Reviewerly/exHarmony huggingface dataset:
An Appointed Access Control Scheme for Distributed-Cloud Environment

With the development of cloud computing technology, single clouding services have not been able to cope adequately with the online services boom, distributed-cloud is satisfied with the demand. The access control technology among the distributed-cloud environment has become an indispensable threshold to ensure the security of cloud services. However, the current access control schemes are not appointed, i.e., once a user gets the credential, he/she has the capacity to access all services in the distributed-cloud environment. This is conflict with the practical application usually restricted by the role of the user. To overcome this weakness, this work designs an appointed access control scheme using signcryption for distributed-cloud environment, which provides mutual authentication, hierarchical access control, appointed access, etc. Finally, we compare with the existing scheme, and the results show that our scheme has the best computational efficiency and communication efficiency. Therefore, our scheme is more suitable for complex and diverse distributed-cloud environment.
Narrow Gaps Crossing Flight Trajectory Tracking for MUAVs Using Adaptive Fuzzy Balance Control

Micro unmanned aerial vehicles (MUAVs) often need to perform maneuvering in an unstructured environment with narrow gaps and confined spaces in-between obstacles due to low-altitude flight. Therefore, this paper proposes a feasible flying trajectories planning scheme based on micro drones orientations and dynamics for the micro drones with external disturbances to pass through narrowly in-between obstacles to the desired position. In order to incorporate quadrotor’ orientations and dynamics into the flying trajectory planning, we first consider the dynamics modeling within the ellipsoidal coordinates, and employ the convex separation hyperplane theorem of the flight constraints to set up the trajectory. As for controlling the quadrotor, an online adaptive fuzzy control is used to achieve robust tracking performance even under disturbances. Finally, five drones flying experiments under various flying constraints are used to show the effectiveness of the proposed scheme to follow a feasible and obstacles gaps trajectory and reach the desired position.
Edge Oriented Redistribution of Computational Load for Authentication Systems

Having an authentication system that is secure can be rather computationally expensive on the server and even affect user experience. This can affect user experience due to high demand by many users during the authentication process. Furthermore, users may find an authentication system that requires more interaction to be inconvenient and may opt out of the current standard 2FA options. We propose a method of using hash chains that are computed on the client side, by a verified device, to alleviate some computational overhead of a server, while also providing high security during the transmission of secure information. This system hopes to be less inconvenient for a user by only requiring them to type in a username/password and scan a QR Code.
Study on Green Cloud Computing-A Survey

Cloud computing is basically known for its pay- as-you-go model. Cloud computing has made the tremendous impact on every business by the services it offers. Cloud computing offers the various services which made easier for every organization to carry out their business task with in less time. The services offered are: IaaS (Infrastructure as a service), PaaS (Platform as a service), SaaS (Software as a service) and many more services Before cloud computing we have had the traditional data centers, which offered us the same resources but somehow less than what the cloud computing is offering. The traditional data centers offered us storage service, computing service networking and many more. But installing of data centers cost too much to organization and thus led to more Co2 emissions, or greenhouse gases emissions. Data centers consumes lot of energy. Thus, with the advent of cloud computing there was shift in approach from traditional data centers to the cloud, which helped us in reducing the carbon foot print thus in this manner somehow cloud computing offers us the green and sustainable approach where shifting to the cloud is more sustainable and environmental friendly. In this paper we will discuss about different services, deployment models of the cloud, different matrices of energy consumption and if there is any energy efficient architecture exist, and finally we will discuss ours propose model.
Automated Business Rules Classification Using Machine Learning to Enhance Software Requirements Elicitation

The elicitation of software requirements is an essential phase in building commercial software. Business rules can be an important source of software requirements specifications. Business rules are the description of the business process. Categorization and classification of these business rules can enhance the process of software requirements elicitation. We proposed an approach to categorize and classify business rules based on Witt's approach, which classifies business rules into four main categories. The proposed approach showed good accuracy, recall, and F1-scores values compared to the state-of-art approach.
Ethereum-Based Smart Contract and Traceability System for Food Bank Operations

Efficient food bank operations are crucial in addressing food insecurity and providing resources to those in need. However, traditional approaches often face challenges related to accountability, transparency, and traceability. This paper proposed an innovative solution utilizing blockchain technology and smart contracts on the Ethereum platform. The system utilized the Remix Integrated Development Environment for contract development and testing, along with Ganache for local blockchain simulation. This combination enabled the establishment of a decentralized and immutable ledger to track the journey and quality of food donations and distributions. This study provided a reliable and transparent framework for food bank operations, enhancing the effectiveness and efficiency of food distribution processes. Results indicated that this approach promotes collaboration, builds stakeholder confidence, and ultimately contributes to reducing food insecurity in communities.
Towards Better Software Development Effort Estimation with Analogy-based Approach and Nature-based Algorithms

Software effort estimation has always been among the main considerations of software practitioners and researchers. A project's proper cost estimation is crucial for the organization's conservation and competitiveness; thus, excessive overestimation or underestimation of software development cost is a key factor for the failure of software products or closing software projects without any special achievement. The cost considered for software system development mainly pertains to the human efforts whose amount is calculated using the time span and the number of individuals involved in a project. The research behind software effort estimation is generally focused on providing solutions for improving the estimation, resulting in different approaches for improving software effort improvement. One of the most prevalent approaches is analogy-based estimation. The literature review indicates the significant and positive results of this approach in software effort estimation; however, there are still serious research gaps. This article attempts to address the research gaps and novel trends ahead of software researchers in analogy-based effort estimation.
Tensor-based Color Feature Image Extraction for Blood Cell Recognition

Blood cell recognition plays a huge role in medicine. Many researchers have recently promoted the use of deep learning models to improve the accuracy of blood cell recognition. However, many remain challenges, such as the unavailability of large training data sets and the similarity between normal and malignant cells. To overcome these challenges, this paper proposed a novel method to extract the feature of blood cell images in the preprocessing step of a deep learning-based blood cell recognition system. First, the color blood cell image as a third-order tensor is expressed, and then compute its core tensor using high-order singular value decomposition (HOSVD). Second, a new core tensor is created by fixing the second frontal slice, and the remaining frontal slices are set to all-zero matrices. Finally, the feature of the blood cell was obtained by reconstructing the image with the new core tensor by inverting HOSVD. Experiments performed on two most famous public medical datasets named BCCD and Raabin-WBC using three state-of-art deep learning models, AlexNet, GoogLeNet, and ResNet show that by using the proposed feature image, a very high accuracy recognition rate is obtained, higher the using original images even use only small amount data for training.
Explainable AI for CPS-Based Manufacturing Workcell

This paper discusses the application of Explainable AI (XAI) for a Cyber-Physical System (CPS)-based manufacturing workcell. XAI involves providing interpretable outputs from deep learning models. CPSs are systems that integrate interacting analogue, physical and human inputs, and have applications in control optimisation, automation and process intelligence. Current state of the art and gaps in XAI and CPSs are introduced. The need for CPSs with in-built logic to correct and reduce dependency on conventional mathematical modelling of physical systems is identified, together with how the in-built logic must be explainable and understandable to build trust in the logic. This is especially true for applications where manufacturing accuracy and precision are critical. Whilst there have been advances in both XAI and CPSs in recent years, little progress has been made in integrating the two. We show that XAI can increase the efficiency and reliability of CPS-based manufacturing. XAI in CPS can ensure that manufacturing validation inputs made by deep learning networks within the CPS are understandable and correct. It thus ensures that the CPS outputs have actions that are explainable and logic dependent, with safeguards against malicious intent. This contrasts against conventional CPS which may have learning-based systems with ‘black box’ outputs that are assumed to be correct. Approaches towards XAI and how they can be integrated into a learning-based machine vision CPS manufacturing workcell are described. An analysis of the effectiveness of these approaches and their feasibility of implementation in such a CPS is performed. A recommendation of what is the most promising approach towards implementing XAI in the CPS manufacturing of a mass-market, specialised and personalised product, is made as a conclusion.
Developing Skills and Competencies for Educators' Transitions to Using Digital Technologies

The purpose of the study reported on in this chapter was to investigate the extent to which educators were developing skills and competencies for their transitions to using digital or Information and Communication Technologies (ICTs) at a specific school in the greater Johannesburg area, Gauteng province, South Africa. Against the background of Developing Skills and Competencies for Digital and Green Transitions, the objectives of this study also included establishing to what extent the school was ready to implement such technologies in teaching and learning and hence be characterized as an e-school.
Incentive-driven and SAC-based Resource Allocation and Offloading Strategy in Vehicular Edge Computing Networks

Vehicular edge computing (VEC) networks can provide low-latency services for vehicles. However, it is a great challenge for edge nodes to satisfy the computing tasks of all vehicles during vehicle peak hours. This paper studies the joint optimization problem of offloading strategy and resource allocation in a VEC network composed of road side units (RSUs) with computing resources, vehicle users and vehicle workers. In order to alleviate the computing pressure of the RSU, we use contract theory to encourage vehicle workers to execute computing tasks. At the same time, we propose a novel algorithm based on Soft Actor-Critic (SAC) to solve the system cost minimization problem considering vehicle users' satisfaction, RSUs' cost and vehicle workers' reward. Finally, we conduct extensive simulations in different scenarios, the simulation results show that our proposed algorithm has higher performance in reducing system cost compared with other benchmark methods.
Radio Frequency Fingerprint Extraction and Identification for Wi-Fi Signals in Noisy Channels

A noise-robust radio frequency fingerprint (RFF) extraction and identification scheme is proposed and demonstrated for Wi-Fi device recognition. Specifically, an RFF extractor pre-trained by supervised contrastive learning extracts the distinctive features from the spectrogram, which is produced by Fourier-based sychrosqueezing transform (FSST). Then a neural network-based classifier is adopted for precise device identification with the extracted RFF. Utilizing 11 commercial wireless network interface controllers (WNICs), the experimental results demonstrate the significant performance improvement for noisy channels, with classification accuracy achieving up to 95.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 86% when the signal-to-noise ratio (SNR) is 10 and 5 dB, respectively.
Lay Importance on the Layer: Federated Learning for Non-IID data with Layer-based Regularization

We propose Federated Learning with Layer-Adaptive Proximation (FedLap), a new class of algorithms to tackle non-IID data. The novelty is a new regularization term in the local (client) loss function which generalizes that of FedProx and captures divergence between global and local model weights of each client, at the level of Deep Neural Network (DNN) layers. Thus, the weights of different layers of the DNN are treated differently in the regularization function. Divergence between the global and local models is captured through a dissimilarity metric and a distance metric, both applied to each DNN layer. Since regularization is applied per layer and not to all weights as in FedProx, during local updates, only the weights of those layers that drift away from the global model change, while the other weights are not affected. Compared to FedAvg and FedProx, FedLap achieves 3–5 % higher accuracy in the first 20 communication rounds, and up to 10% higher accuracy in cases of unstable client participation. Thus, FedLap paves the way for a novel layer-aware class of algorithms in distributed learning.
Blockchain Based Identity Management System Using Cryptography and Steganography

Identity documents (ID) are susceptible to various theft and hacking. Government institutions, banks, and credit agencies are the weakest spots in the present identity management system. The current centralized system is neither trustworthy nor secure. One needs to provide identification at every stage using multiple IDs recognized by the government. Privacy issues and data breaches result from sharing different IDs. We have proposed a solution using blockchain to achieve self-sovereign identification through decentralized networks, which ensures privacy where identity documents are secured, authenticated, and only the participants with permission can access the documents. The proposed solution includes a novel security framework and addresses security vulnerabilities that can arise while sharing the hash using a blend of cutting-edge techniques-steganography and cryptography methods.
A Comprehensive Review of Digital Forensics Frameworks for Internet of Things (IoT) Devices

The development of Internet of Things (IoT) devices has brought many benefits to our daily lives and business. Despite this, as the number of IoT devices continues to grow exponentially, their weak security infrastructure provides intruders with new opportunities to engage in digital crimes, violate privacy, and plan or carry out unlawful activities. The vast amount of data generated by IoT devices has the potential to hold valuable clues related to the crime scene. Hence, digital forensic investigators face challenges and obstacles in identifying criminals due to the complex nature of the IoT environment, the multiple IoT standards, and the absence of adequate IoT forensic tools. Therefore, digital forensics is one of the major concerns due to the computerization of everything, including IoT devices. This study aims to review IoT in the digital forensics context, a systematic literature review was conducted to achieve this goal, and the result from the literature review indicates that there is a need to develop and design a suitable framework for IoT digital forensics to overcome the challenges and attacks in the heterogeneous architecture of IoT environments.
Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical Images

Deep learning (DL) algorithms can improve healthcare applications. DL has improved medical imaging diagnosis, therapy, and illness management. The use of deep learning algorithms on sensitive medical images presents privacy and data security problems. Improving medical imaging while protecting patient anonymity is difficult. Thus, privacy-preserving approaches for deep learning model training and inference are gaining popularity. These picture sequences are analyzed using state-of-the-art computer aided detection/diagnosis techniques (CAD). Algorithms that upload medical photos to servers pose privacy issues. This article presents a convolutional Bi-LSTM network to assess completely homomorphic-encrypted (HE) time-series medical images. From secret image sequences, convolutional blocks learn to extract selective spatial features and Bi-LSTM-based analytical sequence layers learn to encode time data. A weighted unit and sequence voting layer uses geographical with varying weights to boost efficiency and reduce incorrect diagnoses. Two rigid benchmarks—the CheXpert, and the BreaKHis public datasets—illustrate the framework’s efficacy. The technique outperforms numerous rival methods with an accuracy above 0.99 for both datasets. These results demonstrate that the proposed outline can extract visual representations and sequential dynamics from encrypted medical picture sequences, protecting privacy while attaining good medical image analysis performance.
Online learning: experiences of Namibian students relying on smartphones

ABSTRACTThis study reports on the experiences of a group of university students relying on smartphones to access online learning during/post COVID-19. In understanding the effectiveness of smartphones Rogers Diffusion of Innovation theory was consulted. The study took place at a University in Namibia at the Faculty of Education. This study adhered to a mixed methodology approach favoured by the paradigm. One hundred students participated in the study recruited through convenience sampling method. The data was collected using a questionnaire containing questions for both qualitative and quantitative data. The study employed content analysis techniques to analyse the qualitative data and statistical software to analyse the quantitative data. The findings present the following factors: opportunities, restrictions and possible solutions for students relying on smartphones for online learning. The study made recommendations for effective online learning which include affordable data packages, provision of suitable smart devices and the need for stable internet connections to all students. The study reveals that smartphones in Namibia have become a necessity to many students to access education during/post COVID-19 era.KEYWORDS: Online learningm-learningsmartphonesdiffusion of innovation theory Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsReginald Gerald GovenderReginald Gerald Govender is a Lecturer: Computer Science Education at the University of KwaZulu-Natal, South Africa. He coordinates several Computer Science Education modules dealing with coding and robotics, teaches and supervises postgraduate research in computer programming, educational robotics, ICTs and Mathematics. He has published in local and international journals.Simon AlbinSimon Albin is a Lecturer: Mathematics Education in the School of Education, Department of Early Childhood Education and Care at the University of Namibia. His roles include lecturing Mathematics and Numeracy Studies and supervising research projects for undergraduate students in the B.Ed Honours programme. Simon’s research interests are intervention research, teaching practice, mathematics proficiency of fractions and educational technology.
Astrophysically sourced quantum coherent photonic signals

Stimulated emission is shown to be robust in stars. Through Bose enhancement this produces quantum states of aligned, monochromatic photons somewhat similar to a laser. The probability of creating such states is computed. We show that from the solar corona such quantum states would propagate outside of the solar region and through the Solar System without decoherence. For a $1\text{\ensuremath{-}}{\mathrm{m}}^{2}$ test detector at the distance of the Earth from the Sun, we estimate rates of such quantum states in the few per second thus potentially detectable. The same process should lead to such quantum states also arriving from stars at interstellar distances.
Classifying falls using out-of-distribution detection in human activity recognition

As the research community focuses on improving the reliability of deep learning, identifying out-of-distribution (OOD) data has become crucial. Detecting OOD inputs during test/prediction allows the model to account for discriminative features unknown to the model. This capability increases the model’s reliability since this model provides a class prediction solely at incoming data similar to the training one. Although OOD detection is well-established in computer vision, it is relatively unexplored in other areas, like time series-based human activity recognition (HAR). Since uncertainty has been a critical driver for OOD in vision-based models, the same component has proven effective in time-series applications. In this work, we propose an ensemble-based temporal learning framework to address the OOD detection problem in HAR with time-series data. First, we define different types of OOD for HAR that arise from realistic scenarios. Then we apply our ensemble-based temporal learning framework incorporating uncertainty to detect OODs for the defined HAR workloads. This particular formulation also allows a novel approach to fall detection. We train our model on non-fall activities and detect falls as OOD. Our method shows state-of-the-art performance in a fall detection task using much lesser data. Furthermore, the ensemble framework outperformed the traditional deep-learning method (our baseline) on the OOD detection task across all the other chosen datasets.