Reviewerly/exHarmony huggingface dataset:
An Effective Cartoonifying of an Image using Machine Learning

Cartoonifying an image is the process of transforming a regular photograph into a cartoon-style image. This research paper proposes a method to cartoonify images using OpenCV, a popular open-source computer vision library using Python. The proposed method involves several steps, including edge detection, color quantization, and image smoothing. The edge detection step is used to extract edges from the input image. Then, in the color quantization step, the image palette is reduced to a fixed number of colors using the k-means clustering algorithm. Finally, the image is smoothed using a bilateral filter to create a cartoon-like effect. The proposed method is evaluated on several images, and the results show that the proposed method produces high-quality cartoon images with reduced noise and better visual appeal compared to existing methods. This method has potential applications in various fields, such as entertainment, advertising, and digital art, and can be easily integrated into existing applications.
SIFT – File Fragment Classification Without Metadata

A vital issue of file carving in digital forensics is type classification of file fragments when the filesystem metadata is missing. Over the past decades, there have been several efforts for developing methods to classify file fragments. In this research, a novel sifting approach, named SIFT (Sifting File Types), is proposed. SIFT outperforms the other state-of-the-art techniques by at least 8%. (1) One of the significant differences between SIFT and others is that SIFT uses a single byte as a separate feature, i.e., a total of 256 (0×00 – 0×FF) features. We also call this a lossless feature (information) extraction, i.e., there is no loss of information. (2) The other significant difference is the technique used to estimate inter-Classes and intra-Classes information gain of a feature. Unlike others, SIFT adapts TF-IDF for this purpose, and computes and assigns weight to each byte (feature) in a fragment (sample). With these significant differences and approaches, SIFT produces promising (better) results compared to other works.
OKAZ: A Deep-learning-Based System for Automatic Arabic Poem Generation

A poem is a sequence of verses that conveys emotions and views via an in-depth knowledge of a language. It represents a deeply rooted element of any language's cultural heritage. For decades, poem generation has been limited to human experts with considerable talent and linguistic skills. However, researchers have been attracted to this area lately, investigating the possibilities of auto-generating poems using AI-based systems. They observe considerable success with highly-inflected languages like Chinese. Nevertheless, research on the auto-generation of Arabic poems is sparse. This work presents one of the first attempts to address this research problem for Arabic poetry by exploring the effectiveness of using deep learning-based models to generate Arabic verses. Our findings show that GPT-2 was able to achieve the best results by outperforming other models and attaining a BLEU score of 0.082 and an average performance of 2.94 out of 5 when evaluated by human experts. We believe that these findings reveal the need for further investigations to bridge the performance gap when compared to the average human-level performance score of 4.62.
A Comparative Study on Facial Detection for Criminal Probe

The potential of crime and violation is intended to be controlled. Computationally, accurate crime predictions and future trend analysis can help to improve metropolis safety. We are all aware that a person's face is an exceptional and significant component of their physical nature. As a result, we can use it to track down a criminal's identification. As a result of technological advancements, CCTV is now installed in many public locations to record illegal activity. The criminal face recognition system can be used with the previously photographed faces and criminals' photos that are on hand in the police station. The primary objective of this study is to determine how law enforcement authorities may use a combination of ML and computer vision to detect, prevent, and solve crimes considerably more rapidly and correctly. The precise estimation of the crime rate, types, and hotspots using historical trends presents numerous computational opportunities and challenges. Despite significant research efforts, a better predictive algorithm is still required to lead police patrols in the direction of criminal activity. The linear regression, support vector machine (SVM), Naive Bayes, k-nearest neighbours (KNN), decision tree, random forest, K-means clustering and neural networks are some of the machine learning techniques used in this system. Our proposed system will detect crime as well as the criminal hotspots on which crime is likely to take place using hidden patterns from criminal datasets.
Intelligent Course Scheduling System for College Nursing Teaching Based on Ant Colony Optimization Algorithms

Curriculum planning and scheduling in nursing teaching in universities is an important and complex issue, especially in the context of an aging population, where the demand for nursing courses is increasing. This paper proposes an intelligent course scheduling system for college nursing teaching based on Ant colony optimization algorithms. Firstly, the system has constructed a curriculum layout design diagram based on the curriculum settings of nursing majors and students' course selection. Then, the Ant colony optimization algorithms is used to optimize the arrangement of courses and find the optimal course scheduling scheme. Finally, the effectiveness of this system was demonstrated through practical case testing. The research results show that the intelligent course scheduling system for college nursing teaching based on Ant colony optimization algorithms has good applicability and effect, can improve the utilization rate of course resources and teaching effect, and has great significance in promoting the training of nursing professionals. Compared with the traditional course scheduling method, the course scheduling system based on Ant colony optimization algorithms has the following advantages: First, the course scheduling effect is better, and can consider multiple factors and constraints to obtain more scientific and reasonable results; 2, Faster course scheduling speed, able to automate course scheduling calculations, and improve work efficiency; 3′ Stronger scalability, able to adjust and optimize the system according to various actual situations, and adapt to different teaching needs. The intelligent course scheduling system for nursing teaching in colleges and universities based on Ant colony optimization algorithms has high practical value and development prospects, and is of great significance for improving the level of nursing talent training in colleges and universities.
Real-time Event Streaming for Financial Enterprise System with Kafka

Real-time event streaming refers to the continuous and immediate processing and analysis of events as they occur in real time. Presently, Apache Kafka is a very popular and preferred framework for real-time data streaming. Data stream pipelines to serve enormous data for large organizations are built between systems in Kafka for real-time streaming. Kafka provides a distributed, scalable, and fault-tolerant platform for capturing, storing, and processing streams of events. It provisions seamless integration with different systems and applications, enabling real-time data analysis and processing. Thus, event streaming with Kafka is a modern approach to processing and analyzing large volumes of data in real-time. Event streaming with Kafka has a wide range of applications in varied industries such as finance, e-commerce, and more. It has the potential to transform the way businesses operate by enabling faster decision-making, improved operational efficiency, and better customer experiences. With financial enterprise application as our main goal, we focus on how Kafka could be a better-suited alternative to enhance event streaming.
Improved Parallel k-means Clustering Algorithm

The K-means algorithm, one of the most well-known clustering techniques, has been widely employed to solve a variety of problems. In contrast, the k-means clustering algorithm has numerous restrictions. For instance, the difficulty of dealing with voluminous data, the sensitivity of the outlier, and the random selection of the initial centroid. In this paper, a parallel K-means clustering algorithm is proposed that improves the performance of sequential K-means clustering algorithms by removing outliers from the data before clustering, dividing the data into smaller sections among the threads, and selecting the initial centroid with care. Our primary parallelization tool was OpenMP, which was implemented using the C programming language on 234,296 records. This experiment was conducted using sequential and parallel source code, with modifications made to enhance the parallel functionality. The improved parallel execution resulted in a significant reduction in execution time relative to sequential algorithms. The proposed algorithm source code is also available on GitHub for the community.
Asymmetric Byzantine Quorum Approach to Resolve Trust Issues in Decentralized Blockchain Oracles

In Blockchain technology, distributed computing is based on trust assumption among independent nodes. It cannot connect with the extrinsic world without third-party or interface called Blockchain Oracle. Blockchain Oracle is important as it provides external data to a blockchain network, enabling smart contracts to execute accuracy in real-time. This inability to verify the accuracy of the information gathered by Oracles is known as the Oracle problem. However, blockchain Oracle authenticity needs a transparent and reliable input stream for their execution as the efficiency of Blockchain Oracle directly affects the scalability of Blockchain. To address the aforesaid issues an approach based on Byzantine fault tolerance is proposed. Moreover, a spontaneous system for audit and accountability is also proposed based on heuristic-based detection. The efficiency and accuracy of the proposed approach are compared with the existing solutions using two real datasets to fulfill Blockchain Oracle authenticity requirements.
Dynamic Images Comparison using Siamese Neural Network

Dynamic images represent images that have the same context but different content. This means that they show the same object, but in one of the images, the object is rotated, moved, shrunken, enlarged or there is something in front of the object that partially covers it. It is often necessary to compare such dynamic images, for example, when testing the correct operation of dynamic graphic user interfaces (GUI). Since the content of the two dynamic images is not the same, standard computer methods of comparing the two images cannot be applied for reliable automatic testing of the equality of these images. Therefore, for their comparison, it is necessary to apply different methods, such as those based on comparing image features. Since machine learning and neural networks (NNs) have been used more and more recently in tasks related to image processing, as part of this paper, we wanted to examine the possibility of using NNs to compare dynamic images. Therefore, it was necessary to provide adequate datasets of dynamic images of sufficient size for the training of the NN model, for which a dynamic image generator was created within this research, which made this possible. The Siamese neural network (SNN) was then trained using new datasets for the dynamic image comparison tasks. Finally, it was tested on a freely available CGIAD dynamic image dataset on which it achieves a precision of 94.45%, recall of 92.94%, and accuracy of 93.74%. Results show that SNNs could be a good choice for dynamic image comparison tasks, but should be additionally improved to overcome the best state-of-the-art existing solutions based on a feature comparison.
Skills 360: Machine Learning-Driven Job Recommendation and Immutable Document Verification Through Blockchain Technology

Validating an applicant's credentials and expertise prior to hiring is one of the most elementary yet crucial responsibilities for every company. However, it is extremely time-consuming owing to the soaring number of applicants, especially in larger firms. Therefore, it is overlooked many times. The hiring department is increasingly concerned about the applicants providing incorrect information, and while traditional talent verification tools are helpful, they are still far from ideal for handling it. Although expensive, this activity is crucial, thus it needs to be made more efficient and cost-effective. Blockchain is a new technology that, due to its decentralised and distributed ledger system, ensures security and trust. Although this is a high-cost activity it is very significant, and hence, needs to be made more efficient and economical. Blockchain is an emerging technology that guarantees security and trust due to its decentralised and distributed ledger mechanism. Therefore, a blockchain based system for the verification of candidates credentials is a promising solution. Furthermore, from an applicant's point of view, it can become overwhelming to pick a job role in the technology industry considering the wide array of career opportunities and roles that are available today. Hence, a sophisticated machine learning algorithm can be used to recommend job titles to student applicants to help ease their decision-making process and provide them with necessary input concerning any career decisions that they would want to make in the future.
A Strategy to Improvise Coin-age Selection in the Proof of Stake Consensus Algorithm

Proof of stake is one of the consensus protocols in Blockchain technology. Within PoS, there are two common strategies employed for validator selection: Coin-age selection and Randomized block selection. However, both of these strategies have drawbacks when it comes to ensuring a fair selection of validators, leading to a phenomenon known as the "rich getting richer" syndrome. The paper proposes a new consensus algorithm that introduces a novel parameter: The time stamp of staking tokens. Staking tokens are those minted by validators to participate in the staking process. By considering the timestamp of staking tokens minted before participating in the staking procedure, the proposed algorithm aims to address the shortcomings of the existing strategies. The algorithm seeks to eliminate the loopholes in the coin-age selection method and enhances it in terms of precision. Additionally, it aims to increase the randomness in choosing a validator, thereby mitigating the rich-getting-richer syndrome.
A Comparative Analysis of Federated Learning Towards Big data IoT with Future Perspectives

Over the past few years, big data has come a long way, allowing us to make sense of the immense amounts of information being produced by cutting-edge services and apps, as well as a plethora of IoT devices. Only through extracting value from huge data through data analytics can big data's full potential be accomplished [6]. Machine learning (ML) plays a highly essential part in this process because of its capacity to know patterns and then deliver insights through learning from facts. Since the data may include private information about individuals, governments, or financial accounts, using a more traditional technique raises serious concerns about data confidentiality. Recently, Federated learning (FL) seemed like a potential learning procedure that could help us get beyond this obstacle. In FL, the central ML algorithm is only given the parameters from the local devices so that it can train and make predictions on a global scale. The aforementioned problems with huge data may be amenable to resolution thanks to this aspect of FL. Furthermore, a thorough assessment of FL for big data applications has forward done, leaving a void in the literature. This article delivers a summary of FL, big data, and the motives for the procedure of FL for big data by surveying its use in big data applications. In particular, it is important to thoroughly examine the usage of FL for major big data services with applications like the smart city, remote surgery, transportation, smart grid, and social networks. Additionally, briefly discuss the most pressing problems associated with this engaging subject and the most potential approaches to fixing them.
Breaking the Silence: Empowering the Deaf, Mute &amp; Blind Community through Sign Language Live Captioning Using Deep Learning

Gesture recognition is a rapidly advancing field in human-computer interaction, driven by the progress in artificial intelligence. This study focused on developing an efficient method for real-time translation of static and dynamic gestures in American Sign Language (ASL), British Sign Language (BSL), French Sign Language (FSL), and Indian Sign Language (ISL). Hand features were captured using Google's Mediapipe, while a custom dataset was created for experimentation. Long Short Term Memory (LSTM) was employed to recognize hand gestures, encompassing 70+ classes of signs and gestures. By training the LSTM model with spatiotemporal features extracted from the dataset, the resulting machine learning model facilitated real-time gesture prediction, live captioning, Text-to-Speech audio generation in multiple languages as well as text/speech conversion into ASL. This system has profound applications, benefiting individuals who are deaf, mute, or have difficulties in reading. With the proposed method tested on an 80% training and 20% test split, encouraging results with an accuracy of 96.77% were obtained for sign language gesture recognition.
Video Generation by Summarizing the Generated Transcript

Generative AI is a subset of Artificial Intelligence that focuses on generating new and unique data based on training data, such as images, music, text, or even 3D models. A large number of cameras continuously record the video, creating a massive amount of data. It takes a lot of time, labor, hardware storage, and other resources to process these massive amounts of video. This process has been revolutionized in recent years through the development of generative models. In this sense, video summarization is significant. In this study, we used two different kinds of approach Speech transcript-based approach and the action-based approach for summarization and creation. This paper's primary objective is to obtain the user's video URL to offer them a summary and a shortened version of the original video. Additionally, if there is only music or no transcript in the video, this will tell you which actions are performed in the video and provide you with a shortened version of that video, according to the activity and actions in the video. Image generation is the process of creating new images that do not exist in the real-world using computer algorithms. With Image generation option, users can create new and unique images that do not exist in the real world.
Neural Networks to Predict Software Development/Maintenance Performance and Required Time

This research focuses on the application of multilayer perceptron Neural Networks (NNs) with Bayesian Regularization (BR) to predict task difficulty in software development/maintenance. The task difficulty is reflected through solution complexity and required time for resolving a given task. Our aim is to support traditional, human-based estimations by providing additional insights into the difficulty of current tasks. Previous research explores several NN types, including Bayesian, fuzzy and convolutional NNs, with prediction accuracy rates below 80%. Clearly, inaccurate predictions can lead to financial losses and may damage client relationships. We analyze data related to developing software features and resolving bugs/incidents obtained from JIRA Service Management. Our results advocate the use of multi-layer perceptron NNs with BR over Stochastic Gradient Descent (SDG), Levenberg-Marquardt and Resilient Backpropagation (RPROP) leading to an accuracy rate of about 70%. Further tuning of the model parameters such as hidden layers, learning rate, and regularization could improve the accuracy even further. Of course, the quality and precision of the data used to train a model have a great impact on its performance.
QoE-Driven Coding Bitrate Determination for Adaptive Video Streaming to Home Viewers

This paper discusses the problem of selecting the best codecs and coding bitrates during the preparation of the content for the video streaming service. Most of the literature discussing relations between compression rates and quality of the video is based on objective evaluation or subjective assessment performed under strictly controlled laboratory circumstances. This paper presents the results of the video quality assessment done by home viewers in their environment. The quality evaluation takes into account H.264 and H.265 codecs, coding bitrates from 300 to 6000 kbps and three spatial resolutions of the examined video, i.e. 480p, 720p and 1080p. The results indicate statistically valid differences in the quality of the video evaluated in the laboratory and at home. Viewers at home tend to give lower scores than in the laboratory environment. This suggests the need to modify coding bitrate ladders, by increasing the video encoding speed, in order to meet the quality requirements of home users.
Design and Implementation of a Fully Pipelined and Parameterizable Hardware Accelerator for BLAKE2 Cryptographic Hash Function in FPGA

Cryptographic hash functions play a pivotal role in ensuring the security and integrity of data in various applications such as online banking, secure communications, and digital signatures. The Blake2b hash function is a widely used cryptographic hash function known for its security and efficiency. However, its implementation can be computationally intensive, especially for large inputs. In this project, the design and implementation of a fully pipelined Blake2b accelerator on an FPGA using Vivado Design Suite is presented. The primary goal of this work is to create a highly effective hardware accelerator to enhance the performance of the Blake2b hash function. A fully pipe lined accelerator that can process multiple input blocks simultaneously, reducing the overall latency of the implementation has been developed. The design was implemented on FPGA using Vivado Design Suite, which is a powerful FPGA design and verification tool. The fully pipelined design achieved a significant reduction in delay compared to the software implementation, demonstrating the effectiveness of the pipeline architecture. In conclusion, this project demonstrates the importance of performance improvement of cryptographic hash functions such as the Blake2b hash function. The proposed fully pipelined accelerator improves the performance of the Blake2b hash function while keeping the level of security high. The pipeline architecture offers a straightforward and effective way to accelerate the computation of cryptographic hash functions on an FPGA.
A Framework Proposal for Early Cyber Threat Identification and Profiling

Recently, there has been an increasing reliance on the Internet for business, government, and social interactions as a result of a trend of hyper-connectivity and hyper-mobility. While the Internet has become an indispensable infrastructure for businesses, governments, and societies, there is also an increased risk of cyber attacks with different motivations and intentions. In this work, we present a framework for automatic identification and profiling of emerging threats using Twitter messages as a source of events and MITRE ATT&CK as a source of knowledge for threat characterization. The framework comprises three main parts: identification of cyber threats and their names; profiling the identified threat in terms of its intentions or goals by employing two machine learning layers to filter and classify tweets; and alarm generation based on the threat’s risk.