Reviewerly/exHarmony huggingface dataset:
Optimizing Apache Kafka Deployments - Configuration customization is all you need

Apache Kafka is an open source publish-subscribe message broker and distributed logging tool that has a widespread adoption in the modern world by companies like Uber, Airbnb, Linkedin and many more. Delivering extreme high volume event data to diverse consumers via a publish-subscribe messaging system with several out of the box optimizations on the 4 major pillars of latency, throughput, durability and availability makes this tool so much popular in the IT landscape. Kafka has the option to customize most of its configuration related parameters to optimize it the most for our use case. Some of the use cases have millions of writes per second, hence it needs to handle huge billions of volumes of data which cannot be done easily by a traditional database or key-value store pair. Some use cases like a chat application demands very low latency where storing the data can be delayed, but the recipient needs to get the sender’s message delivered as soon as possible. Some use cases like a banking application requires high durability over latency and throughput, where the data records must be stored in spite of any system failures. Latencies over 500ms can be tolerated but not data loss in any possible case. And finally some use cases like emergency systems, autonomous vehicles, these are some applications that have to be available for communication all the time and it really doesn’t demand low latency or high throughput or high durability.So the crux of this is, we should know to configure the application depending on our use case. In this paper we are comprehensively going to talk about how we can optimize Apache Kafka correctly depending on these 4 major pillars. After immense testing and brainstorming we have come up with a set of configuration parameters for each of these pillars which would provide a headstart to optimize the kafka deployment. Kafka’s ability to scale efficiently, but it can become a bottleneck if not properly configured. We have benchmarked the results along with those provided by Microsoft and Confluent. Instead of proposing, using graphs and metrics we imply it in a prudent way.
Cryptocurrency PricePrediction using LSTM

This paper focuses on utilizing the Long Short-Term Memory (LSTM) algorithm to forecast the prices of cryptocurrencies. that is implemented through the model, which will allow us to keep tabs on the fluctuating prices of the various coins that are available. As various cryptocurrencies are available for trading and investment and they are regarded as a type of digital asset. Because trading and investing should be done after the knowledge of the market and trends, So, to solve this problem there should be analysis before trading any pair. Our goal is to present accurate prediction based on the dataset of the selected cryptocurrency. Hence, the proposed model is built and it concentrate mostly on technical analysis of the crypto coins which is the major indicator and most suitable for the prediction models. The proposed LSTM model achieves a variance regression score of 0.95 on test data of the Bitcoin which serves as a representative cryptocurrency that is used in the model.
Cybersecurity Awareness Assessment Based on Bangladeshi People Perspective Using Machine Learning Approaches

The demands placed on cyber security continue to rise. When data is compromised from a company, organization, or individual, we realize how crucial it is to have a basic understanding of cyber security. The company should provide their employees with training on how to stay secure online. In light of the current circumstances, it is very important for everyone. Those who have less awareness on it are contributing to a rise in cybercrime. The most significant cyberattack against a developing country. As a result of this, hackers and spammers target individuals in order to execute out attacks, and Bangladesh is vulnerable to an excessive number of attacks on its online and mobile banking transactions. In essence, they apply a social engineering approach in order to get the password. In this study, we gathered data from a survey in which we asked certain questions about fundamental levels of awareness. This research has a total of 456 participants who have answered the questions. In order to get the highest level of accuracy, we employ a machine learning method.
ECAPA-Based Speaker Verification of Virtual Assistants: A Transfer Learning Approach

Speaker Verification technology is crucial in identifying individuals through their unique voice characteristics. However, the increasing use of speech assistants has posed new challenges for this technology. To address this, transfer learning is employed using the ECAPA-TDNN model trained on human voices from the VoxCeleb2 dataset. This study compares intra-voice assistant variations and proceeds to inter-voice assistant comparisons. In intra-pair comparisons, text-independent samples achieved accuracies of 83.33% (iOS versions) and 66.67% (Alexa versions), while text-dependent samples achieved 50% accuracy for both versions. In inter-pair comparisons (Alexa, Siri, Google Assistant, Cortana), accuracies of 100% (text-independent) and 80% (text-dependent) were observed. These findings showcase the effectiveness of transfer learning and the ECAPA-TDNN model for secure speaker verification in different speech assistant versions. The study provides valuable insights for enhancing speaker verification in the context of speech assistants.
A comparative analysis between three local descriptors

Literature reports comparative analysis between numerous descriptors (local) in form of their research article. These studies although achieve good results but the best combination of local, global and classification methods are missing in the earlier research. As a result, the performance of these are not as encouraging as it be in the harsh light variations. This work eliminate this demerit and executes the best combination of local, global and classification methods in Face Recognition (FR). Precisely, this work presents the comparative analysis of three local descriptors. These three local descriptors are 6x6 MB-LBP, MB-ZZLBP and MRELBP-NI. All three captures the macrostructure and microstructure information therefore their comparison is performed. The idea is to check which descriptor produces better results in harsh illumination variations. For all three descriptors global histograms are extracted, which develops histogram size of 256. For compression and matching FLDA and SVMs are utilized. It is MRELBP-NI which attains better results than other two, as result suggests. The best accuracy achieved by MRELBP-NI is 93.49% and 99.33% on EYB and YB datasets. All testing is conducted in MATLAB R2021a settings.
Credit Card Fraud Detection with Auto Encoders and Artificial Neural Networks

The popularity of credit card usage for payment has surged, but it has also led to a corresponding increase in credit card fraud, creating a significant challenge. The rapid advancement of technology and the prevalence of online transactions have provided fraudsters with numerous opportunities to engage in fraudulent activities, resulting in substantial financial losses. Consequently, it is imperative to devise effective strategies to minimize these losses. Fraudsters employ a diverse array of tactics, including deceptive phone calls, misleading SMS messages, phishing attacks, impersonations, and other methods, to illicitly obtain users’ credit card information. Their primary targets are individuals who are easily accessible and susceptible to credit card fraud. With the expansion of e-commerce and the proliferation of online platforms, the availability of online payment options has expanded significantly, thereby amplifying the risk of online fraud. To combat the escalating rates of fraud, researchers and experts are leveraging various machine learning techniques to identify and analyze fraudulent online transactions. The primary objective of these research endeavors is to develop a specialized fraud detection algorithm capable of examining historical customer transaction data and extracting behavioral patterns from the dataset. In our proposed model, we employ Autoencoders and Artificial Neural Networks to facilitate the extraction of distinctive features and the classification of anomalies.
SEOHFB: A Secure Environment for Organization based on Hyperledger Fabric Blockchain

Blockchain and hyperledger are decentralized approaches used for maintaining security, secrecy, and integrity of organization based business transactions. In our study we have built a permissioned blockchain using HyperLedger Fabric framework that able to handle data storage and secure intra-and inter-organizational communication. The key reason for which we have chosen HyperLedger Fabric framework is that, it uses RAFT consensus algorithm that provides permissioned blockchain network by creating a channel that was unavailable to the public blockchain network. We have made a smart contract for the permissioned blockchain by developing and writing the chaincode. We have used Fabric-CA server to generate crypto material. Then we have set up the network configuration, organizations, ordered peers, endorsing peers, anchor peers and developed client application for Hyperledger Fabric. We have integrated the web-front with fabric using WEB-API.
Is the Internet of Things (IoT) helping people and planet achieve Sustainable Development Goals?

Technology and sustainable development are two intertwined phenomena that can positively transform environment, society, businesses, and the planet as a whole. The Internet of Things (IoT) has been introduced as the next wave of innovation and can facilitate attaining more gains with limited resources. The current emerging paradigm of Internet of Things (IoT) can be harnessed to build an ecosystem that is more efficient, effective and resilient by leveraging linked devices to address the major global concerns that this and future generations will confront. This paper attempts to analyze and document the implied benefits of the application of the Internet of Things (IoT) that would help achieve the Sustainable Development Goals by the end of the current decade.
Enhancing the Management of Supply Chains Using Blockchain Technology

Blockchain innovation offers significant open doors for the management of supply chains. This paper targets exploring and methodically planning research on blockchain possibilities in further developing production network execution. Reports were recovered from many significant information sources, chosen, assessed, assembled into different topics, and incorporated. Blockchain can give an extremely durable, shareable, reviewable record of items through their inventory network, which further develops item tracking, legitimacy, and lawfulness in a more financially efficient way. In this section, the potential enhancement assumptions through blockchain innovation for the instance of agribusiness were also examined. Blockchain has not yet been extensively applied in the business of supply chains. This paper adds to the writing by offering an extensive guide of examination on blockchain possibilities in further developing inventory network execution. The discoveries of this study will be useful for executives who look for a thorough comprehension of how blockchain influences their organizations, especially in inventory network management.
Discovering Informative Data from Catastrophe Tweets Using Sentiment Analysis

Most of the social media platforms especially Twitter aims at connecting the users and helps in communicating their thoughts and feelings. During times of crisis, whether they involve immediate calamities or longer-term occurrences like floods and earthquakes, social media messages could be a vital origin of information. There is a probability that a tweet will contain both situational and non-situational information. Majority of previous efforts concentrated on recognizing tweets that contained a particular piece of information, such as situational, relevant, or actionable information. It can be difficult to distinguish between tweets that contain situational and non-situational information. Situational tweets include details that the concerned authorities may find useful, such as the count of impacted people in a certain area or contact information. Non-situational tweets can include sentimental information and personal viewpoints that may not be helpful to the authorities in question. Even after classifying the tweets, it is more important to give the information in short and efficient way. So that the tweets have to be summarized for quicker consumption without even losing a vital information. In data mining, clustering analysis is used to group similar data points together. In this paper, we are proposing Hierarsort framework that clusters and summarizes the data. The summarization is performed by pulling tweets out of different clusters.
Using Machine Learning to Predict Patient Health Outcomes

Predicting patient health outcomes accurately is a key challenge in modern healthcare, significantly impacting treatment planning, patient management, and healthcare resource allocation. In this study, we propose an innovative approach for predicting patient health outcomes using federated machine learning (FedML), a novel machine learning technique that enables collaborative learning from decentralized data sources while ensuring privacy. This research advances current methodologies by enabling data analysis across diverse health institutions without explicit data sharing. The goal is to develop a robust model capable of capturing a wide spectrum of health factors impacting patient outcomes, thus potentially leading to more accurate predictions. Our approach integrates data from multiple healthcare institutions to train a global predictive model, iterating between local model training and global model aggregation until convergence. We also employ a privacy-preserving mechanism to ensure the sensitive health data remains within the original institution, alleviating privacy and data security concerns inherent in traditional centralized learning methods. We assessed our federated learning strategy in contrast to well-established machine learning techniques such as deep learning, support vector machines, and random forests by using a range of patient datasets. Our technique has been shown to increase prediction performance, generalizability, and scalability while maintaining data privacy, as shown by the results. The study concludes by discussing the potential of federated learning in transforming predictive modeling in healthcare and addressing key challenges in implementing this approach. This research contributes to the body of knowledge in predictive health informatics and provides a roadmap for implementing federated machine learning in healthcare applications.
A Key-frame Extraction for Object Detection and Human Action Recognition in Soccer Game Videos

In professional team games, sports analysts frequently analyze to learn tactical and strategic insights into the actions of players in these team games. The foundation of current analytic procedures is the examination of team footage. We provide a visual analytical framework that seamlessly combines abstract visualizations with team sports video recordings. It offers an exciting opportunity because several complicated, real-time occurrences are examined towards making strategic decisions. Visual object detection is a well-known and active research area. Any object, its speed, and its appearance have their level of detection difficulty in the face of numerous obstacles. Human Action Recognition (HAR) is required to carry out advanced operations in team games as there is an increase in demand for video analysis of sporting events. To strategically improve the team’s performance, the team coach may, for instance, use an automatic monitoring system to monitor the player’s movement and locations throughout a soccer match as well as the location of the football. This paper proposes a YOLOv7 model that uses the key-frame selection technique to analyze players’ actions during a soccer game. In addition to detecting the football, player, and referee, the deep learning model can recognize six of the human actions in the soccer game. The experimental results show that using the key-frame selection technique for human action recognition, the total execution time can be reduced by approximately 68% to 70%.
Comparative analysis of Face Mask Detection models using deep learning techniques

The spread of and the casualties due to covid-19 virus can be kept under control with human efforts and self-discipline such as sanitization and wearing masks. However, the suggested protocols could not be followed all the time due to various reasons, such as negligence or taking the protocols lightly. Hence, in this paper, a face mask classification (FMC) and detection model are being proposed that can easily differentiate between humans wearing masks and with no mask on their face. We address this problem with object detection approach using deep learning techniques. Two main classifiers used in the paper are MobileNetV2 [1] and ResNet V2 152 [2]. The detection step has been performed with Haar-cascade, Multi-Task Cascaded Convolutional Neural Network (MTCNN) and Single shot detector (SSD) [3]. The hybrid model has been compared based on accuracy, frames per second (FPS), and F1-score. MobileNetV2 and Resnet V2 152 has detected human face masks at an accuracy of 98% and 96 % respectively based on the pre-trained data. The proposed method has a great potential for being implemented in hardware as an application and establish covid-19 safety guidelines in public places to prevent/reduce the spread of the virus.
Comparative Analysis of Attention-Based Sketch Recognition

Sketches transcend the boundaries of language and ethnicity to present a common way of expressing thoughts. Sketch recognition tasks have numerous real-world applications, such as in art, education, and engineering fields. Deep learning methods have shown great success in computer vision tasks, with research exploring its potential for sketch recognition through various unique approaches. This paper presents a comparative analysis of the current state-of-the-art attention-based approach to sketch recognition tasks, Transformer, also involving competing non-transformer methods. We discuss the advantages and limitations of these methods and compare their performance on the Quickdraw data set.
Breast Cancer Prediction Using Machine Learning Algorithms

Breast cancer develops when cells in your breast multiply and expand out of control, resulting in a lump of tissue known as a tumor. Feeling a lump, observing a change in breast size, or observing changes to the skin surrounding your breasts are all examples of breast cancer symptoms. Early detection may be aided by mammograms. The goal of this project is to determine the likelihood that the characteristics of a breast mass computed from digital photographs may accurately indicate the type of breast cancer (malignant or benign). The cases offered are cases where a tumor of some kind has been diagnosed, although only a small percentage of them (about 37% of them) are malignant. Based on the characteristics gathered from the breast mass, this study will examine the data provided and attempt to predict whether a diagnosis of breast cancer is malignant or benign.
Autonomous wound bite point generation using UNet

In this paper, a deep learning based autonomous wound bite point generation has been proposed. The proposed system consists of two setups Computer Vision (CV) followed by Robotic system. The CV setup consists of a series of steps that involves generating the binary mask of the wound using a U-Net model, which is further processed by the bite point generation algorithm to generate entry and exit points of the suturing needle in 2D space, this is called as CV pipeline. The robotic setup would utilize the results from the previous pipeline to perform the task of maneuvering the end-effectors to the desired location in 3D space. The proposed system’s output has been tested in the virtual environment of the robotic setup. The proposed U-Net model achieved an accuracy of 97% and IOU score of 70%, the entire system was integrated on a user-friendly interface for the ease of the use.
The Impact of CyRIS in Ascertaining Accurate Implementation of Projects

This systematic review seeks to evaluate the impact of CyRIS in ascertaining accurate implementation of projects with a primary focus on the merits of its functionalities. Core to the study is the view that CyRIS-driven project implementation reduces the risks of inaccuracy to an acceptable level. Although CyRIS is an extensively researched area in cyber security, a comprehensive review of subcategories, specifically, the accurate implementation of projects using the tool remains understudied. The proposed systematic review of literature provides insights and future directions from selected databases, including ScienceDirect, Springer Journals, SAGE Journals, and Wiley Online Library. Moreover, the limitations and strengths of each article collected are assessed using the PRISMA model to identify risks of bias. The research, which consists of journal articles, conferences, and symposium papers, is analyzed using "Cyber-Range Instantiation Systems and accurate implementation of projects" as the key phrase and "Cyber-Risk Models", "Cyber Range Instantiation", and "Cyber Security Training", and "Accurate Implementation of Projects" as the keywords. The findings of the study will bridge the awareness gap in using CyRIS-led Cyber-Risk Models to execute accurate training scenarios, improve weaknesses of implementation, simulate real-time attack scenarios, and highlight existing gaps as well as prospects for future research.
SWAYAM MOOC Reviews: Assessing Acceptability through Sentiment Analysis using Machine Learning

Massive Open Online Courses (MOOCs) have been granted a place in pedagogy as a result of the paradigm change occurring in higher education. The user base of the Indian MOOC platform SWAYAM has grown to over 18 million in only a few years, making it third only to Coursera and edX in terms of total users. This study gathered 4029 reviews from participants in 247 SWAYAM MOOCs and applied sentiment analysis with machine learning approaches to measure student acceptability of MOOCs on the SWAYAM platform. The Orange tool and the rule-based sentiment analysis method VADER were used to analyze the user reviews. Box plots of compound scores and chi-square tests of independence were calculated to identify patterns and trends in user sentiment toward MOOCs. The study found that overall user sentiment towards SWAYAM MOOCs was positive, with users expressing appreciation for the course content and delivery quality. Additionally, the study suggests that sentiment analysis may be a helpful tool for determining the quality and acceptance of MOOCs, and it can also be used to develop courses, advise policy, and assist in decision-making. The research also emphasizes the use of machine learning approaches in sentiment analysis.