Reviewerly/exHarmony huggingface dataset:
Akats: A System for Resilient Deployments on Edge Computing Environments Using Federated Machine Learning Techniques

Edge computing is a game changer for IoT, as it allows IoT devices to independently process and analyze data instead of just sending it to the cloud. But managing this considerable number of devices and deploying workloads on them in a coordinated and intelligent manner remains a challenge nowadays. In this paper, we focus on introducing the resilience dimension into these deployments, and we provide two main contributions: the use of federated machine learning techniques to develop a collaborative tool between the different devices aimed at detecting the possibility of a device failure, and subsequently, the utilization of the inferred information to optimize deployment plans ensuring the resilience in the devices. These two advances are implemented in an intelligent system, Akats, whose architecture is described in detail in this article. Finally, an application scenario is presented, based on Industry 4.0 - Machine predictive maintenance, to exemplify the benefits of the proposed intelligent system.
Nudging: a double-edged sword in the era of Big Data

This article aims to analyse the tool of nudging, in its most recent evolution of hypernudging, from a mainly practical perspective. The development of the big data world and the parallel phenomena of datafication have on the one hand reduced the costs of implementing nudging and on the other hand exponentially increased its pervasiveness. By penetrating the intimacy of one’s private life, hypernudges risk compromising the renown individual’s right to be left alone. The article therefore elaborates on these privacy risks and attempts to offer solutions both theoretically and practically.
Applications of deep learning and artificial intelligence methods to smart edge devices and stereo cameras

The aim of this work is to test a computer vision application that, thanks to edge computing and the use of devices optimised for artificial intelligence, allows the distance of objects from a fixed point to be measured. The distance is calculated between two or more points of interest with a certain accuracy and within a certain range. The point of interest from which to measure the distance is determined by a fixed point or an object recognised through deep learning techniques, with which the neural chip can perform very efficiently and with low energy consumption. In this work we used an Edge AI device with stereo cameras, a Luxonis OAK-D, equipped with an Intel Myriad-X neural chip and an improved version of the open-source Luxonis DepthAI library. The application is very complex and has the potential to be used in a variety of areas where precise positioning and real-time object recognition is required. The areas of applicability can be diverse, including assisted navigation, spatial data analysis, robotics, surveillance, health and safety, construction and engineering, surveying and mapping, transport, sports and fitness.
Optimal Land Suitability Based on GIS Tools for Solar PV Farms

Currently, the energy policy in Algeria emphasizes the diversification of energy sources and encourages the penetration of other sources such as solar energy in the energy mix, in order to gradually minimize the use of conventional energies and to ensure their exploitation for other decades. However, massive development of solar PV power plants and their efficiency depend essentially on the degree of compliance with technical criteria, such as the choice of the site where the plant to be installed. The evaluation of site-suitability is then considered a complex task, where multiple criteria inherent to the territory, are involved. This paper gives a state of the art of optimal land suitability for PV farms, based on GIS techniques. Several criteria were reviewed for optimal site selection. The limiting factors that hinder the installation of large-scale power plants have also been indicated.
Towards a Method for Evaluating Realism of Randomly Generated Models of IT Systems

Informally, an information technology system (ITS) consists of hardware and software infrastructure, such as workstations, servers, LANs, etc., along with their connections and interdependencies. Although most organizations operate an ITS, very few share detailed information about their ITS publicly. Because of this, models of arbitrary IT systems have to be generated either manually or automatically, following well known best practices and domain specific requirements. For applications in cyber security, the generated ITS models should be realistic in a sense that a relevant real-world organization could choose to organize its ITS according to them. Since most such models lack a corresponding real-world ITSs for comparison, their realism cannot be evaluated in the sense of conforming to a real-world ITS. In this paper, we propose a method for evaluating arbitrary models of ITSs for which a corresponding real-world ITS is not available for comparison, relying on several visualizations and a questionnaire. We perform a small-scale survey to validate the method, and present its results. The results are encouraging and show that the basic idea of the proposed method can serve the intended purpose, but also indicate several problems that must be addressed before definite conclusions regarding realism can be made.
Steganography App Based on Local Colour Statistics

In today’s world the usage of smart phones goes way beyond the common use. It can be used to send and receive secret messages concealed into different data transfer formats, like documents and images, that are invisible to human perception. This paper deals with the development of a Image Steganography Application for a smartphone based on statistical Gaussian Mixture Model of pixel representation by two colour model in the local neighborhood. Edges play important role in this statistical approach, since they are the regions of image high frequency that are capable of carrying payload without visual detection. The effectiveness of the proposed steganography technique was assesed by PSNR and L2 norm. The detailed description of an application developed using Matlab is provided as well.
Analysis of Sensor Data and Machine Learning Models for Gesture Recognition in Smart Toy Design

This study focuses on the analysis of sensor data and machine learning models for gesture recognition in Smart toy design. By examining different sensor technologies and evaluating various machine learning algorithms, the research aims to determine the most effective approaches for enhancing gesture recognition. The findings obtained from analyzing the sensor data and machine learning models will provide valuable insights for developing interactive Smart toys with improved gesture recognition capabilities.
Smart sustainable daily life: Insights from across the social sciences

It is essential that a human-centered smart digital society is a sustainable society. As research is often conducted in disciplinary silos, there is a need for more holistic approaches. This paper is a step towards such advancements, reviewing multiple social science fields which study how ICTs have shaped daily life and why digitalisation has become so integral. Outlining such perspectives, we illustrate the different methods for understanding social aspects of digital daily life and how they can be applied to studying smart sustainable daily life. For example, through ethnography, time diaries and in-depth interviews. We highlight the importance of collecting detailed insights on digital use overtime; the sequences and patterns of activities in daily life; and rich contextual variables to account for the impact of broader social dynamics on human and computer interactions. Our review hopes to inform future research seeking to couple work on the integration of smart technologies and energy systems into society with more foundational social science approaches.
Advising chatbot for high school in smart cities

A smart sustainable city is an innovative city that use a smart technology and smart education besides other ecosystem’s drivers in order to improve the quality of life. Several smart applications are developed by using different artificial intelligence models to enhance the education. A chatbot is considered as one of these advanced technologies that can be deployed in schools to enhance the education and learning processes in smart cities. However, high schools are the most crucial stage in student’s life as they have higher stress levels regarding their future compared to other students. They need advices more than others do, for example, they need help to find the best-fit universities and courses that can fit with their interests and goals, however, most previous studies focused on providing chatbots for supporting the matriculated and new incoming students at universities. Therefore, to fill this gap, this study aims to develop a novel chatbot for supporting students in high schools particularly by using the Multinomial Naive-Bayes and Random Forest algorithms, which can be, able to understand the natural language of students’ enquiries and accordingly, the intention class for each question will be predicted. The data in this study is comprised of 505 questions, which were obtained from multiple resources such as students' blogs, schools' counsellors, students, parents as well as from schools & universities' websites. The results in this study shows that the performance of the chatbot is improved by using the techniques of pre-processing and the feature extractions such as CountVectorizers and TF-IDF. Moreover, the results show that the Random Forest classifier performed better than Multinomial Naive-Bayes in all metrics. The performance of the models is checked by using different metrics such as accuracy, precision, recall and F1-score and all show high scores with exceeding 90%. However, the accuracy of Multinomial Naive-Bayes classifier achieved higher score when using CountVectorizers compared to using the TF-IDF. In the future work, this result will be investigated by re-evaluated the model with using a large corpus with students’ enquiries.
Exploiting the DICE specification to ensure strong identity and integrity of IoT devices

IoT devices are becoming widely used in several contexts, and nowadays billions of devices are deployed in different scenarios, some of which are very critical to people’s privacy and safety. For these reasons, it is very important to provide capabilities for guaranteeing the correct behaviour of the devices. Remote attestation is a technique traditionally used to monitor the integrity status of nodes and to determine if they are behaving as expected. This technique requires that the device is equipped with Roots of Trust, that are the set of hardware and software features that make the platform capable of providing reliable integrity reports even when it has been compromised. This paper proposes a solution that permits to identify and attest devices in a dynamic context, such as Smart Cities or Smart Homes, where unknown devices can connect to the network and perform several actions. The proposed security schema is based on the Device Identity Composition Engine (DICE), which represents a set of specifications designed by the Trusted Computing Group (TCG) to enhance security and privacy of devices with minimal silicon requirements.
Hand Gesture Recognition System with Finite State Machine for Remote Desktop Control

Humans may interact with computers in many ways. One of them can be communication through recognition and right interpretation of hand gestures. This study addresses the problem of remote desktop control through visual-based human–computer interaction that is based on hand gesture recognition. We designed and implemented the hand gesture recognition system that combines various machine learning (ML)-based technologies, including the MediaPipe Hands model for detection of keypoints on hands, and the artificial neural network for classification of single static gestures. To recognize dynamic or group gestures that are assigned to command actions, we proposed a flexible recognition system that is based on the concept of finite state machines. The new group gestures do not require retraining and can be easily composed of single existing gestures, making our approach more accessible to the end user. The experiments demonstrated that our system can correctly recognize a few group gestures for performing the following operations on the computer desktop: cursor movement, clicking, window dragging, full-screen switch on/off, zooming in/out, window closing, and active window switching.
Digital Forensics Investigation Models: Current State and Analysis

A digital forensics investigation (DFI) is characterized with activities related to search, identification, collection, analysis of digital evidences as all activities should be documented in a given format. Different models and methodologies are proposed to facilitate DFI, trying to describe procedures and activities performed by the investigator. Continuous technological development leads to the emergence of new sophisticated attacks, which also requires new approaches to be taken to crime scene investigation. In this paper a summary and analysis of contemporary achievements in DFI is performed to outline the current state of DFI development and to draw challenging issues and trending research topics. A conceptual model generalizing current state in DFI models is also presented.
Gulf Dialect Speech Recognition Using Neural Network

Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Automatic Speech Recognition has found its application on various aspects of our daily lives such as biometric analysis, education, security, healthcare in smart cities. This paper implements Automatic Speech Recognition Mechanism along with Neural Network where the system converts the Arabic and Gulf dialect speech into Arabic text that is written in flask framework. A comprehensive research of utilizing a Convolutional Neural Network, Long Short Term Memory networks in speech recognition besides introducing methods for training of the neural network so that a convenient neural output can be acquired to the desired output. The proposed model was successfully tested using 35 speech recorded files containing 60,000 words, collected from different websites. The model was able to correctly recognize and convert gulf dialect speech record with accuracy 72.7%
Harnessing IoT, E-Business, and Digital Marketing for Enhancing User Satisfaction and Performance in UAE’s Hotel Industry: An Empirical Study

The hotel industry in UAE is one of the major source of revenue, competition among the hotels is mounting every day. Therefore, the top management of these hotels needs to identify the factors to improve their performance. This study aims to analyze the effect of IoT, E-business strategy, digital marketing and performance through user satisfaction. The quantitative research was conducted by distributing questionnaires among the customers of different hotels of UAE. The sampling technique adopted in this study was purposive sampling. The usable questionnaires used for analysis were 212. The study adopted the SEM technique and PLS tool for analysis. The results indicate that digital marketing, IoT, and e-business strategy significantly impact the user satisfaction. User’s satisfaction is an important predictor of the performance of hotels. These findings can be valuable for decision-makers in the UAE hotel industry.
Machine Learning Model for Measuring Cyclomatic Complexity of Source Code

Software developers strive to build high-performance and quality software with a very high degree of coherence among its components, simplified structure, and reduced complexity. A cyclomatic complexity measure is used to identify areas of code that are difficult to maintain or debug by determining how many independent paths, conditional and iteration statements there are through the source code. Using AI, it is possible to estimate and measure the cyclomatic complexity of source code by analyzing the structure of the code and identifying any potential areas where complexity could arise. This research aims to utilize deep learning methodologies, specifically for text classification, to measure the Cyclomatic Complexity of source code, it presents a method for measuring source code Cyclomatic Complexity using Multinomial Naive Bayes Machine Learning Model trained on 3270 samples collected from programs written in Python, Java, and C++. The results show an average accuracy of 96% and precision, recall, and f1-score were calculated for different levels of complexity.
BioTrak: A Blockchain-based Platform for Food Chain Logistics Traceability

The food supply chain, following its globalization, has become very complex. Such complexities, introduce factors that influence adversely the quality of intermediate and final products. Strict constraints regarding parameters such as maintenance temperatures and transportation times must be respected in order to ensure top quality and reduce to a minimum the detrimental effects to public health. This is a multi-factorial endeavor and all of the involved stakeholders must accept and manage the logistics burden to achieve the best possible results.However, such burden comes together with additional complexities and costs regarding data storage, business process management and company specific standard operating procedures and as such, automated methods must be devised to reduce the impact of such intrusive operations.For the above reasons, in this paper we present BioTrak: a platform capable of registering and visualizing the whole chain of transformation and transportation processes including the monitoring of cold chain logistics of food ingredients starting from the raw material producers until the final product arrives to the end-consumer.The platform includes Business Process Modelling methods to aid food supply chain stakeholders to optimize their processes and also integrates a blockchain for guaranteeing the integrity, transparency and accountability of the data.
Enhanced Capuchin Search Algorithm Using Cooperative Island Model with Application of Evolutionary Feedforward Neural Networks

This paper introduces an enhanced version of the Capuchin Search Algorithm (CapSA) called ECapSA. CapSA draws inspiration from the collective intelligence of Capuchin monkeys and has shown success in solving real-world problems. However, it may encounter challenges handling complex optimization tasks, such as premature convergence or being trapped in local optima. ECapSA employs a local escaping mechanism operating the abandonment limit concept to exploit potential solutions and introduce diversification trends. Additionally, the ECapSA algorithm is improved by integrating the principles of the cooperative island model, resulting in the iECapSA. This modification enables better management of population diversity and a more optimal balance between exploration and exploitation. The efficiency of iECapSA is validated through a series of experiments, including the IEEE-CEC2014 benchmark functions and training the feedforward neural network (FNN) on seven biomedical datasets. The performance of iECapSA is compared to other metaheuristic techniques, namely differential evolution (DE), sine cosine algorithm (SCA), and whale optimization algorithm (WOA). The results of the comparative study demonstrate that iECapSA is a strong contender and surpasses other training algorithms in most datasets, particularly in terms of its ability to avoid local optima and its improved convergence speed.
The Role of Social Media Usage, E-WOM, and Perceived Enjoyment in Shaping Customer Attitudes and Blockchain Adoption Loyalty in UAE Banking: A Quantitative Investigation

Technology is shaping the banking sector around the globe. Therefore, the prime objective of this research was to examine the relationship among social media use, e-WoM, perceived enjoyment and customer’s attitude towards loyalty to the use of Blockchain technology in the context of U.A.E. banks. This study used a cross-sectional research design and quantitative methods for data collection. Five-point Likert scale was used to develop the well-designed questionnaire for data collection. A simple random sampling technique was adopted to distribute the questionnaire among respondents. A sample of one hundred seventy-four observations was analyzed using Smart PLS 3.3.9. The findings confirmed the significant and direct effect of social media use, e-WoM, and perceived enjoyment on customers’ attitudes. The direct effect of customer attitude on Blockchain adoption was also supported. The findings also revealed the important mediating role of customer attitude as well. The findings are helpful for policymakers and academicians.
Intelligent Phishing Email Detection with Multi-Feature Analysis (IPED-MFA)

The rise in bulk email phishing attempts has emerged as a serious cyber-risk for businesses given the pervasive usage of email as a primary form of communication. Despite the existence of manual and technical countermeasures, phishing emails still pose a risk of deceiving employees. Machine learning has played a vital role in improving the detection of many types of cyberattacks, including email phishing. In this paper, the utilization of a machine learning-based IDS model to detect phishing emails before they reach inboxes is proposed. A review of phishing email detection techniques and algorithms is presented, and a publicly available dataset of ham and phishing emails is discussed and used. Multiple machine learning classifiers were deployed (RF, SVM, Adaboost, Logistic Regression, and KNN), and their evaluation scores were generated and analysed. Using recursive feature elimination and multi-feature analysis, Random Forest outperformed the other classifiers with an accuracy of 98.6%, closely followed by Adaboost with 98.1%.
On Asymptotic Optimality Property of a Sampling based Motion Planner

In this paper, we study the asymptotic optimality property of a randomized incremental sampling based motion planner, namely RRT. The study proves that an RRT planner is not an asymptotically optimal motion planner. The result, while being consistent with similar results that exist in the literature, however, brings out an important characteristic of an RRT planner, that is, the degree distribution of the tree vertices follows a power law in an asymptotic sense. Simulation results are shown to support the theoretical claim. Based on these results, this work establishes a simple necessary and sufficient condition for sampling based motion planners to be asymptotically optimal.
Synthesis and Application of Constrained Flatness-Based Real-Time Trajectory Planning for Autonomous Emergency Steering

The contribution of this paper is the synthesis of a constrained real-time trajectory planning approach for Autonomous Emergency Steering (AES) and its application in a demonstrator vehicle of the SAFE-UP project. The proposed concept consists of two core elements. The first element is a model-based trajectory planner which takes into account both state and input constraints. The second element is a sampling-based optimization algorithm that embeds the planner and performs an online optimization of AES trajectories. After the synthesis of the core elements, the resulting planner is discussed and demonstrated both in a simulation study and a vehicle testing campaign on a test track, where the algorithm is set up to perform a cyclic trajectory re-planning during an AES maneuver.