined the optimal duration of the online class in the conditions of digital education. It is proved that the lesson should be built with the consideration of students’ activity peaks, which are mainly in the first half of the lesson. The authors found that the most important condition for the effectiveness of distance learning is the activity of students in the learning process. The research novelty lies in obtaining the quantitative and qualitative characteristics of the online lesson as a didactic unit of distance learning. The results allow the authors to draw conclusions that may be of practical relevance for organizing the educational process in higher education.
Research on Immersive Scene Modeling Language Design of Virtual Reality Animation Based on Affordance Design

With the development of virtual reality technology and the popularization of its application, it has profoundly influenced the development of animation art. Virtual reality technology itself has the characteristics of immersion, it can create a kind of “immersive” feeling, for the innovative development of animation provides a good opportunity. However, with the development of technology, the blind following of technology presents the characteristics of homogeneity, making a large number of works fall into the “uncanny valley” effect, which makes it difficult to cause empathy. If not guided, it may neglect the expression of human thought, character and emotion. This paper discusses the cognitive concept of availability, physical availability, availability of emotions to create immersive virtual reality animation scene instructional methods, combined with the characteristics of virtual reality technology multimodal perception, try to use the personalized modelling intangible ideographic language, time, space, sound source localization and “feeling” trigger emotional issues. These four approaches attempt to solve the problem of the construction of immersive virtual technology space, as well as the homogenization caused by technological development, and the “uncanny valley” effect.
IoT enabled machine learning framework for social media content-based recommendation system

Analysing huge volume of data from the social media tweets on product reviews provides a better understanding of any product. Exploring customer opinions from tweets is helpful to find the strengths and weaknesses of different products and features. There are several studies on product recommendations from Twitter product reviews. In this paper, Internet of Things-based two level product recommendation framework (TLPRF) is proposed to efficiently handle large amount of Twitter users' product reviews data. TLPRF consists of a Raspberry Pi microcomputer as an IoT mining machine and it is programmed to generate a feature level opinion summary. Feature level opinion is found to be useful in accomplishing the product ranking. Based on the customer interest in the product purchase request, a normalised ranking of each matching product is calculated from the feature-wise opinion summary and the product with maximum ranked score is recommended to Twitter user. The proposed TLPRF is found to be superior to similar other approaches in terms of accuracy, precision, recall and f-measure.
Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in燬mart City

In recent years, Smart City Infrastructures (SCI) have become familiar whereas intelligent models have been designed to improve the quality of living in smart cities. Simultaneously, anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of pedestrians. The increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions, a laborious process. In this background, Deep Learning (DL) models can be used in the detection of anomalies found through video surveillance systems. The current research paper develops an Internet of Things Assisted Deep Learning Enabled Anomaly Detection Technique for Smart City Infrastructures, named (IoTAD-SCI) technique. The aim of the proposed IoTAD-SCI technique is to mainly identify the existence of anomalies in smart city environment. Besides, IoTAD-SCI technique involves Deep Consensus Network (DCN) model design to detect the anomalies in input video frames. In addition, Arithmetic Optimization Algorithm (AOA) is executed to tune the hyperparameters of the DCN model. Moreover, ID3 classifier is also utilized to classify the identified objects in different classes. The experimental analysis was conducted for the proposed IoTAD-SCI technique upon benchmark UCSD anomaly detection dataset and the results were inspected under different measures. The simulation results infer the superiority of the proposed IoTAD-SCI technique under different metrics.
Gabor Wavelets in Face Recognition and Its Applications

Face recognition has become an advanced area in the field of recognition and has various applications especially in biometrics, forensic investigations, smart advertising, national database for identity cards etc. Various methods and algorithms are available for face recognition using image processing, machine vision and deep learning. In image processing, wavelets transform is one of the effective methods as they are capable of extracting both spatial and frequency information of the given signal and is a substitute of Fourier transforms in many applications of signal processing used in machine vision, speech analysis etc.. Among all the wavelet transforms the most efficient is Gabor wavelet transform that is widely used in research done in the field of image processingas it has certain important mathematical properties. Gaborwavelet transform is also used as they yield distortion tolerance space for various recognitions including face recognition and pattern recognition. This paper addresses the applications of Gabor wavelets in face recognition along with face reconstruction, iris recognition, and corner detection etc.
Would Blockchain Disrupt the Accounting and Auditing Professions? An Exploratory Study in the UAE

The accounting and auditing profession look to be in the middle of a technological transformation. The continuing increasing stakeholder demands for information, the increasing digitalizing of data, and the integration of technology into accounting procedures are giving a new shape to the profession. Nowadays, with the development of the blockchain, an additional change is imposed on the accounting landscape. This will have a potential effect on the audit and attestation profession. Blockchain technology offers a radically new way to record, process, and store business transactions and information and, more importantly, has the potential to fundamentally change the landscape of business ecosystems and reshape the accounting and auditing profession. In this research paper, we explore the opportunities and challenges of the new technology on the United Arab Emirates’ (UAE) accounting and auditing profession.