Reviewerly/exHarmony huggingface dataset:
Time Series Prediction using Convolutional Neural Networks

Pseudo-Random number generators (PRNG) are fundamental in cryptography. The quality of generated pseudo-random numbers (PRN) often determines the "cryptographic strength" of a system. Historically, there have been numerous attempts to develop PRNGs that reduce the predictability and correlation of each successive value produced. This paper introduces a novel approach to predicting sequences of specific PRNGs by leveraging deep convolutional networks (CNN) in a regression-based supervised learning process. An experimental framework is provided, and by building upon existing prediction models, it is demonstrated that the cryptographic strength of subsequent PRNGs increases. Simultaneously, prediction success rates, even with advanced learning, remain relatively ineffective. This underscores the growing importance of robust PRNGs across various industries.
Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian

In this paper, we focus on generating a synthetic QA dataset using an adapted Translate-Align-Retrieve method. We created the largest Serbian QA dataset, which we name SQuAD-sr. To acknowledge the script duality in Serbian, we generated both Cyrillic and Latin versions of the dataset. We investigate the dataset quality and use it to fine-tune several pre-trained models. Best results were obtained by fine-tuning the BERTić model on Latin SQuAD-sr dataset, achieving 73.91% Exact Match and 82.97% F1 Score on the benchmark XQuAD dataset, which we translated into Serbian for the purpose of evaluation. The results show that our model exceeds zero-shot baselines, but fails to go beyond human performance. We also note the advantage of using a monolingual pre-trained model over multilingual, as well as the performance increase gained by using Latin over Cyrillic. Finally, we conclude that SQuAD-sr is of sufficient quality for fine-tuning a Serbian QA model, and can be used in absence of a manually crafted dataset.
Enhancing Training Efficiency in Facial Emotion Recognition

This paper explores the utilization of image filters to accelerate image processing tasks. By applying filters, the goal is to speed up image processes while maintaining model accuracy. The paper investigates various filter techniques, considering their effects on processing speed. Experimental results demonstrate the effect of these filters on image processing time, diminishing the training time by a significant one-third. Particularly relevant for complex models, this methodology offers a mechanism for lower training times.
A Novel LSTM based Approach for Crypto Currency Price Prediction

The nature of cryptocurrencies is decentralized and they have potential of large returns, due to this nature of crypto currencies, they have increased in approval in form of investment. Due to the erratic and volatile nature of the crypto currency market, it can be difficult to predict their pricing. As a result, reliable price forecasts are essential for investors to make wise investment choices. The proposed LSTM based approach will create machine learning models using open-source libraries like pandas, NumPy, and Scikit-learn. Cross-validation will be utilized to test the working of different models, and the one gives best output will be taken as the final model. The aim of this research is to create a machine learning algorithm that can forecast bitcoin values. Predicting the future price swings of crypto currencies like Bitcoin and Ethereum has become a crucial research subject as their use and popularity have increased. By using regression and deep learning algorithms, the work seeks to use different number of machine learning models to analyze historical bitcoin data and forecast future prices. The end result of this work will be a very effective and accurate model with R2 score of 0.96 for train data and 0.97 test data for forecasting crypto currency values, which traders, investors, and researchers may use to decide wisely on investing in crypto currencies.
MEWA: A Benchmark For Meta-Learning in Collaborative Working Agents

Meta-reinforcement learning aims to overcome important limitations in reinforcement learning, like low sample efficiency and poor generalization, by creating agents that adapt to new tasks. The development of intelligent robots would benefit from such agents. Long-standing issues like data collection and generalization to real-world dynamic environments could be mitigated by sample-efficient adaptable algorithms. However, most such algorithms have only been proven to work in low-complexity environments. These provide no guarantee that a near-optimal global policy does not exist, which makes it difficult to evaluate adaptable policies. This hinders the in-depth analysis of an agent's potential to adapt, while also introducing a gap between controlled experiments and real-world applications. We propose MEWA, a collection of task distributions used as a benchmark for adaptable agents. Our tasks contain a shared structure that an agent can leverage to learn the task-specific structure of new tasks. To ensure our environment is adaptive, we select some of the task parameters using the solution to a constrained optimization problem. Other parameters are randomized, allowing the creation of arbitrary task distributions. We evaluate three state-of-the-art meta-reinforcement learning algorithms on our benchmark, that were previously shown to adapt to new tasks with a simpler structure. Results show that the algorithms can reach meaningful performance on the task, but cannot yet fully adapt to the task-specific structure. We believe this benchmark will help identify some of the issues that hinder adaptability, ultimately aiding in the design of new algorithms, more suitable for real-world human-robot applications.
Encouraging Success: The Reward-Driven Approach to Object Detection

To date, YOLO algorithms have achieved great results in general-purpose object detection tasks. However, tasks such as screen time estimation where object from a given class can appear at most once per image can cause problems for this network and produce inadequate results. To solve this problem and improve the performance of YOLO networks on screen time estimation tasks, this study proposes an improved version of loss function and a new type of filtering on YOLOv5 framework. This new type of filtering ensures that each class has at most one object present in each image. Improved loss function focuses on the fact that YOLO networks tend to treat many decent predictions as false positives. We mitigate this problem by either ignoring these predictions when calculating loss or even rewarding the network for finding them. Experimental results show a 2.5% increase in precision when detecting characters in scenes from “Dr House” and a 3.4% increase of recall when detecting letters in American sign language.
Hyper-V as type-2 hypervisor virtualization: guest file system performance examination

The main objective of this paper is to examine the performance of different guest file systems for hypervisor-based virtualization with the MS Hyper-V as the type-2 hypervisor. For this research, we employ the mathematic modeling of file system environments and the measurement the file system performance, in fair-play conditions applying the same environment and benchmark method. For testing purposes, we used Filebench benchmark, which provides the equality and independence between hardware and operating system characteristics. CentOS 7 is used as the guest operating system, while MS-Hyper-V under Win10 is used as the type-2 hypervisor. Performances are compared for the cases of one, two and three virtual machines running simultaneously. Using the mathematical model, we discuss the expected behaviors, then we make the experiment with Filebench, while the results are interpreted though the model as validation of the assumption about expected behaviors.
Sentiment Analysis of IMDB Movies Dataset Using Deep Learning Based GRU Model

Sentiment Analysis is the need of the hour for every big or small companies, be it manufacturing or entertainment industries, educational institutions, media houses, political parties or even individual. All organizations want to identify churn and to get their customers retained with them by mean of sentiment analysis on social media. Increasing number of Internet and social media users contribute in petabytes of unstructured data generated every day. The continuous advancement in technology sector, in terms of high storage capability, computational processing power of the computers, cloud technology etc., has enabled processing of these high-volume unstructured data with ease, in a very less time, which otherwise would have taken even days to process. Many sentiment analysis systems are modelled by using deep learning, by using GRU (gate recurrent unit) architecture, has showed significant improvements with high tendency to reveal the underlying semantic meaning in the input text. GRU model seems to perform even better with lowest loss 0.0783, variable loss 0.4728, highest accuracy 97.20% and variable accuracy 88.19%.
Exploring Heterogeneous Open Multi-Agent Systems on Cloud Using a Docker-Based Architecture*

In Open Multi-Agent Systems (OMAS), heterogeneous agents in varying environments or models can transition from one system to another, retaining their attributes and knowledge. This migration process results in an augmented development complexity compared to conventional Multi-Agent Systems. Additionally, the intricacy of this transition arises from uncertainties and dynamic behaviors associated with the agents' changes, necessitating the formulation of techniques to analyze this complexity and comprehend the system's overall behavior. To address these challenges, we employed Docker, which enables a flexible architecture that accommodates different programming languages and frameworks for the agents. This paper introduces a Docker-based architecture that aids OMAS development, facilitating agent migration between various models operating in heterogeneous hardware and software setups. To validate the proposed approach, we conducted simulations using NetLogo's Open Sugarscape 2 Constant Growback and JaCaMo's Gold Miners. These simulations were executed locally, in the cloud, and in a hybrid mode to assess the feasibility of the proposed architecture.
Augmented Reality in Interactive Multiplayer Game Application

Virtual Reality (VR) replaces the real world with a fictional reality whereas Augmented Reality (AR) merges computer-generated visuals with the real environment for an interactive 3D experience. Its applications span research, data visualization, training, entertainment, arts, and medicine. Notably, AR's impact is prominent in gaming, a field that often pioneers technology adoption, such as GPUs. AR gaming seamlessly blends game elements with the user's surroundings in real time, distinct from VR gaming that demands a confined space. Unlike VR's dedicated headsets, AR games commonly run on mobile devices like smartphones and tablets, enjoying broader accessibility. This convergence of technology and gaming showcases AR's potential within an ever-expanding industry.
Design of Chatbot to Prevent Cyberbullying from Social Media

In the present world, social media has greatly influenced our lives but with the increase in usage of social media, people get exposed to its adverse impacts as well. The common and major impact is cyberbullying, which tends to have tremendous negative impacts on the victim's life. Hence, detecting cyberbullying has now become a crucial task. This research study proposes a novel chatbot design to identify the occurrences of cyberbullying based on user's input and provide emotive support or guidance as required. It also offers data on various aspects and domains of cyberbullying, thus helping to create a safer online environment.
Connectivity Schemas in NeuroEvolution: What Neural Architectures does GEPNN evolve?

In recent years, there has been a rise in popularity of using evolutionary algorithms (EA's) in conjunction with artificial neural networks (ANN's). This approach is commonly known as NeuroEvolution (NE). NeuroEvolutionary approaches typically optimize just the weights of an ANN or optimize the architecture, learning rates, thresholds, and weights together. Algorithms capable of the latter are known as Topological and Weight Evolving ANN (TWEANN). One such TWEANN is Gene Expression Programming for Neural Networks (GEPNN). This paper presents an empirical investigation of the network topologies that arise when GEPNN is used and whether evolved architectures have any relation to state of the art architectures. Results show that GEPNN naturally discovers powerful structural motifs such as shortcut connections and also creates sparse networks. Both these schemas have been shown to be advantageous in deep learning techniques. As an additional contribution from this work, we provide an open source library for developing GEPNN solutions in Python.
Deep Learning for Breast Cancer Prediction in the Era of Big Data: A Comparative Study of Gene Expression and DNA Methylation

In the era of big data, conventional machine learning algorithms struggle to handle the vast and unique characteristics of this data-rich landscape. This study is dedicated to the challenging task of breast cancer prediction, focusing on two critical data types: gene expression (GE) and DNA methylation (DM). Our primary goal is to leverage the power of Deep Learning algorithms, both individually for each dataset and in combination,. At the heart of breast cancer prediction, System employ the Convolutional Neural Network (CNN) algorithm, renowned for its prowess in pattern recognition and feature extraction. Our research involves a comprehensive analysis of GE, DM, and the merged GE and DM datasets. Also evaluate the performance of the CNN algorithm by comparing it with the widely-used Random Forest (RF) algorithm for classification tasks. Experiments underscore the effectiveness of our approach, particularly when using the GE dataset. This configuration yields the highest accuracy and is the most cost-effective among all classifiers considered. These findings highlight the remarkable potential of Deep Learning techniques in breast cancer prediction, especially in the context of big data. This research aims to develop a deep learning framework for identifying significant breast cancer biomarkers. This model excels in managing large volumes of data, even when dealing with missing values and class imbalances. The study emphasizes the advantages of feature reduction and class balance, resulting in improved predictive outcomes compared to imbalanced datasets. Furthermore, conduct Gene Set Enrichment Analysis on the gene sets identified by the model and assess its efficacy by comparing these gene sets with established cancer resources. The results suggest that deep learning methods can be extended to predict various cancer types.
Machine Learning Based Crime Identification System using Data Analytics

Poverty is known to be the mother of all crimes, and a vast percentage of people in India live below the poverty line. In India, the crime rate is rapidly rising. The police officers must spend a significant amount of time and personnel to identify suspects and criminals using current crime investigation. In this research, the method presented for designing and implementing crime identification and criminal recognition systems for Indian metropolitans is utilizing techniques of data mining. These occurrences are represented by 35 predefined crime attributes. Access to the crime database is protected by safeguards. The pending four subjects are important for crime unmasking, identification and estimation of criminals, and crime authentication, in that order. The detection of crime is investigated with the help of K-Means clustering, which iteratively builds two crime batches based on congruent criminal features. Google Maps is to enhance the k-means visualization. K-Nearest Neighbor classification is used to examine criminal identification and forecasting. This is used for the authentication of the results. The technique benefits society by helping investigative authorities in crime solving and criminal recognition, resulting in lower crime rates. This research study describes a way for creating and deploying crime solving and criminal recognition systems for Indian metro's using data mining tools in this study. The method consists of data evulsion, data pre- processing, clustering, Google map delegation and classification. The first module, data evulsion, retrieves unformed or unrecorded crime datasets from several criminal sources online from 2000 to 2012. In the second module, Data pre-processing cleans, assimilates, and reduces the obtained criminal data into organized 5,038 crime occurrences. Several predefined criminal traits represent these instances. Safeguards are in place to prevent unauthorized access to the crime index. The remaining components are critical for detecting crimes, criminal identity and prediction, and crime verification, in that sequence. The investigation of crimes is investigated using k-means clustering, which gives results repeatedly.
"Let’s Discuss it in a Team Meeting!" Collaboration Challenges of Distributed Software Design

Background. Global Software Engineering (GSE) poses several challenges during the software design process. While the existing literature provides practices and guidelines to improve architecture knowledge management in GSE-based software organizations, the challenges related to organizing collaboration during the software design process still need attention. Aim. This study aims to identify the collaboration challenges faced by distributed teams during the software design process and provide recommendations. Method. We conducted semi-structured interviews with eight professionals working in different GSE-based organizations. Results. The study revealed three challenges that underscore the negative impact of the distributed setting on team collaboration during the software design process. Conclusion. We provide recommendations and propose future research direction to enhance team collaboration in the software design process for GSE-based organizations.
Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.
Fast Configuration Planning Algorithm for Cost Optimization in Undersea Fiber Cable System

We propose and numerically study a fast configuration planning algorithm for cost optimization in undersea fiber cable system Compared with full traversal algorithm, it has been accelerated by 30 times.
Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation

The growing field of Content-Based Medical Image Retrieval (CBMIR) plays an integral role in the diagnosis and treatment plan of numerous diseases, including cancer. However, the effective representation of gigapixel medical images via a large number of extracted features remains a challenging task, crucially influencing other processes in a digital workflow. In this paper, we propose a novel QR code representation strategy to enhance retrieval performance. Unlike the traditional one-dimensional binary representation methods (i.e., barcodes), this 2D approach captures more intricate and informative patterns from the features by differentiating each pair of features resulting in a 2D binary vector. We delve into three distinct QR code generation strategies, namely, the Thresholding QR, the MinMax QR, and the Hybrid QR, each offering unique strengths. Our experiments on representing whole slide images of the Cancer Genome Atlas (TCGA) dataset reveal that while the Hybrid QR tends to provide balanced performance, there are instances where the other two methods outshine. Even though this approach requires more memory usage, the considerable enhancement in accuracy justifies this trade-off; obviously, in medical applications, accuracy holds the highest priority. Hence, the findings indicate that QR codes can effectively improve the performance of CBMIR systems by not only accelerating the retrieval process but also increasing the accuracy of image retrieval, leading to potentially more accurate diagnoses and treatment planning.
Meet C4SE: Your New Collaborator for Software Engineering Tasks

The software industry’s complexity and scale have increased rapidly, leading to challenges in managing information and tasks among developer teams, often resulting in inefficiencies, misunderstandings, and delays. The extensive search for automated tasks led to using chatbots—conversational agents—in software development. However, despite their positive contributions, their adoption has numerous issues, notably the lack of full working context, making their support sometimes useless. To address such a limitation, we propose C4SE, a chatbot designed to assist software engineers and managers in performing various tasks by gathering information helpful for better support. We use the GPT 3.5 model, and a specialized data store based on a vector database for long-term memory, to understand users’ intentions and maintain contextual information. Our prototype C4SE can perform code suggestions, reviews, GitHub API operations, and generate unit and acceptance test cases. Preliminary evaluation reports encouraging results, showing potential to increase productivity in the software development lifecycle.
Group Teaching Optimization Algorithm with Machine Learning Model for Big Data Analytics

Big Data An analytics (BDA) plays a significant role in the process of decision-making over several domains. Effective evaluation and processing of enormous data amounts need influential techniques and Machine Learning (ML) methods. BDA by implementing ML and Feature Selection (FS) is an influential model in extracting valued patterns and vision from huge and convolutional datasets. The process encompasses implementing modern ML methods and models for interpreting, processing, and analyzing data on a scale that cannot be handled effectively by conventional models. This research study proposes a design of Group Teaching Optimization Algorithm with Machine Learning model for Big Data Analytics (GTOA-MLBDA) technique. The GTOA-MLBDA technique classifies the big data and it can be managed by Map Reduce tool. The GTOA-MLBDA technique uses GTOA to determine the important features, reduce dimensionality and enhance classifier results. In addition, the classification of big data takes place using functional link neural network (FLNN) model. Finally, the parameter tuning of the FLNN model takes place using Adam optimizer. Experimental analysis has been carried out by using big data datasets and the results highlight that the combination of the GTOA with the FLNN classifier of the GTOA-MLBDA technique resulted in improved results over classical approaches.