Reviewerly/exHarmony huggingface dataset:
Enhancing Security of IoT Data Transmission in Social Network Applications

The development of ad hoc networks leads to enormous growth of information technology industries. With the current growing need for wireless connectivity, IoT systems use ad hoc solutions to communicate between devices and objects. The Internet of Things technology has gained immense popularity in recent years. Devices, things and sensors have acquired a virtual identity that allows them to connect and communicate with each other, society and a specific user of the system. Globally most of the networks are open networks. For social network, Internet of Things devices act as nodes, where the node transmission secured data won’t be assured. There is a highly possibility to the intruders to steal the information from the node. Based on this background, this paper proposed a data transmission model for social network with enhanced security and minimal energy consumption. The proposed method enables the secure data transmission after clearly analysing the nature of the node and behaviour of the malicious node. This will prevent the data communication with malicious node and enable reliable communications. The simulation results reveal that the proposed method significantly improves the secured communication in IoT social networks. In the proposed method, the performance is increased by 50–75% when compared to the previous method.
Enhanced Artificial Neural Network for Spoof News Detection with MLP Approach

A huge number of people put forth the Internet and various social media platforms. These platforms can be deployed for different purposes by these users. In this digital age, spoof news has spread to more people than ever before. There has been an explosion in spoof news detection due to social media and direct messaging platforms. As of now, the “Fake News Challenge” is a Kaggle competition and Facebook is using artificial intelligence technology to weed out fake news. Combating spoof news is an easy project that relies on text classification. The proposed method is manipulated with natural language processing (NLP) for text analysis, and stop words and stemming words techniques are utilized for preprocessing techniques with tokenization method. The study is also explored with enhanced artificial neural network (EANN) method, and term frequency and inverse document frequency (TF-IDF) is used for feature extraction from the article text. The performance of the proposed model is validated and compared with the support vector machine, logistic regression and random forest using the performance measures like precision, recall and accuracy parameters.
Chinese Character Writing Evaluation System Based on Image Processing

With the rapid development of the digital age, more and more people seldom write. In order to maintain the traditional culture and promote the correct learning and writing norms and aesthetics of students under 13 years old and other Chinese character beginners, a Chinese character writing evaluation system based on image processing is proposed. In order to improve the standardization of the evaluation of the system, the system uses the national standard regular script GB 2312 as the writing evaluation template, and integrates the system into the WeChat applet on the mobile phone to provide learners with the convenience of practicing words, and through the perspective transformation and threshold binarization. The image preprocessing is carried out, and finally the learners ‘ handwritten Chinese characters are evaluated from the aspects of structure and stroke, and suggestions for improvement are given. The statistical data of 1000 people's actual test shows that the recognition accuracy of handwritten Chinese characters is 99.8%, which can meet the practice needs of learners.
Effective Scheduling of Real-Time Task in Virtual Cloud Environment Using Adaptive Job Scoring Algorithm

This paper built up a figuring design and calculations for supporting delicate constant assignment planning for a distributed computing climate by dynamically distributing virtual machines. The design coordinated delicate continuous assignment planning calculations, in particular expert hub and virtual machine hub plans. What's more Adaptive Job scoring calculation is additionally applied. At the point when innovation advances and science, the issues experienced become more muddled and need seriously processing power. Rather than the conventional thought of utilizing supercomputers, lattice processing is proposed. Disseminated figuring upholds asset sharing. Equal processing upholds registering power. Matrix registering intends to saddle the force of both dispersed figuring and equal processing. The objective of framework figuring is to total inactive assets for the Internet like Central Processing Unit (CPU) cycles and extra rooms to encourage usage. Matrix innovation, which associates various PC bunches with fast organizations, can accomplish a similar registering power as a supercomputer does, likewise with a lower cost. Notwithstanding, lattice is a heterogeneous framework. Booking free undertakings on it is more muddled. To use the force of framework totally, we need an effective work booking calculation to allocate occupations to assets in a lattice. This undertaking proposes an Adaptive Scoring Job Scheduling calculation (ASJS) for the network climate. Contrasted with different strategies, it can diminish the finishing season of submitted occupations, which may make out of registering concentrated positions and information serious positions.
Summarization of Research Paper into a Presentation

With the increase in the use of the internet over the globe, the exchange and storage of information have seen an exponential surge as well. While writing and reading, people prefer shorter versions of content to save time and derive only significant insights. This has promoted the use of summarization in the past few decades. Since human-generated summaries are resource and time-intensive, automatic text summarization was developed. Inspired by these problems and by observing the issue of time management among our peers in the research community, we have tried to form a system that converts a research paper into a presentation. This system takes the research paper as the input document, summarizes the content section-wise and adds it to slides which are combined into a presentation. The techniques used to summarize are Text Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis, and Text Rank Algorithm. The model is tested on four different papers and evaluated on their ROUGE scores to compute their utility.
Proactive Public Healthcare Solution Based on Blockchain for COVID-19

Globally, the COVID-19 pandemic costed people dearly and may continue with persistent potential of further waves of new variants. A proactive solution would be more prudential than the currently employed reactive methods. This paper identifies the existing methods of containment and their drawbacks in controlling the pandemic. An understanding of proactive and reactive health care, the impact of COVID-19 and blockchain-based health applications are explored in this paper. A potential proactive approach addressing the identified drawbacks utilizing blockchain is proposed. This blockchain-based system, as a common platform for all healthcare stakeholders, enables coordinated efforts for a proactive pandemic control. The much-needed accountability, transparency and trust in public health care in this chaotic pandemic are feasible by the proposed blockchain-based approach.
Face Mask Detection Using Keras/Tensorflow

As recently, coronavirus is a very infectious and latest epidemic disease, that was discovered in late 2019. Looking into the situation, this project is based on the detection of proper wearing of a mask. It detects masked and non-masked using video stream by extracting each ROI of a face from the given dataset. Here python script is used for training the dataset using Keras/Tensorflow. This project goal is to train a custom deep learning whether the person dressed a proper mask or not. The proposed technique identify the facial image precisely and indicates whether it is masked or unmasked. A monitoring duty performer can also identify a masked face in motion. The technique achieves 99.99% accuracy with the combination of two various image data sets.
A Multi-Objective Task Scheduling Approach Using Improved Max–Min Algorithm in Cloud Computing

Cloud task scheduling is a multi-objective optimization problem (MOOP), and most MOOP failed to provide a balanced trade between makespan and cost when it comes to scheduling tasks. For cloud computing multi-objective task scheduling strategies, this research employs an optimization model based on the Improved Max–Min algorithm. The present Max–Min algorithm has a limitation in that it prioritizes large tasks over the smaller tasks on the resource. As a result, the makespan was increased, and the load was unevenly distributed across the resources because of selecting execution time (ET) of large tasks. The Max–Min method is improved by selecting the ET of an average task, which reduces the makespan, and costs and improves load balancing. The CloudSim tool is used to compare the Improved Max–Min algorithm to the existing Max–Min and Min–Min algorithms. The core difference between the space, time shared policy, the homogeneous and heterogeneous environment is that the spaced shared policy and homogeneous cloud environment has lower values for makespan and cost.
Analytical Investigation of Different Parameters of Image Steganography Techniques

Data is the most valuable asset in the current age of digital era. A person can lose billions of dollars if unauthorized users steal some data. Image steganography is the solution for these types of problems. It uses the image as the cover object for the security of data. Every steganography approach revolves around five key parameters. The key parameters are the key parameters of hiding capacity, perceptual transparency, robustness, tamper resistance and computational complexity. The blended approach using XOR operation, adapted approach using pixel mutation and parity checker method is used for analytical investigation of different parameters. The relationship between different parameters shows that if we increase or decrease one parameter, then it will affect another parameter. After analysis of some results, we have found that if hiding capacity is increased, then the imperceptibility of image steganography automatically decreases. This analytical investigation is helpful in developing new approaches in the field of steganography as we can trade off between different parameters according to the user’s requirement.
Women Safety Using IoT Device with Location and Video

In this modern era of fast developing technologies, the security of women in public places is still in debate due to continuous assault against women and sexually exploiting them whenever and wherever they are helpless. This kind of violation does not only happen to adult women, but also to children. In 2018, a child named AsifaBano who was an eight-year-old girl at that time was brutally raped and murdered in Kashmir ( https://www.bbc.com/news/world-asia-india-43722714 ). While the case of Nirbhaya in the year 2012 ( https://www.bbc.com/news/world-asia-india-51969961 ) is still shaking India, numerous new cases are being filed every year which makes every parent fear for their children’s lives every day. This research proposes a two module security systems particularly aimed for women and children. The first module is being a security device that can be worn by an individual, and if they ever run into a situation where their life is threatened, the device picks up their abnormal heart rate and temperature through sensors and immediately sends the location of the victim to their parents; alongside, a video starts to record everything that takes place in the crime scene. The second module includes data protection of the device by using Honey encryption algorithm.
Hardware design of a model generator based on grammars and cartesian genetic programming for blood glucose prediction

People with diabetes need to control their blood glucose levels to avoid dangerous situations such as getting into hypoglycemia or hyperglycemia, which can lead to long-term and short-term complications. One of the most important daily tasks of people with diabetes is to estimate or predict the glucose in a near future as a consequence of medication, eating, or insulin administration events. We present a parameterized hardware implementation of a blood glucose level predictor generator. The design was implemented over a Field Programmable Gate Array and uses as input variables a set of data from the person (blood glucose levels, carbohydrates, and insulin units). Our implementation produces personal devices the patient can use whenever new readings of the variable are available. Moreover, it could be combined with insulin pumps and continuous glucose monitoring systems to develop an artificial pancreas. For the model generation, we designed a novel technique based on grammars, cartesian genetic programming with an evolutionary strategy (1+λ) and a fitness function based on the Clarke Error Grid Analysis. Preliminary results show that our hardware implementation achieved higher speeds and lower power consumption than its software counterparts while preserving or even improving the accuracy of the predictions.
Optimizing Telemetry Signal Influence for Power Consumption Prediction

Automated Guided Vehicles (AGVs) are common elements of contemporary industry. Their continuous operation, and thus detection of anomalies in their operational cycles, is critical for uninterrupted production flow. Prediction of signals, such as momentary power consumption (MPC), is used in most anomaly detection methods. Feature engineering - selection or weighting - can significantly improve prediction quality. In this work, we use a genetic algorithm (GA) to optimize weights for features from AGV telemetry. A 2-layer Long Short-Term Memory (LSTM) network was used to predict MPC. Our primary goal was identifying the most effective weighting strategy for enhancing predictive accuracy. We examined different schemes of population initialization. The performance of each was compared to baseline models. Results show a significant improvement in prediction quality compared to the baseline. Our application of GA optimization in feature engineering contributes to the growing body of knowledge on developing more reliable AGV systems, which can lead to reduced operational costs and enhanced sustainability in various industrial settings.
Graph-Based Mutations for Music Generation

Our study aims to compare the effects of direct mutation and graph-based mutation on representations of music domain. We focus on short tunes from the Irish folk tradition, represented as integer sequences, and use a graph-based representation based on Pathway Assembly (a directed acyclic graph) and the Sequitur algorithm. We define multiple mutation operators to work directly on the sequences or on the graphs, hypothesizing that graph-based mutations will tend to preserve the pattern used per tune, while direct mutation of sequences will tend to destroy patterns, resulting in new generated tunes that are more complex. We perform experiments on a corpus of tunes and apply the mutation operators many times consecutively to analyze their effects.
Neuroevolution Trajectory Networks: revealing the past of incrementally neuroevolved CNNs

Analysing Neuroevoution algorithms often proves to be challenging from a fitness performance standpoint. We argue that our Neuroevolution Trajectory Networks (NTNs) visualisation technique, based on the use of complex networks, can effectively highlight the idiosyncratic differences and peculiarities of this family of evolutionary algorithms. This work deploys NTNs specifically to analyse the transfer of knowledge, from different benchmark classification datasets. Here, the knowledge transferred, is considered as the inheritance of evolutionary units representing the layers (and learning optimisers) forming the architecture of Convolutional Neural Networks (CNNs), incrementally developed and generated by Fast-Deep Evolutionary Network Structured Representation. Our approach highlights salient characteristics about this transfer learning paradigm, as well as exceptional findings, which help consolidate our understanding of Neuroevolution and Deep learning. This Hot-off-the-Press paper summarises the work awarded "Best Paper" entitled: "Under the Hood of Transfer Learning for Deep Neuroevolution" by Stefano Sarti, Nuno Lourenço, Jason Adair, Penousal Machado, Gabriela Ochoa; published in Applications of Evolutionary Computation. EvoApplications 2023. https://doi.org/10.1007/978-3-031-30229-9_41
Attention in Rule-Based Machine Learning: Exploiting Learning Classifier Systems' Generalization for Image Classification

Deep learning is a cutting-edge methodology that has been widely used in real-world applications to solve computer vision tasks. Deep learning models are typically seen as black boxes, opaque, and difficult to interpret. Recently, attention-based vision transformers have been introduced to overcome the black-box behavior of deep networks. However, the decision-making process of the vision transformer is still not interpretable. Moreover, these models require a large amount of memory, huge computational resources, and enormous training data.
Towards a General Boolean Function Benchmark Suite

Just over a decade ago, the first comprehensive review on the state of benchmarking in Genetic Programming (GP) analyzed the mismatch between the problems that are used to test the performance of GP systems and real-world problems. Since then, several benchmark suites in major GP problem domains have been proposed over time, which were able to fill some of the major gaps. In the framework of the first review about the state of benchmarking in GP, logic synthesis was classified as one of the major GP problem domains. However, a diverse and accessible benchmark suite for logic synthesis is still missing in the field of GP. In this work, we take a first step towards a benchmark suite for logic synthesis that covers different types of Boolean functions that are commonly used for the evaluation of GP systems. We also present baseline results that have been obtained by former work and in our evaluation experiments by using Cartesian Genetic Programming.
Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.
Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent.

Symbolic regression is a common problem in genetic programming (GP), but the syntactic search carried out by the standard GP algorithm often struggles to tune the learned expressions. On the other hand, gradient-based optimizers can efficiently tune parametric functions by exploring the search space locally. While there is a large amount of research on the combination of evolutionary algorithms and local search (LS) strategies, few of these studies deal with GP. To get the best from both worlds, we propose embedding learnable parameters in GP programs and combining the standard GP evolutionary approach with a gradient-based refinement of the individuals employing the Adam optimizer. We devise two different algorithms that differ in how these parameters are shared in the expression operators and report experimental results performed on a set of standard real-life application datasets. Our findings show that the proposed gradient-based LS approach can be effectively combined with GP to outperform the original algorithm.
MEG: Multi-objective Ensemble Generation for Software Defect Prediction (HOP GECCO'23)

This Hot-off-the-Press abstract aims at disseminating our recent work titled "MEG: Multi-objective Ensemble Generation for Software Defect Prediction" published in the proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) [4]. We believe this work is of interest for the GECCO community as it proposes a novel way to automatically generate ensemble machine learning models leveraging the power of evolutionary computation: MEG introduces the concept of whole-ensemble generation as opposed to the well known Pareto-ensemble generation. While we evaluate the effectiveness of MEG for Software Defect Prediction in our work, MEG can be applied to any classification or regression problem and we invite both researchers and practitioners to further explore its effectiveness for other application domains. To this end, we have made MEG's source code publicly available.
High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%.
Modern Applications of Evolutionary Rule-based Machine Learning

tutorial Free Access Share on Modern Applications of Evolutionary Rule-based Machine Learning Authors: Abubakar Siddique Wellington Institute of Technology, Te Pūkenga, Wellington, New Zealand Wellington Institute of Technology, Te Pūkenga, Wellington, New Zealand https://orcid.org/0000-0002-3253-802XSearch about this author , Will Browne Queensland University of Technology, Brisbane, Australia Queensland University of Technology, Brisbane, Australia https://orcid.org/0000-0001-8979-2224Search about this author , Ryan Urbanowicz Cedars Sinai Medical Center, Los Angeles, USA Cedars Sinai Medical Center, Los Angeles, USA https://orcid.org/0000-0002-0487-5555Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1301–1330https://doi.org/10.1145/3583133.3595047Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF