Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Multiple Linear Regression and K Nearest Neighbours using Machine Learning

In recent times, Machine Learning methods are widely used to handle large and complex data to generate interesting patterns and trends. Supervised Learning methods are generally used to classify different types of real life datasets. In this paper, the two methods Multiple Linear Regression and K Nearest Neighbours have been used to classify the quality of wine and compare the accuracy. As a result, it is found that K Nearest Neighbours gives the good accuracy. The calculated Mean Squared Error (MSE) and calculated Root Mean Squared Error (RMSE) give the model perfection. Result shows that the value of MSE and RMSE applying K Nearest Neighbours (KNN) is higher than Multiple Linear Regression (MLR). The classification performance of the methods is compared with their accuracy. Based on these methods, the highest accuracy of KNN with K = 5 is 0.9444. Meanwhile, for the Multiple Linear Regression, the accuracy reached to 0.6657. Also, MSE and RMSE are calculated as 0.0555 and 0.2357 for KNN with k=5. Multiple Linear Regression has MSE (0.1692) and RMSE (0.4113). The experimental result shows that KNN can be used as alternative method for predicting the new instances. From UCI Machine Learning Repository, the wine dataset is taken which are tested in this research paper.
Shot Segmentation and Replay Detection for Cricket Video Summarization

Interest in video summarization is increased recently. People are fascinated by watching sports videos, but as videos are lengthy to watch and exciting events only occupy a small segment of the video, it is needed to represent such a long video in its precise and summarized view. Shot segmentation and replay detection are important tools in video analysis and can help automate many tasks related to video processing and content creation. It is noticed that exciting events in sports videos are usually replayed. We proposed an approach to detect cricket video shot boundaries and extract replay segments from the cricket video sequence. The proposed technique is simple and computationally effective. The results show the effectiveness and usefulness of an approach.
Lifeline Consensus: Protect Small-scale Blockchain From Typical Attacks

Blockchain is a distributed network system known for its anonymity, security, and decentralized nature. While blockchains with robust networks such as Bitcoin and Ethereum are proving immutable to attacks, many others are vulnerable to security threats. To address this issue, we proposed the Lifeline consensus, which aims to protect small-scale blockchains by delivering safety from a strong leading blockchain to a relatively vulnerable following blockchain using a new type of data called Coil. Coil carries the proof-of-work evidence created by miners from an arbitrary following blockchain, which the leading blockchain then gathers and generates immutable blocks that contained them. Miners from the following blockchain can use the coils in the leading blockchain for validation, thereby protecting small-scale blockchains from typical blockchain threats such as 51% attacks and selfish mining. We implemented our protocol and encountered adversaries in the local network, and analyzed its performance. The results showed that the Lifeline protocol is dependable with a Trusted Leader. These findings suggest that our protocol has the potential to improve the security of small-scale blockchains and enhance the overall performance of the blockchain network.
An Efficient Hardware Design of Prime Field Modular Inversion/Division for Public Key Cryptography

In this paper, we proposed an area-efficient hardware implementation of modular inversion/division, which is a complex and crucial component in elliptic curve cryptography (ECC). Our modular inversion/division is based on our modified binary inversion algorithm. The proposed hardware implementation of modular inversion/division improves the area efficiency and was designed and implemented on Xilinx Spartan-6 and Virtex-7 field-programmable gate array (FPGA) platforms and simulated with TSMC 90nm and 180nm technology nodes. Our proposed modular inversion/division is suitable for prime fields used in public key cryptography, including the NIST-recommended elliptic curves. It occupies 618 slices and 607 slices in Xilinx Spartan-6 and Virtex-7 FPGA platform, computes in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10.6\ \mu \mathrm{s}$</tex> and 6.45 over the prime filed P-256, at a maximum operating frequency of 33.76 MHz and 55.49 MHz. It occupies 23997 GE and 28471 GE, computes in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.25\ \mu \mathrm{s}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.43\ \mu \mathrm{s}$</tex> over the prime fields P-256 at a maximum operating frequency of 285.71 MHz and 147.06 MHZ, respectively for TSMC 90nm and 180nm technology node implementation.
Enhancing Web Application Efficiency: Exploring Modern Design Patterns within the MVC Framework

The growing demand for web applications across various sectors has led to challenges in developing efficient and high-performance online apps. The traditional approach to web app development may struggle to meet the performance requirements of modern online applications. To address this, the Model-View-Controller (MVC) framework has become an essential tool for creating interactive online apps. While the MVC framework has limitations, several new design patterns have emerged from its architecture that offers improvements. This paper explores some of the thoroughly researched design patterns of the MVC framework. The Composite View pattern merges several views to facilitate the development of complex user interfaces, while the Intercepting Filter design pattern filters and processes requests before they reach the controller, enhancing security and speed. The Service Layer design pattern creates an abstraction layer between the data access layer and the controller to increase scalability and maintainability. Employing MVC-based design patterns can help developers enhance the functionality and efficiency of web apps, ensuring they meet the expectations of modern consumers.
A Low-Power Dual-Core Motion Estimation Chip Design and Validation for a Wireless Panoramic Endoscopy

A low-power motion estimation chip is designed for a wireless panoramic endoscope system. This chip consists of two motion estimation cores and is implemented by a dual-Vdd low-power technique. The proposed technique is efficient in decreasing power consumption without reducing the operation frequency of the chip. From the full-function chip measurements, this dual-Vdd chip can reduce power consumption by 20%∼40% than the operation in single-Vdd at different clock frequencies.
Edge Computing-based Adaptive Machine Learning Model for Dynamic IoT Environment

With the advent of IoT and smart systems, edge computing coupled with machine learning (ML) techniques are becoming imperative to locally process and analyze the heterogeneous data generated from various IoT devices in real-time. The most common problem in dynamic IoT environment is performance degradation, mainly due to virtual concept drift (VCD). The issue of VCD often incurs, in dynamic IoT environment, when statistical properties of the input features change over time and make existing ML models obsolete or degrade models' performance and efficiency. Thus, the need for adaptive ML models. To facilitate this endeavor, our main objective is to create an adaptive ML model for resource-constrained edge computing devices to address the VCD issues in dynamic IoT environment. In this paper, we present a proof-of-concept adaptive ML model for edge computing based on a CNN single classifier with a real-time transfer learning method using fine-tuning. We also present our problem formulation and preliminary experimental results for problem validation.
MPCAuth: Multi-factor Authentication for Distributed-trust Systems

Systems with distributed trust have attracted growing research attention and seen increasing industry adoptions. In these systems, critical secrets are distributed across N servers, and computations are performed privately using secure multi-party computation (SMPC). Authentication for these distributed-trust systems faces two challenges. The first challenge is ease-of-use. Namely, how can an authentication protocol maintain its user experience without sacrificing security? To avoid a central point of attack, a client needs to authenticate to each server separately. However, this would require the client to authenticate N times for each authentication factor, which greatly hampers usability. The second challenge is privacy, as the client’s sensitive profiles are now exposed to all N servers under different trust domains, which creates N times the attack surface for the profile data.We present MPCAuth, a multi-factor authentication system for distributed-trust applications that address both challenges. Our system enables a client to authenticate to N servers independently with the work of only one authentication. In addition, our system is profile hiding, meaning that the client’s authentication profiles such as her email username, phone number, passwords, and biometric features are not revealed unless all servers are compromised. We propose secure and practical protocols for an array of widely adopted authentication factors, including email passcodes, SMS messages, U2F, security questions/passwords, and biometrics. Our system finds practical applications in the space of cryptocurrency custody and collaborative machine learning, and benefits future adoptions of distributed-trust applications.
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning

Deploying machine learning models in production may allow adversaries to infer sensitive information about training data. There is a vast literature analyzing different types of inference risks, ranging from membership inference to reconstruction attacks. Inspired by the success of games (i.e. probabilistic experiments) to study security properties in cryptography, some authors describe privacy inference risks in machine learning using a similar game-based style. However, adversary capabilities and goals are often stated in subtly different ways from one presentation to the other, which makes it hard to relate and compose results. In this paper, we present a game-based framework to systematize the body of knowledge on privacy inference risks in machine learning. We use this framework to (1) provide a unifying structure for definitions of inference risks, (2) formally establish known relations among definitions, and (3) to uncover hitherto unknown relations that would have been difficult to spot otherwise.
Comparison of Machine Learning Algorithms for Position-Oriented Human Fall Detection

Smart sensor systems are increasingly pervading all kind of application fields such as in industry, ambient assisted living, or lifestyle accessories. In this work, a smart system for position-oriented human fall detection is investigated using various machine-learning algorithms for data processing and evaluation. Data from an inertial measurement unit is combined with data from visible light positioning methods to achieve position-based fall detection. Furthermore, an experimental setup and test methods were created to generate appropriate datasets for this analysis. The classification accuracy is compared with three machine-learning algorithms commonly used for such tasks, which are Decision Tree, Naïve Bayes and Support Vector Machine. It is demonstrated that the combination of data from the two sensor systems can improve the recognition accuracy beyond 99% in the best case.
The Corona Graph for Symmetric Encryption Schemes

A special graph is defined in this work as new concept of graph theory. This graph is named as corona graph (CG) to design new versions of the symmetric encryption (SE) schemes. The ciphertexts of the plaintexts on designed versions are sent to the receiver entity as the CG. The CG is created based on a simple graph with original vertices and edges. Each original vertex has other vertices to form the corona vertices. A CG is generated depending on the random choices of the plaintext letters. The purpose for using the CG is to increase the security level of the SE schemes in comparison with previous encryption schemes. New experimental results of the proposed corona graphic symmetric encryption (CGSE) schemes are introduced through two study cases. On the CGSE schemes, the security issues are determined. The CGSE schemes consider as more suitable for secure SE communications.
A Study on Secure Quantum Computing for Healthcare System

Medical care system broadly use Information technology (IT) for framework confirmation (advanced marks), web surfing, messages, texting, safeguarding information very still, Voice over Web Convention (VoIP) communication, and cell communication. To safeguard patient ID and medical care data, cryptographic frameworks are broadly used to get these information from malignant outsiders (fraud). In present healthcare system, we noticed sensible progress in productive stockpiling of the data of patients and their family, in its opportune recovery, and guaranteeing its wellbeing from enemies. Be that as it may, the information are expanding quickly and the ongoing computational frameworks could lack in the near future. Quantum computing examination could prompt “quantum jumps,” and it could have unexpected repercussions in the clinical field. This method can possibly be utilized in an expansive scope of settings, where some incorporate the improvement for novel medications, the personalization of clinical medicines, and increased efficiency of DNA sequencing. This Research paper will enlighten us on how Q.C is changing things all over Healthcare Industry and help understand why it's important to go for Q.C rather than Current Technology.
Ising Machine Based on Bifurcations in a Network of Duffing Oscillators

Ising Machines have been extensively explored lately for developing new nonconventional computing architectures. A recently proposed approach, based on simulating a dynamical system exhibiting bifurcations, has shown promising performance. Inspired by this concept, we propose using bifurcations in a network of coupled electrical Duffing oscillators to realize an Ising Machine. Numerical simulations of large Duffing oscillator networks, solving various Max-Cut problems, demonstrate the potential of our proposed approach for realizing Ising Machines based on bifurcations. It also establishes a new direction towards analog Ising Machine architectures.
BayBFed: Bayesian Backdoor Defense for Federated Learning

Federated learning (FL) is an emerging technology that allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks such as backdoor attacks. Consequently, a variety of defenses have recently been proposed, which have primarily utilized intermediary states of the global model (i.e., logits) or distance of the local models (i.e., L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> −norm) with respect to the global model to detect malicious backdoors in FL. However, as these approaches directly operate on client updates (or weights), their effectiveness depends on factors such as clients’ data distribution or the adversary’s attack strategies. In this paper, we introduce a novel and more generic backdoor defense framework, called BayBFed, which proposes to utilize probability distributions over client updates to detect malicious updates in FL: BayBFed computes a probabilistic measure over the clients’ updates to keep track of any adjustments made in the updates, and uses a novel detection algorithm that can leverage this probabilistic measure to efficiently detect and filter out malicious updates. Thus, it overcomes the shortcomings of previous approaches that arise due to the direct usage of client updates; nevertheless, our probabilistic measure will include all aspects of the local client training strategies. BayBFed utilizes two Bayesian NonParametric (BNP) extensions: (i) a Hierarchical Beta-Bernoulli process to draw a probabilistic measure given the clients’ updates, and (ii) an adaptation of the Chinese Restaurant Process (CRP), referred by us as CRP-Jensen, which leverages this probabilistic measure to detect and filter out malicious updates. We extensively evaluate our defense approach on five benchmark datasets: CIFAR10, Reddit, IoT intrusion detection, MNIST, and FMNIST, and show that it can effectively detect and eliminate malicious updates in FL without deteriorating the benign performance of the global model.
Data Science – A Compendious Study on Statistical Methods and Visualization Techniques

Knowledge and insights may be gleaned from both organized and unstructured data through the use of Data Science, which is itself a multidisciplinary discipline. Finding patterns and trends in data requires a wide range of methods, including but not limited to cleaning, transformation, analysis, and visualization. A data scientist's primary responsibility is to transform unstructured data into a form suitable for analysis through the processes of processing and cleaning. This approach utilizes several statistical procedures, such as calculating the mean, mode, median, standard deviation, correlation, regression, and hypothesis testing. These techniques are useful for discovering abnormalities, missing values, and outliers in data, as well as for eliminating them. When the data has been cleansed and organized, it will be analyzed using statistical and machine-learning techniques to draw conclusions and make forecasts. To find patterns, trends, and correlations in the data, this study may make use of methods including clustering, classification, regression, and time series analysis. Data visualization is crucial to data science because it facilitates the dissemination of ideas and conclusions to stakeholders who may not have technical expertise. Scatter plots, bar charts, heat maps, and network diagrams are just a few of the data visualization methods that may be utilized to convey information interestingly. A wide variety of fields, from economics and finance to medicine and social media, can benefit from data science. Data science is useful in business for several purposes, including cost reduction, client retention, and discovery of untapped markets. As a field of study, data science has the potential to improve healthcare by aiding in areas such as disease detection, treatment, and the creation of new medications. Data science may be applied to social media to study user behavior and sentiment and to spot and stop criminality. It is a dynamic and promising new area that has the potential to revolutionize many different markets. Data visualization, machine learning, and statistical analysis all work together to help data scientists extract useful information from large, messy datasets.
Performance And Security Enhanced Improved Hill Cipher

Hill cipher is one of the classical ciphers which is considered as a secured one till date apart from its vulnerability of known plaintext attack. Because of its simplicity, in recent days also it is used as one of the building block of cryptography and steganography based algorithms. Many research articles are available to minimize the known plaintext attack vulnerability of Hill cipher. One of the research works suggests to use a second key which varies exponentially with block numbers of the plaintext. Though the proposal is very simple, it is a time consuming process. Furthermore, it has some weak blocks which deteriorates the security. In this research article both issues are rectified by the inclusion of a look-up-table method. The proposed method saves power and enhances the speed by slightly compromising with area. Implementation results show that the proposed method reduces the performance complexity from O(nlogn) to O(n).
Case Review of Implementing IT Camp Programs Connected with Eco-Environmental Contents Development and Performance

This study aims to help students carry out their creative projects by producing media through design-based coding camp while awakening the importance of the ecological environment. The subject of this study is two IT camp programs that produce ecological environment contents and link performances for elementary, middle and high school students. The first case is a camp where about 50 elementary and middle school students near Suncheon City come up with ideas related to the ecological environment for 6 weeks from August 2021. The second case is a program in which five high school students from a public alternative school compose an ecological environment story and produce props for three weeks from September 2021 and then perform a reading performance. As part of the case study, the results of analyzing the students’ outputs, student activity videos, and instructor interviews are as follows. First, elementary and middle school students developed the ability to carry out creative projects through convergence activities such as producing and performing scratch animations and Arduino LEDs after creating a story about ecological environment conservation. Second, high school students generally showed the importance of collaboration and design-based project execution capabilities through ecological environment content production activities and performance experiences. In addition, the characteristics of IT-based convergence camps and implications for the quality improvement of camp programs were presented.
Suggestions for Convergence and Complex Teaching and Learning Methods and Curriculum Reorganization through Historical Development of the Limit Concept of Calculus in Field Education

In the 21st century, the knowledge and information society is rapidly changing and knowledge is exploding due to the unthinkable development of science and technology. Considering this, it is only natural that there should be internal and external changes in the existing secondary mathematics textbooks and university liberal arts mathematics.&#x0D; The purpose of this study is to examine the fusion and complex elements of the historical development of extreme concepts in the Greek, Middle, Modern, and 17th and 19th centuries, and to suggest the convergence and learning methods and curriculum that can be presented to students when teaching calculus in field education.&#x0D; Through this study, we find that the limit concepts currently dealt with in the secondary education field are historical products created by convergence over a long period of time, and, according to the learning goal of the limit concept, teaching and learning methods such as project-based learning (PBL), creative problem-solving learning, heuristic learning, mathematicaliation inquiry learning using ICT, and question-and-answer and discussion classes were proposed. In addition, this study only discussed limit concepts, but suggests that other problems such as number concepts, equation and function concepts, geometric problems, probability and statistics problems in secondary school content are long-standing historical products. And it suggests the need to reflect this when reorganizing the subject.