Reviewerly/exHarmony huggingface dataset:
Pseudonymous Identities Generated from RSS-Based Secret Key Generation in A Join Protocol Process

Join protocol in an anonymous authentication is an interactive communication between participating mobile nodes and an authorized entity such as a Group Manager (GM) to obtain some credentials before actively and anonymously involving in the system. Some keys are equipped to the participating mobile nodes in their credentials including some pseudonymous identities (PIDs) could be embedded. These PIDs are usually unique and their existence should be secured from the adversaries. In this paper, we adopted a received signal strength (RSS) based secret key generation between participating mobile nodes and GM to uniquely provide some PIDs. We introduce two scenarios of interactive join protocol mechanism between participating mobile nodes and the GM. First scenario when the GM is in an immobile position and the second scenario when the GM is in mobile position. Performance evaluation is evaluated by varying the traffic conditions as communication impairment between mobile nodes and GM in the first scenario, and mobile nodes’ speed from 20 km/h to 60 km/h in both scenarios with ping time interval from 7 ms to 20 ms. The evaluation result showed that the PIDs generated properly with the number of PIDs ranges from 6 to 10 in the first scenario, and 3 to 5 in the second scenario. The highest correlation 0.99 is achieved, whilst the lowest one is 0.90. Meanwhile, utilized quantization worked properly for both two scenarios with 3000 data input in the first scenario and 6000 in the second scenario. Furthermore, both scenarios successfully achieve zero key disagreement rate (KDR).
A Blockchained Incentive Architecture for Federated Learning

The naive use of Federated Learning (FL) in a distributed environment exposes it to a risk of corruption, whether intentional or not, during the training phase. It happens because of the lack of monitoring of the training increments and difficulty of checking the quality of the training datasets. A very common type of attack of this type is Model Poisoning. To improve the security of the FL structure, we propose a decentralized FL framework based on blockchain, that is, a blockchain-based FL framework to increment the system security using an incentive mechanism to reward good trainers in the form of tokens. The system modeling will be presented as well as its implementation in the Mininet simulator. The validation tests performed to attest its accuracy were executed using the MNIST dataset.
Human Target Distance Estimation System Using Mono-camera On Human-Following Mobile Robot

A reliable and accurate target distance estimation system is very much needed in the human-following mobile robot system. Because the distance estimation system is used as input to control the mobile robot so that it can follow the human and stably maintain the distance. In other studies, additional proximity sensors have been used, such as LiDAR, ultrasonic, and laser range sensors. But the use of additional proximity sensors requires more power and for components that are more accurate usually also require more power, thus saving power consumption is an important aspect of human-following mobile robots because of the limited battery capacity. In this paper, a human-following mobile robot system is proposed that uses a mono-camera as the primary sensor without additional sensors for the human target distance estimation to save power consumption. By using Google’s human detection framework, Mediapipe, which can capture key points of the human body, this distance estimation system can be more efficient. Because it does not require additional energy by using the same output from the Mediapipe detection results, unlike using additional proximity sensors. The test results show that the use of the proposed method does not increase the power used during the detection and distance estimation process, and the results of the estimated distance have a small error with an average error of 1.3% with the human target being at a distance of 60cm to 180cm.
Symmetric Key Generation Use Received Signal Strength for V2V Communication

This paper proposes a symmetric key generation scenario to secure information on V2V (Vehicle-to-Vehicle) communications. This research uses the Raspberry Pi 3 as an OBU (On-Board Unit), which is installed on each vehicle. The key generation mechanism uses the Ad-Hoc network to get the RSS (Received Signal Strength) value between two communicating vehicles for further processing to produce an equivalent key between vehicles. The key that has been generated is used to secure the information so that it cannot be captured or stolen by others. When retrieving RSS data, various speed and ping time interval scenarios are used to see system performance. Based on the test results, it can be seen that the Kalman Filter can increase the correlation of RSS data up to 0.967. The resulting key can produce the highest KGR of 9.600 bit/s, and the KDR in all scenarios can reduce to 0%.
Hydrometeorological Disaster Vulnerability Mapping in East Java Using Hierarchical Clustering

East Java Province is an area with a high increase in cases of hydrometeorological disasters every year with as many as 620 disaster cases occurring. The absence of information related to the level of vulnerability of an area to hydrometeorological disasters has resulted in the magnitude of the risk and impact of disasters that occur on humans as well as increasing damage to facilities and buildings. In this study, a website-based application is created that can map the level of vulnerability of an area to hydrometeorological disasters in East Java. In conducting the mapping, several criteria are needed to determine the vulnerability of an area. The criteria are taken from the hazard/threats and vulnerability criteria. Then the criteria data are grouped using the Centroid Linkage method. This method groups the data based on the level of similarity of the data that is processed by calculating the centroid (middle) distance between the clusters so that the distance of the two clusters that are at least minimal will be combined into a new cluster. The vulnerability level is divided into 3 clusters, namely low, medium, and high which are visualized on a map with a period from 2016 to 2020. Cluster testing is done by calculating the silhouette coefficient value and it is known that the average of each year is 0.370168.
Natural Language Processing based Math Word Problem Solver and Synoptic Generator

Math word problem solution generation is a process where the machine itself will automatically generate/provide a final answer as well as a step-wise solution for the provided Math word Problem. This simply measures the machine's capacity and capability to comprehend and reason in natural language. This paper proposes a solution where various techniques are implemented, different models are tried and tested, and the scope is extended to cover multiple types of Math Word Problems. The domains worked on are speed, distance, time, profit and loss. The main purpose of this paper is to create a system with various domains in a hybrid framework which can accurately produce a step-wise solution for the given Math Word Problem. Based on the different tested approaches, Random Forest (used for Domain Classification) + 2 custom Transformer Model (used for Equation and Formula Generation) are found to be the most suitable method which provided the best accuracy.
Construction of College Sports Online Guidance System based on Real-Time Network Flipped Classroom Information Flow Graph Algorithm

This paper analyzes the current situation of the current college physical education teaching mode, and focuses on the construction of the MOOC-based college physical education "flipped classroom" teaching mode, which mainly includes the preparation of pre-class learning resources, the independent learning of students, and the systematic teaching of knowledge by teachers in class. In order to fully guarantee the security of the computer in the actual application process, avoid the risk of computer network information security leakage, and comprehensively improve the efficiency of social development, a network security proxy server can also be established than the traditional physical education teaching design process.
Quick Aid: Mobile Application for Product Rental

This research study aims to design and develop Quick Aid app with an innovative idea of renting products in order to help people by supplying the essential/non-essential things. The primary objective is to create a product rental app, which enables the user to rent a product or require a service, so that the product/service can be used by the requestor. This system could also help in building a strong relationship among people and solve the problem of people not owning non-essential things readily. It is always difficult to find a product for rental or short term. Quick Aid app will make it easier to get products and services by utilizing the help of other users. Quick aid delivers simple yet innovative way to request items or services by using a simple online form from the user. The placed online request is then notified to every other user of the app, so that the interested people could help in fulfilling the request.
Opinion Mining based Identifying Deceptive Reviews

People nowadays are using online reviews to purchase products online. These reviews affect the sales of the product and play a major role in the company’s profits. The gradual increase in the number of social networks is paving the way for the checking of truthfulness of various reviews that are conducted online. Also, some cyber criminals are used to target some of the products, organizations or even individuals. The tasks performed by such individuals are called as Opinion Spamming and the individuals are called as Opinion Spammers. This process of Opinion Spamming not only damages the money and reputation of the organizations. But also plays a crucial role in the politics. As technology is getting highly sophisticated, it is becoming very difficult to identify such Spamming activities. This paper concentrates on various detection techniques of Opinion Spamming and suggests some of the ways to prevent it.
Secured Health Data Sharing System using Cloud Technology and Blockchain with Machine learning

This paper proposes a systematic approach for storing and managing personal health related dynamic data using the concept of blockchain supported with cloud storage to store huge amounts of records with high integrity and security. In addition, this paper gives out a data segregation and quality inspection module which works on the concept of Naive Bayes Classification algorithm. The main objective of the paper is to let people construct, manage and stake their records with high accuracy in security, using the methodologies of Data Protection. Firstly, the data is segregated into different sections based on four segregation methods under Naive Bayes classification for the data entered into the system by the user. Secondly, this paper crushes down the limitation of size by using blockchain along with cloud storage. The paper also stores the personal health data after encrypting it with the use of AES Encryption in the cloud based on the concept of blockchain. Thirdly, a data quality and segregation module are included in this paper to have a control over the quality of the data using a Machine Learning technique called Naive Bayes Classification. This paper also gives a systematic approach for scholars and data scientists to have data of high quality for research purposes.
Performance Analysis of Geospatial Serverless Computing for Geospatial Big Data Analysis

Nowadays, real-world applications are primarily concerned with collecting the physical characteristics that correspond to various geographical phenomena, and information plays a critical role in the analysis and forecast of various occurrences that occur. Real-time analysis is used in a variety of application domains, including traffic flow monitoring, healthcare monitoring, and so on. This research study has considered the existing geospatial serverless computing framework and proposed an analytical model to enable the geospatial serverless platform to compute and operate at different workloads and the tradeoff between the cost and performance are then determined by the users’ preferences. Here, the accuracy of the model is validated in Amazon Web Service (AWS) Lambda environment and the model calculations are shown based on the performance metrics by including average response time, probability of cold start as well as the average amount of functional instances in the steady state by using the queuing theory. Here, the performance modelling is carried out in the geospatial serverless environment for different workloads, which results in delivering better performance at a minimal cost.
Sports Competition Data Collection and Intelligent Training System based on Ultra-High-Speed Cameras

A new practical intelligent controller based on ARM+FPGA architecture for high-speed real-time data acquisition application is researched, designed and implemented. This paper expounds the realization of hardware system technologies such as the main processor ARM minimum system, the coprocessor FPGA minimum system, and the communication interface between ARM and FPGA. After optimization and improvement, a new sports training motion video analysis system is proposed. Through detailed analysis of the overall design and detailed design of the system, the system performance test is further carried out. Based on ultra-high-speed cameras and data rapid capture and analysis of sports data game feature extraction.
Face Mask Identification using Transfer Learning Techniques

Coronavirus (COVID-19) has become the major health issues in the current scenario. Symptoms start with the common cold and end with fever. These viruses affect the respiratory part of the human body. Hence, wearing of face mask is essential. Usage of masks in public places has become mandatory. Due to the significance of the face mask in public places, a transfer learning approach has been proposed for the identification of face masks. The system comprises feature extraction and classification. For feature extraction, transfer learning is used and for classification, Machine Learning is used (ML). Mobile Net and VGG 16 (Visual geometric Group) is used as feature extractor, similarly, SVM and Random Forest are used as a classifier. Performance metrics are evaluated to determine the highest accuracy from the resultant model. The high optimal model is determined by comparing its accuracy. Of the four models, VGG-16_SVM gives high accuracy of 89% and Mobile Net_ random Forest gives the second most accuracy of 87%. Out of all VGG-16,_ SVM gives the best classification output, which will be the most optimum model for face mask identification. The dataset is procured from Masked Face - Net dataset, it consists of human faces with correct and incorrect mask data based on Flicker-Faces-HQ (FFHQ).
Secure Cryptosystem for Preventing CCA Attack

The use of intermediary re-encryption cryptosystem rescrambles the message each time the client time sends it. It expands the calculation time and makes ready for sending invalid re-scrambled figure message to the client that builds the opportunities for CCA (Chosen Ciphertext Attacks). In proposed framework, client can make their own private and public key pair from their qualifications and timestamp. In the wake of refreshing the public key with administrator, the beneficiary public key is utilized to send the mystery key for start to finish encryption. It eliminates the twofold layer encryption for each message without changing the security strategy of the clients. It makes use of an effective intermediary re-encryption (PRE) plot in the ICN structure to help reduce the overhead on the client side while maintaining adaptive information distribution among supporters and, surprisingly, their cooperators. It creates an entrance control strategy for registered clients, assigns a group of supporters to this arrangement who can access the substance, then scrambles the substance thing using a Shamir’s Secret polynomial mystery consistent and the switch’s portion, i.e., creates an encoded empowering block, and sends the empowering square to the capacity switch.
Improved Honey Encryption to Protect the Internet users from Various Attacks

In recent months, there has been a rise in security issues as well as data breaches, which has led to increased anxiety regarding the safety of online banking. The existing authentication method for internet banking, which is entirely dependent on a password, is not robust enough to withstand the recent wave of successful password guessing and cracking attacks due to the fact that it is solely dependent on a password. As a potential remedy to this problem, the authors of this paper proposed a method known as Honey Encryption (HE), which adds another layer of protection on top of the user authentication measures that are already in place. When a malicious user guesses the password for a cloud account, the HE algorithm immediately generates false bank data that is impossible to distinguish and then directs the attacker to an account that has a fake password. This prevents the attack from being able to determine whether or not the password that was guessed is valid. As a direct consequence of this, cracking attacks and guessing passwords have become increasingly challenging. In addition to that, this research investigates different attack models and potential defences for password-based authentication schemes.
Application of Various Sensors in Rehabilitation Program and Clinical Practices

The electrical apparatus that conveys signals from one energy part to another is known as a sensor. Sensors have already been classified in many ways by various authors and agencies. In the beginning division, the sensors are distributed into active and passive categories. The further that follows is based on the phenomena of reformation, or the input and output. Thermoelectric, Photoelectric, Electrochemical, and other common conversion processes are a few examples. The ultimate sorts of sensors are analogue and virtual sensors. The subsequent may be a list numerous styles of sensors that are usually utilized in various applications of these sensors are used for measure one among the physical properties like Temperature, Resistance, Capacitance, Conduction and so on. Accelerometer, Infrared Sensor, Pressure Sensor, Light Sensor, Ultrasonic Sensor, Smoke, Gas, and Alcohol sensor, Touch Sensor, Colour Sensor, Position Sensor, Hall Effect Detector, Sound Sensor, Tilt Sensor, Strain and Weight Sensor, Temperature Sensor are a few illustrations.
Analysis of Deep Learning Techniques to Investigate and Support Diagnosis of Virus Borne Diseases

The poorest individuals, who often reside in remote, rural areas, urban slums, or conflict zones, are disproportionately affected by neglected tropical illnesses (NTDs). Arboviruses are one of the most major subgroups of mosquito-borne NTDs. In Latin America and South America, three kinds of arboviruses impact a major section of the population. The names of these viruses are Dengue, Chikungunya, and Zika. The clinical identification of distinct arboviral infections is a difficult task. Due to the continuous circulation of several arboviruses, all of which have symptoms that are very similar, test results are often erroneous. This study explores the most current advancements in Machine Learning (ML) and Deep Learning (DL) models for the automated classification of arboviral diseases. According to the results, the major focus of the current research is on categorizing dengue using tree-based machine learning approaches. Utilizing a high-quality clinical decision support system for the treatment of arboviral infections may enhance the overall quality of the clinical process, hence increasing the diagnostic and therapeutic accuracy. It should facilitate clinicians' decision-making and, subsequently, increase resource use and the quality of life for patients.
Computer Vision based Student Behavioral Tracking and Analysis using Deep Learning

Over the past couple years, The Covid-19 virus has triggered a global pandemic, requiring us all to remain isolated in our homes. Students and professionals were forced to move to the virtual world to resume their academic activities. This has led people to use video conferencing applications on a massive scale. However, it has been a challenge to monitor many students at the same time by a single invigilator/faculty thereby, the student engagement and interaction has dropped in a rapid scale. To overcome this challenge, an open-sourced modular system is proposed to Face identification for face spoofing using CNN and students’ gaze tracking with head positioning & eye tracking in real-time virtual video conferencing applications using computer-vision & deep-learning. With a single-Shot-Multibox detector, using ResNet-10 Architecture as the foundation, the student’s face is recognized and detected. Then to take facial landmarks, pre-trained Histogram Oriented Gradients (HOG) and linear SVM object detectors are used. With the captured features, the eyes are detected using dlib and the centers of eyeballs are extracted using OpenCV eye classifiers. This paves the way to perform eye tracking and gaze tracking. All the stated Machine learning modules’ output are aggregated and translated to a graphical representation as the final analysis of the students’ behavior in the virtual sessions.
Rain Streaks Detection and Removal from an Image using Canny Edge Detection and Combination of Filter

Raindrops that cling to a window’s glass can drastically limit a scene’s visibility and degrade the perceived image quality. The ability to detect and remove rain drops will assist many computer image applications. Detection techniques such as L0 gradient, k-means clustering, RGB separation, and removal techniques such as median filter were employed in the preceding methodology. This work describes a new Rain drop identification and removal technique based on YCBCR, canny edge detection, novel filtering that combines median and Gaussian filters.
Chaotic analysis and entropy estimation of the entropy source based on semiconductor superlattice chaos

A true random number generator (TRNG) is a fundamental security primitive. Recently, the semiconductor superlattice (SSL) has been investigated as an ideal physical entropy source with high speed. It shows multi-degree freedom nonlinearity based on the effects of quantum mechanics. However, there is still a lack of systematic evaluation schemes for SSL. In this study, a chaotic assessment model for SSL is proposed. Chaotic characteristic assessment and nonlinear dynamics estimation are carried out using the model. Furthermore, a set of entropy estimation procedures are introduced to evaluate the uncertainty of the SSL. The estimation results demonstrate that SSL is chaotic, highly complex, and nonlinear. SSL shows stable performance and has an entropy rate greater than 50% with 8 sample bits. Moreover, the throughput of the assessment prototype reaches 300 Mbit/s. In brief, SSL is an excellent physical entropy source for high-quality and high-speed TRNG.