Reviewerly/exHarmony huggingface dataset:
Blockchain Technology Integration in IoT and Applications

The Internet of Things (IoT) pictures an entire connected world, where things or devices are proficient to exchange a few measured data words and interrelate with additional things. This turns for a feasible digital demonstration of the existent world. Nonetheless, nearly all IoT things are simple to mistreat or compromise. Moreover, IoT devices are restricted in computation, power, and storage, so they are more vulnerable to bugs and attacks than endpoint devices like smartphones, tablets, and computers. Blockchain has remarkable interest from academics and industry because of its salient features including reduced dependencies on third parties, cryptographic security, immutability, decentralized nature, distributed nature, and anonymity. In the current scenario, blockchain with its features provides an anonymous framework for IoT. This chapter produces comprehensive knowledge of IoTs, Blockchain knowledge, security issues, Blockchain integration with IoT (BIoT), consensus, mining, message validation mechanisms, challenges, a solution, and future directions.
A Holistic View on Blockchain and Its Issues

Blockchain, the technology behind most popular cryptocurrency Bitcoin and Ethereum, has attracted wide attention recently. It is the most emerging technology that has changed the financial and non-financial transaction system. It is omnipresent. Currently, this technology is enforcing banks, industries, and countries to adopt it in their financial, industrial, and government section. Earlier, it solved the centralize and double-spending problems successfully. In this chapter, the authors present a study of blockchain security issues and its challenges as well. They divided the whole chapter into two parts. The primer part covers a holistic overview of blockchain followed by the later section that argues about basic operations, 51% attack, scalability issue, Fork, Sharding, Lightening, etc. Finally, they mention an intro about its adaptation (financial or non-financial) in our 24/7 life and collaboration with fields like IoT.
Privacy Preserving Data Mining as Proof of Useful Work

Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, the authors propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models – a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.
Comprehensive Study on Incorporation of Blockchain Technology With IoT Enterprises

Unprecedented advancement in wireless technology, storage, and computing power of portable devices with the gigabyte speed of internet connectivity enables the possibility of communication among machine to machine. IoT has a different way to connect many nodes simultaneously to store, access, and share the information to improve the quality of life by the elimination of the involvement of human. Irrespective of unlimited benefit, IoT has so many issues that arise to eclipse IoT in reality because of its centralized model. Scalability, reliability, privacy, and security challenges are rising because of the huge numbers of IoT nodes, centralized architecture, and complex networks. Centralized architecture may lead to problems like a single point of failure, single way traffic, huge infrastructure cost, privacy, security, and single source of trust. Therefore, to overcome the issues of the centralized infrastructure of the IoT, the authors diverted to decentralized infrastructure. It may be the best decision in terms of performance, reliability, security, privacy, and trust. Blockchain is an influential latest decentralization technology to decentralize computation, process management, and trust. A combination of blockchain with IoT may have the potential to solve scalability, reliability, privacy, and security issues of IoT. This chapter has an overview of some important consensus algorithms, IoT challenges, integration of the blockchain with IoT, its challenges, and future research issues of a combination of blockchain and IoT are also discussed.
A Cloud-Assisted Proxy Re-Encryption Scheme for Efficient Data Sharing Across IoT Systems

In recent years, the growth of IoT applications is rapid in nature and widespread across several domains. This tremendous growth of IoT applications leads to various security and privacy concerns. The existing security algorithms fail to provide improved security features across IoT devices due to its resource constrained nature (inability to handle a huge amount of data). In this context, the authors propose a cloud-assisted proxy re-encryption scheme for efficient data sharing across IoT systems. The proposed approach solves the root extraction problem using near-ring. This improves the security measures of the system. The security analysis of the proposed approach states that it provides improved security with lesser computational overheads.
Blockchain Technology for the Internet of Things Applications in Apparel Supply Chain Management

Adoption of the internet of things (IoT) and blockchain technology opens new opportunities of business process automation in apparel supply chain management. The IoT technology helps to capture real-time information from different aspects of garment manufacturing activities by using radio frequency identification (RFID) tags and sensors. Blockchain technology is an emerging concept of computing that enable the decentralized and immutable storage of business transactions. In combination with IoT, blockchain technology can enable a broad range of application scenarios to enhance business value and trust. This chapter presents some of the blockchain-based IoT technology applications in apparel business processes. Moreover, the chapter provides a classification of threat models, which are considered by blockchain protocols in IoT networks. Finally, the chapter provides a taxonomy and a side-by-side comparison of the state-of-the-art methods towards secure and privacy-preserving blockchain technologies concerning the blockchain model, specific security goals, performance, and limitations.
Cyber Security and Cyber Resilience for the Australian E-Health Records

Cybersecurity is a critical consideration for all users of electronic health records (EHR), particularly for patients. With the advent of Healthcare 4.0, which is based on the internet of things (IoT) and sensors, cyber resilience has become a key requirement in ensuring the protection of patient data across devices. Blockchain offers crypto-enforced security, data immutability, and smart contracts-based business logic features to all the users in the network. This study explores how blockchain can be a single digital option that can address both the cybersecurity and cyber resilience needs of EHR. The effective use lens is adopted to analyze how blockchain can be leveraged to meet cybersecurity needs while the novel use lens is adopted to analyze how blockchain can be leveraged to address cyber resilience needs originating from IoT. Based on the analysis, this study proposes two Hyperledger-based security models that contribute to individual privacy and information security needs.
Application of Technology in Healthcare

The COVID-19 pandemic has disrupted healthcare worldwide and laid several fundamental problems that will have to be tackled to ensure high-quality healthcare services. This pandemic has represented an unparalleled challenge for healthcare systems and poses an opportunity to innovate and implement new solutions. Digital transformation within healthcare organizations has started and is reshaping healthcare. Technologies such as blockchain and IoT can bring about a revolution in healthcare and help solve many of the problems associated with healthcare systems that the COVID-19 crisis has exacerbated. In this chapter, IoT and blockchain technologies were discussed, focusing on their main characteristics, integration benefits, and limitations, identifying the challenges to be addressed soon. The authors further explored its potential in describing concrete cases and possible applications for healthcare in general and specifically for COVID-19.
Design of Montgomery Multiplier – RSA Algorithm

Abstract In cryptography, during the process of encryption, we use multiplication modulo architectures (2^n- 1) and (2^n+ 1) are proposed that enable the implementation of very efficient combinational and pipelined circuits for modular arithmetic. This project is an improvisation of existing modulo multiplier architectures for higher speed and regularity. We use the Montgomery multiplier in this project for the RSA algorithm. Hence, the IDEA block cipher is represented as a high-performance modulo multiplier adder. The circuits that arise are compared qualitatively and quantitatively for the RSA algorithm.
Machine Learning approach for Software Effort Estimation using Combination of Principal Component Regression and Neural Network

Abstract For software projects that deploy vital tasks, it is difficult to estimate the effort of a project. In order to anticipate a few hours of labour effort (either time or personal) to deploy or maintain the software programme, software measurement points must be used. It is difficult to predict the behavior of an application that is engaged in software development during the initial phases of the effort. The hybrid model technique is used in this paper. It is necessary to apply Supervised Learning techniques in the Machine Learning algorithm. It is further subdivided into different types, such as linear regression, logistic regression, SVM (Support Vector Machine) algorithm, Naive Bayes algorithm, PCR(Principal Component Regression) algorithm, (Neural Network)NNET algorithm, KNN (K-Nearest Neighbour) algorithm, K-means, Random Forest algorithm, Dimensionality reduction algorithms, Gradient boosting algorithm, and Ada Boosting algorithm, to name a few examples. PCR algorithm and the nnet algorithm have been utilised for hybrid method, as shown above. Predictions from JM1/Software have been used to create this data collection. With 10886 unique instances and 18 unique traits, this is a very large amount. Metrics for evaluating this system include Mean Absolute Error (MAE), Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), Percentage of Predictive Accuracy (PRED), and R-squared. Compared to single model approaches based on machine learning algorithms techniques, the proposed hybrid using principal component regression and neural networks produced the best results, as demonstrated by the results.
EINet: camouflaged object detection with pyramid vision transformer

Camouflaged object detection (COD) is a new computer vision challenge for locating and identifying camouflaged objects in complex situations. Camouflaged objects are more similar to their surroundings than conventional objects, and their appearance in terms of size and shape is also considerably different, making accurate identification of the COD tasks difficult. As a result, we propose an enhanced identification network (EINet) to strengthen the COD task’s identification capabilities. First, the pyramid vision transformer is used as an encoder for extracting more robust multiscale features. Second, the multiple texture refinement modules are exploited to refine the multiscale features. Third, an improved neighbor and hop connection decoder is designed to produce a coarse estimation map for guiding the detailed identification of camouflaged objects backward. Finally, numerous new reverse criss-cross block attention modules that gradually recognize fine-grained features at various scales is designed to allow for the accurate recognition of camouflaged objects. Extensive experiments have been conducted on four benchmarked datasets of camouflaged objects. The results of the experiments reveal that our EINet is a powerful COD model that outperforms current state-of-the-art models.
A Fast Data Transmission Method of Vehicle-Road Cooperative Communication Based on the Clustering Algorithm

In order to improve the security and stability of data transmission in vehicle-road cooperative communication and reduce the error rate of data transmission, a fast data transmission method based on the clustering algorithm is proposed. First, the multisensor information acquisition method of the vehicle networking is used to realize data acquisition and data structure analysis of the vehicle-road cooperative communication, and a data transmission channel model for vehicle-road cooperative communication is constructed. The interference suppression in the data transmission process of the vehicle-road cooperative communication is realized by the matched filter detection algorithm. Then, according to the symbol characteristic distribution of the vehicle-road cooperative communication channel, the baud interval equalization method is used to realize the piecewise equalization adjustment of data transmission for the vehicle-road cooperative communication. With the adaptive error compensation and channel fading suppression, the K-means clustering algorithm is used to carry out the coordinated adjustment to data packets during the rapid data transmission of the vehicle-road cooperative communication. Finally, the adaptive control of data transmission for the vehicle-road cooperative communication is carried out according to the dynamic distribution characteristics of the clustering center so as to reduce the influence of channel fading and intersymbol interference caused by channel spread. The simulation results show that the bit error rate of this method is kept at about 0.05, and the data transmission rate continues to increase, most of which remain above 0.95. This method has strong anti-interference ability for the rapid data transmission of vehicle-road cooperative communication, with lower communication bit error rate, less end-to-end time delay, and higher stability and accuracy of data transmission.
Security, Privacy, and Trust Management and Performance Optimization of Blockchain

Blockchain provides innovative ideas for storing information, executing transactions, performing functions, creating trust in an open environment, etc. Even though cryptographers, mathematicians, and coders have been trying to bring the most trustable protocols to get authentication guarantee over various systems, blockchain technology is secure with no central authority in an open network system because of a large distributed network of independent users. If anyone tries to change the blockchain database, the current hash will also change, which does not match with the previous hash. In this way, blockchain creates privacy and trust in digital data by removing malleability attacks. In this chapter, security and privacy on the blockchain has been focused. The safety and privacy of blockchain are mainly engrossed on two things: firstly, uncovering few attacks suffered by blockchain systems and, secondly, putting specific and advanced proposals against such attacks.
Research on neural network algorithm in artificial intelligence recognition

With the rapid development of computer application technology, computer artificial intelligence recognition technology has also made rapid progress. Artificial intelligence was first represented by LISP language, machine theorem proof, etc., and then the intelligent medical treatment and neural network appeared, and now it realizes the popularization of AI. The purpose of this article is to explore the application of neural network algorithms in artificial intelligence recognition. Based on the classic convolutional neural network with LeNet structure, this paper constructs a reasonable convolutional neural network model by deepening and expanding, and uses it in face recognition to conduct face recognition research. LeNet is the origin of CNN, and LeNet is mainly used to identify and classify handwritten characters. This paper uses three networks on the YaleB face database for face recognition, and compares the pooling method of the maximum pooling and average pooling, the misclassification rate of the ReLU incentive function and the tanh incentive function on the three networks.YaleB is a very complete face database, divided into five subsets, ten people, and 64 images per person, including changes in illumination, expressions, and gestures. The tanh function has what is an odd function and has a soft saturation property, while the relu function has a hard saturation property. Analyze whether it can solve the problems caused by illumination factors and occlusion factors in traditional face recognition. Finally, the recognition effect is compared with the traditional face recognition method, and it is concluded that the effect of the traditional face recognition method is not as high as the misclassification rate of 0.0356 based on the convolutional neural network. The recognition method works well. Maximum pooling is also known as maximum reduced pixel sampling, and is more based on the input element map than average pooling.Through the optimization of the algorithm, the face recognition rate is improved, which helps the application and development of artificial intelligence.
Possibilities of using the stakeholder approach in the implementation of reactive control

At the modern stage development, the formation of the most effective and constructive communication between the management of the organization and key stakeholders makes it possible not only to adapt the strategic goals of the organization to the requirements of the stakeholders, but also to respond as quickly as possible to both external threats and internal changes taking place in the organization itself in the course of its development, with high efficiency to minimize risks, as well as increase the level of business reputation of the organization and the level of public confidence in its activities. Effective management of an organization is achieved by the most coordinated actions of all its representatives, and also an important element in this system is the creation of a common information base, which allows to qualitatively coordinate the interaction of the organization’s management and the main groups of stakeholders in order to assess the market position and justify all management decisions aimed at maximizing profits and minimization of risks and other financial losses. Competent interaction with stakeholders, as well as meeting their requirements, can ensure sustainable business development in the future.
Transforming Universities for a More Competent Society

Digitalization has transformed the higher education sector. It is a significant factor that shapes the future of higher education. Universities are the primary institutions that must ensure the relevancy and actuality of their curricula and provide high-quality education programs by utilizing new methods and technologies in the rapidly changing environment. Business administration, a popular program for undergraduate and graduate students, must evolve with the dynamic education environment shaped by digital learning alternatives. The management programs must respond to the rapidly changing needs of the labor market, students, and society to provide students with the relevant skills. The chapter presents a literature synthesis, summarizes the new trends and priorities in designing and managing higher education, and points out practical implications and future research directions for higher education managers and researchers.
Urban Intelligence and IoT-UAV Applications in Smart Cities

The objective of this chapter is to propose a model of an automated city crime-health management that can be implemented in future smart cities of developing countries. The chapter discusses how a suitable amalgamation of existing technologies such as IoT, artificial intelligence, and machine learning can output an efficient system of unmanned city management systems, thereby facilitating indirect engendering of innovative scopes for technology workers and researchers and alleviating the living standards within the city fabrics, catalyzing infrastructure development. In this chapter, the authors have structured an ideal UAV-matrix layout for city fabric surveillance built over the scopes of artificial intelligence. Succinctly, this chapter provides a platform that would galvanize the possibilities and that could be reimagined to structure a more resourceful working model of new emerging smart cities and enlighten the settings of existing ones.
Applying Internet of Things (IoT) and Blockchain Technology to Improve Traceability in Pharmaceutical Supply Chain

The combined application of IoT and blockchain technology helps organizations to better track and trace items in the food, manufacturing, and logistics supply chains. Evidence shows that the characteristics of blockchain such as decentralization, distributed ledger, consensus algorithm, data immutability, and data security has brought various benefits to supply chains. Specifically, the pharmaceutical supply chain has a high requirement of maintaining medicine quality, trust, and traceability across the supply chain. Hence, a thorough review on the topic is necessary to understand the scholarly work in this area. The study uses a systematic literature review (SLR) to extract data from Scopus database for the period 2017-2020. Fifty-six articles were reviewed. The descriptive and thematic analysis revealed various insights into themes, methods, benefits, challenges, and blockchain architecture in achieving traceability across the pharmaceutical supply chain.
Using Machine Learning Algorithms for Breast Cancer Diagnosis

There are many cancer patients, especially breast cancer patients as it is the most common type of cancer. Due to the huge number of breast cancer patients, many breast cancer-focused hospitals aren't able to process the huge number of patients and might expose some women to late stages of cancer. Thus, the automation of the process can help these hospitals in speeding up the process of cancer detection. In this paper, the authors test several machine learning models such as k-nearest neighbours (KNN), support vector machine (SVM), and artificial neural network (ANN). They then compare their accuracies and losses with themselves and other models that have been developed by other researchers to see whether their approach is efficient or not and to decide what machine learning algorithm is best to use.
Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder

In this chapter, the authors used autoencoder in data preprocessing step in an attempt to improve image representation, consequently increasing classification performance. The authors applied autoencoder to the task of breast lesion classification in mammographic images. Image Retrieval in Medical Applications (IRMA) database was used. This database has a total of 2,796 ROI (regions of interest) images from mammograms. The images are from patients in one of the three conditions: with a benign lesion, a malignant lesion, or presenting healthy breast. In this study, images were from mostly fatty breasts and authors assessed different intelligent algorithms performance in grouping the images in their respective diagnosis.