Reviewerly/exHarmony huggingface dataset:
An Explorative Analysis of IoT Security on Federated Intelligent Networks

This paper describes the use of deep learning techniques in order to analyze the data generated by IoT devices and determine whether the data was generated as a part of the normal operation of that device or was it generated by a malicious node. The malicious node could cause attacks like denial-of-service attack or a brute force attack to bypass the password authentication of the target device. Deep learning is suggested to be an effective way of providing security to the devices that participate in an IoT network. This paper describes federated learning techniques which are utilized since the IoT devices tend to have less processing power sufficient for the normal operation of the device while conserving the rest in order to minimize the cost. Certain vulnerabilities arise in the system due to the use of federated learning techniques namely lack of incentive to individual nodes and poisoning of the global model. We propose a blockchain based solution to provide incentive to the individual nodes in terms of credits. The incentive is in proportion to the amount of contribution provided by the given node. The resultant updates generated by compute nodes are verified based on the impact they will have on the global model before incorporating them into it.
Design of Solar-powered Irrigation System based on Penman-Monteith Model

The evolution of technology is very fast changing and becomes a major factor in globalization. In the field of farming, multiple technologies have been introduced and tried to alleviate the farming difficulties brought by the current era of climate change but one of the most pressing problems for our farming sector is how to utilize the resources available in a more cost-friendly way such as the utilization of renewable energy based on actual conditions present on their farming area. The Philippines is annually devastated by typhoons resulting in property and infrastructure damages, one of which is the irrigation system. According to National Irrigation Authority, many irrigation facilities has been established in 1963, and currently, most farmers are adapting the pump irrigation system as an alternative because of the accumulated damages from natural calamities. This study aims to provide a design that can be used and adapted by our local farmers to cut their process costs, especially in the use of their diesel pump system with an alternative photovoltaic pump system. This paper presents a design of a solar-powered irrigation system based on the Penman-Monteith model of evapotranspiration to provide an alternative but reliable solution for the problems identified.
Gan-Based Face Aging System with Dynamic Age Intervals

This article explores and implements what a person looks like at different ages. Age affects every inch of the body. Along with the wisdom, experience, and accomplishments that come with aging, so too does our appearance. The change in our faces is of utmost importance. The facial aging system has a variety of uses, including finding lost children, chasing criminals, etc. Previous work on facial aging systems was developed by training a generator in a GAN to static ages such as 00–10, 11–20, and 21–30. The main goal of this work is to propose a model that dynamically determines age groups according to user requirements. In this paper, we implemented a model of GAN for every single age instead of groups of ages. This paper uses the Tkinter object which is a slider that allows the user to select the starting age and next age interval. The output of this model is a set of images for user-specified age groups.
Automated Spam Detection Using Stochastic Gradient Descent with Self-Attentive Deep Learning Model

Since the usage of the Internet is rising, individuals were connected virtually through social networking sites like Facebook, Instagram, Twitter, and so on. This has resulted in surge in the spread of unwanted messages called spam that is utilized for accumulating personal data, marketing, or to offend individuals. As a result, it becomes essential to have a stronger spam detection method that might thwart this type of message. Spam detection is the most important machine learning-oriented application over the past few years. Simultaneously, spam detection on noisy platforms like Twitter which remains a challenge because of high variability and short text in the language used on social networking platforms. To resolve these issues, this paper presents an automated spam detection using stochastic gradient descent with deep learning (ASD-SGDDL) technique. The presented method had a focus towards the detection of spam in the Twitter data. The proposed ASD-SGDDL model pre-processes the tweets in different ways such as filtering, tokenization, stop word removal, and n-gram construction. Followed by, the ASD-SGDDL model utilizes self-attentive deep learning (SADL) method for spam detection and classification. For improving the performance of detection in the presented method, the hyperparameter tuning process can be carried out by utilizing the SGD model. The experimental validation of the proposed method is examined on a benchmark dataset and the outcomes are examined interms of different measures. A wide ranging comparison study stated the enhanced performance of the ASD-SGDDL method to other existing models.
Comparison of Exclamation Mark Usage as Message Nuances in Digital Chat Communication Between Japanese and Indonesian

Exclamation mark or kantanfu in Japanese, is one of written language elements used to convey expressions. Based on exclamation mark usage, the meaning and nuance of a sentence could differ. Amidst the COVID19 pandemic that limits one's interaction to another, the frequency of digital communication has significantly increased. Digital communication through chats has become an activity that cannot be separated from everyday life. Therefore, it is important to understand exclamation marks correctly. The aim of this study is to avoid misunderstandings that could happen through chats in Japanese. This study will compare the use and interpretation of exclamation marks in Japanese chat between Indonesians and Japanese. Japanese chat messages containing exclamation marks were analyzed using Hymes' SPEAKING theory. This study uses Creswell's convergent mixed methods, with quantitative data of Indonesians' questionnaire survey results, and qualitative data of interviews with Japanese people. Study results show that there are differences in the use and function of exclamation marks between Indonesians and Japanese.
Design of Computer Decision Support System for Labor Education Evaluation in Colleges and Universities

Labor education in Colleges and universities is a comprehensive quality education with the content of Cultivating College Students' abilities in related aspects such as concepts, emotions, will, knowledge and skills, the goal of improving relevant quality and quality, meeting the needs of society for knowledge-based, skilled and innovative labor talents, and the value pursuit of carrying forward the spirit of labor and cultivating labor models. The design of computer decision support system for labor education evaluation in Colleges and universities is the process of designing a computer system to assist in the management, monitoring and evaluation of students' performance. The goal is to improve the quality and effectiveness of teaching, learning, evaluation and other activities related to academic teaching. In addition to supporting the teaching delivery of a college or University (for example, by providing information about the progress of students' graduation), it can also be used as a management tool to support institutional policies, such as setting class size limits or determining whether courses meet the minimum standards for inter agency credit transfer.
Cloning an Image using Hybrid Approach

In the modern era majority of the people are addicted to social media. People are more interested towards keeping profile pictures in their social media sites due to some abuse of their images by some people.This article explores and implements how an image looks like after converting in to cartoon with different face expressions and pencil art. Previous work on cartoon image conversion and pencil art conversion was developed for only cartoon images but no edge clarity and richness in edge quality and discontinuous lines with no face expression change. The main goal of this work is to propose a model that dynamically takes the input image from the user and produces the output image of cartoon image with change in face expression and pencil art. This model is implemented using k-means clustering and media pipe for face mesh to identify the multiple face landmarks for cartoon conversion and scipy is used for converting the image in to pencil art. And streamlit an open-source framework is used for developing this model. This model allows user to choose the type of conversion and give an input an image so that it produces the output of either pencil art or cartoon image with face expression conversion.
Industrial Internet of Things (IIoT): Testbed and Datasets for CyberSecurity and Digital Forensics

Industrial Internet of Things (IIoT) datasets are hard to obtain due to confidentiality and security reasons. Consequently, evaluation of security solutions tailored for IIoT systems bears a significant research gap in the industrial security domain. An IIoT testbed that represents real-world scenarios comprising physical processes and its related controls would enable researchers to deploy and analyze solutions better, to explore and model vulnerabilities, understand the flow of data for forensic purposes, and to model viable datasets that help test and validate security measures. However, development and implementation of such testbeds is highly composite and brings along significant challenges upfront. In addition, the existence of legacy systems in the industrial environments, such as SCADA systems, hinders prompt deployment of sophisticated security controls against industrial systems. We propose the design of an IIoT testbed that comprises a wide range of physical processes through diverse controllers, applications, and devices interacting with each other and fostering acquisition of multidimensional data from diverse data sources. In addition, we propose a realistic IIoT dataset that would facilitate understanding of traffic flow and device/application analysis for our Digital Forensic Readiness (DFR) experimentation.
Sharding in Blockchain Systems: Concepts and Challenges

Due to the persistent issue of blockchain scala-bility, sharding was established as a database splitting approach utilized by blockchain systems to improve scalability. Sharding in blockchain systems presents several challenges, including security, cross-shard communication, and scalability. A significant quantity of study has been conducted in these sub-areas. However, no new research has been conducted that looks deeper into the issues in each area. This survey provides a concise overview of current concepts and challenges in sharded blockchain systems. We explore distributed systems, blockchain technology, sharding and related concepts. We analyzed 14 papers and divided the findings into two categories: concepts and challenges. The following is how the paper is organized: The first section introduces the concepts of sharding, distributed systems, and blockchain technology, the following sections discuss the challenges and relevant algorithms. Finally, we compare the challenges to present results.
E-Health Monitoring System Application using OLED and Pulse Sensor

A heartbeat monitoring system application integrated with Blynk Apps, OLED, and pulse sensors is proposed in this paper. Counting the heartbeat in beats per minute (BPM) in a manual way would be time-consuming, especially for a large group of patients and when in an emergency condition. The proposed heartbeat monitoring system is designed to reduce the effort and improve the performance of the current heartbeat monitoring system. The system can capture the pulse rate and use the application to track down every heartbeat condition, with the aid of an IoT system and sensors. The hardware-based Internet of Things (IoT) platform is termed ‘Blynk’ which is basically an amalgamation of machine learning, analytics, private device management, and white-labeled mobile applications. The microcontroller used system in this project would beESP8266 NodeMCU with a built-in Wi-Fi module. The proposed e-health monitoring system can be used for high-intensity workout competitions and training purposes to capture the participants of the heartbeat condition to avoid unprofessional acts that can endanger one's health. Furthermore, the proposed attendance system can save time from queueing up at the clinic or hospital to check heartbeat conditions and wait for consultation, patients can directly use the monitoring system at home and the apps to capture the results.
Solar Irradiance Prediction using Deep Learning-Based Approaches

The worst effects of pollution from greenhouse gas emissions are increase in atmospheric temperature, sea level rise, and a variety of diseases. The main source of greenhouse gases is the combustion of fossil fuels, especially in thermal power plants. The use of renewable energy sources, such as solar energy, can significantly reduce this pollution. The amount of electricity produced from solar energy can be maximised by placing the solar power plant in an area with high direct horizontal irradiance (DHI). Solar irradiance predictions will help to design and build solar power facilities very efficiently. However, accurate prediction is a challenge. In this study, the deep learning approaches such as single-layered LSTM and stacked LSTM variations like two-layered LSTM and three-layered LSTM are used to forecast DHI.
Celebrity Face Matching Using Deep Learning

Face detection and face recognition are hot topics in computer vision and have many real-life applications. In this paper we present a celebrity face matching system to match a random human face with celebrities' faces taken from the Pins Face Recognition dataset. Several deep learning models for face detection and face recognition are explored and compared. An initial accuracy of over 89.4% is achieved. A simple face alignment method is then applied which increases the accuracy to over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{99.26}\%$</tex> , We also apply K-Nearest Neighbours with cosine distance and 100 neighbours to make more robust predictions.
Explainable Computational Pathology for Survival Prediction in Hematologic Pediatric Patients

Pediatric bone marrow failure syndromes are therapeutically and diagnostically strenuous, costly and yet extremely risky to pediatric patients despite the necessity for pediatric survival. To make matters worse, the current approach to the treatment of hematology and blood disorders in pediatric patients is extremely patient-centred and uncertainly lethal. In this work, we computationally leverage Machine Learning and Artificial Intelligence techniques to predictively and interpretably identify important factors influencing the success or failure of stem cell transplantation in pediatric patients using SHapley Additive exPlanations (SHAP based on coalitional Game theory. We also demonstrate a more transparent approach to predict the survival of hematologic pediatric patients based on the significance and importance of survival determinant feature interactions before the cell transplantation is performed. For this role, Catboost, LigtGBM and XBoost algorithms obtained 82%, 92% and 94% accuracy respectively. As much as we validate Kawlak's hypothesis that increasing the CD34+ cells/kg dosage prolongs general survival time of patients without synchronous occasion of unpleasant events affecting patients' quality of life (Kawlak et al., 2010), we discovered that pediatric risk group and recipients' age are likely to be more influential determinants of prolonged survival as compared to CD34+ cell reception. This transparent predictive and preventive approach to pediatric medical transplantation theoretically beat the existing delayed interventional approaches of reactive pediatric hematology. It can potentially reduce child mortality and improve the survival of children with personalized medicine hence improving postnatal and child healthcare.
Smart Vehicle Rental Application using Blockchain and IoT

Nowadays, demand for rental vehicle sharing is increasing. People are preferring to rent vehicles instead of putting a lot of money in a new vehicle. This renting system is beneficial for not only the consumer but also to the owner of the vehicle. Many issues such as theft, use of vehicle, bad condition of vehicles are not considered in the conventional rental systems. With the help of blockchain and Cyber Physical Systems (CPS), these issues are resolved in this system. Blockchain has many advantages due to its peer to peer sharing network. Smart contracts in Blockchain technology are contracts that run on their own in which context for the buyer-seller consensus are inscribed directly into the code. This system proposes a smart vehicle rental system which works with Blockchain, CPS and Internet of Things (IoT). The system utilizes IoT sensors to collect automatic data about car usage in real-time. For transactions of both customer and owner, these data make car availability and usage more authentic and credible. The implementation of this system uses Ethereum, Solidity, Web3.JS, Distributed Applications (dApp), Truffle. The proposed rental vehicle system with blockchain provides better reliability, traceability and security to both the owner as well as consumer.
Evol-AutoNN: Evolutionary Automation of Neural Network

As the domain of data science keeps on growing it leads to the need for a tool that breaks the barriers for non-experts to get into the field of data science. In this paper, we introduce the project of automating the neural network with the Evolutionary algorithm in the name of Evol-AutoNN. Evol-AutoNN is a biomimicry approach to the prediction of tabular data using the well-known two biological concepts that came from nature. Biological Evaluation and the Neural Network that came from the human brain are the two concepts applied in this project. Biomimicry is a new procedure to do innovation that tries to give a strong solution to a human problem by mimicking the patterns identified in nature. It is developed to overcome the problem of finding the best initial setting for the neural network for a given problem by the user. Evol-AutoNN imitates the process followed in both natural selection and genetics. As such, this work entitles to be an important step in the automation of neural networks for tabular data.
Lossless Segmentation of Brain Tumors from MRI Images using 3D U-Net

2D computer vision and activities related to medical image analysis are being remarkably guided with the help of Convolutional Neural networks (CNNs) in recent years. Since a major portion of the available clinical imaging data is in 3D, we are inspired to further develop 3D CNNs for seeking the advantage of greater spatial context. Despite the fact that many FCNs are previously worked on, current 3D approaches still rely on patch processing due to the utilization of GPU memory, which limits the incorporation of bigger context information for improved performance. Using efficient 3D FCNs in MRI images without any data loss would result in more efficient disease detections. In this paper, we propose an approach to an efficient 3D U net segmentation technique for MRI Images using a lossless preprocessing of an MRI image dataset. Our proposal has the advantage of an impressive reduction of the required GPU memory for 3D Medical Image processing activities and that too, with an enhanced performance which is evaluated by the IoU (Intersection over Union) evaluation metric. Comprehensive experiment results performed with MICCAI BraTS'20 exhibit the viability of the presented strategy,
Experimental energy consumption analysis of neural network model compression methods on microcontrollers with applications in bird call classification

Abstract- Running deep neural networks on low-power micro-controllers has become increasingly practical with the advance-ment in algorithm and hardware design. However, hardware resource limitations such as battery capacity still remains as a bottleneck which prevents the deployment of large classifi-cation algorithms. Model compression techniques address this issue through algorithmic means. This work investigates model compression methods, pruning and quantization, to optimize a ResNet-18 model for bird call classification. The investigation identifies the contribution of the quantization resolution and pruning sparsity towards the energy consumption, power con-sumption, inference time, performance accuracy, computational complexity, and the memory size.
IoT and its Significance on Smart Technologies in the Health Sector - An Extensive Review

IoT seems to be the trending solution in all sectors notably because of the yield in productivity, efficiency, effective strategies, and results that are associated with adapting to this technology. These positive results are enormously experienced in one of the crucial sectors which determine and ensure the prolonged healthy life expectancy of mankind. It is well noted that a lot of work has been done on this topic in Academia and Corporate field all over the world but this paper will present a selective review that has been done so far by the Academic world as a scholarly article and a resource for the Health Sector in Fiji to earnestly integrate smart technologies in its architecture.
Real-time classification of transmission line inspection images based on edge computing system

The management of vast and redundant inspection images is developing into an increasingly critical challenge due to the expansion of power line distance and the complexity of inspection requirements. The classification of power line inspection images is the basis for other subsequent tasks and image management. With the development of deep learning, fast and accurate image classification has become possible. For this problem, this paper proposes a real-time image classification scheme based on edge computing modules. In this study, the image acquisition and edge computing modules are designed and the lightweight network MobileNetV3 is improved to make it more suitable for this task. The experiments show that the Top-l accuracy reaches 90.86% when the input image resolution is 672×672. The improved model processes an image on Jetson nano with a speed of 0. 1176s. Most of the categories can be classified with an accuracy of more than 90% when tested on 39 categories. The proposed method in this paper can be used for real-time fast classification of inspection images.