Reviewerly/exHarmony huggingface dataset:
Image Segmentation using Hybrid Relay Level Sets

This research article offers a fast and efficient method of image segmentation using hybrid relay level sets. This automata model of level sets is achieved by the creating subregions with accordance to that of the previous subregion's boundaries. The contour evolves to the desired object by continues small shifts in contour until the level set function is stabilized with ensured full segmentation. This approach has the advantage of detecting more boundaries without interaction to outside world and without specifying number of relays for final overall segmentation. This approach of image segmentation outperforms traditional level set methods in terms of its relay fashion of contour evolution.
A comparative analysis of Deep Learning Algorithms for Breast Cancer Detection

Some breast cells begin to develop irregularly, leading to breast cancer. These cells continue to grow and divide faster than healthy cells, forming large clumps. Surveys predict that there will be about 170,000 new cases of breast cancer diagnosed in India by 2020. Studies have shown that the disease is likely to affect one woman in her 28. Many machine learning algorithms are available for breast cancer detection. Here Logistic regression, a Decision Tree classifier and Random Forest classifier algorithms have been proposed and simulated. The model accuracy has been estimated to 97.23 to 91.22 % for mentioned algorithms.
Detection, Classification and Counting RBCs and WBCs Using Deep Learning

Blood is a highly complex and essential biological fluid in the human body that performs various vital functions. It comprises different components, including plasma, platelets, and various types of blood cells, each possessing unique properties and functions. Among these blood cells, red blood cells (RBCs) and white blood cells (WBCs) hold significant importance. In the medical world, detection, Identification, and counting of these RBCs and WBCs is important to evaluate a person's comprehensive health condition. Counting blood cells is one of the most common tests conducted at medical treatment facilities. Manually detecting and counting blood cell counts using a hemocytometer is time-consuming and tedious. The challenges here to be worked on and the goals to be achieved are related to Accurately detecting and counting two types of blood cells within a limited timeframe, this paper proposes a deep learning approach to this issue. The algorithm that detects and classifies objects “You Only Look Once” (YOLO) is being used in our approach. The output achieved from the work on medical images of blood cells show the detected cell's bounding boxes, classification results, and confidence levels, along with the count of each type of blood cell.
Importance of CURE Clustering Algorithm over K-Means Clustering Algorithm for Large Data-set

The method of finding natural groupings within multidimensional data based on some similarity metrics is known as data clustering. There are many clustering algorithms proposed over time, and many of them have gained considerable popularity due to the ease of use and efficiency of such algorithms. Although most of the algorithms perform well in many use cases, some are limited to specific types of data distributions. This paper aims to demonstrate the importance of Cure algorithm over the K-Means Algorithm with the help of a specified performance metric for large data sets.
Adoption of Blockchain Technology in Land Registry Systems

The adoption of blockchain technology in land registry systems has gained significant attention in recent years due to its potential to increase efficiency, transparency, and security. This paper provides an overview of the current state of land registry systems and the challenges they face, in-cluding issues related to fraud, corruption, and a lack of trust. Then it discusses how blockchain technology can address these challenges by providing a decentralized, tamper-proof, and transparent platform for recording land transactions. The paper also examines some of the existing blockchain-based land registry systems and their features, using approaches and methodologies, including the use of smart contracts and digital signatures. Finally, the paper discusses the potential benefits and limitations of adopting blockchain technology in land registry systems and provides recommendations for policymakers and stakeholders interested in implementing these systems.
Temples Restoration using Gated Convolution and Contextual Attention in Generative Adversarial Networks

India in the past is renowned for its magnificent and extraordinary architecture. Due to several invasions and climatic changes over the years, these magnificent works of art did not last very long. A novel Deep Learning approach is proposed in this work to digitally restore broken and eroded pieces of heritage temples and sculptures in India. This approach uses Gated Convolution and Contextual Attention layers added to Generative Adversarial Networks(GANs). The learning of this model is enabled by using copyright-free images of Indian temples and sculptures after going through data collection and data pre-processing steps. The data pre-processing involves resizing and applying free-form masks on missing pieces. The final images restored by this model have an L1 loss of 0.29 after training the model for 7,10,000 iterations. The whole objective of this work is to rebuild images of ancient temples as they were before being damaged by several invasions and climatic changes, thereby digitally restoring the past glory of India in a small way.
Fuzzified Contrast Enhancement and Segmentation For Nearly Invisible Images

Any computer vision application must first improve a picture before continuing to process it color details losses during the enhancement process is a prevalent issue with most current techniques when applied to photographs that are essentially unnoticeable the qualitatively undetectable image should be improved while maintaining its freshness and coloring. Histogram equalization, a traditional approach of contrast enhancement, resulting in more than enhancement of something like the picture, particularly one with poorer resolution. The objective of this research is to develop an innovative fuzzy inference system capable of enhancing the contrast of low-resolution photos while simultaneously addressing any existing limitations, existing techniques and segmenting the tumor in MRI images. The outcomes from the two methods are contrasted. Throughout this research, the technique results in a very tiny change in intensity value while maintaining the image's information about color and brightness. The method enhances striking contrast while preserving naturalness without introducing any artefacts. Active contour processing on these photos produces extremely accurate segmentation results. Mainly this is used to detect the tumor in MRI images with some basic morphological operations.
Dual Image Reversible Data Hiding: A Survey

Conventional reversible data hiding (RDH) techniques are concerned with hiding secret information in the cover and extracting it along with the original cover at the receiver end. In the recent past, some researchers investigated dual image (DI) RDH in order to increase the data hiding capacity and visual quality of the marked cover image. In this manuscript, we reviewed state-of-the-art DI-RDH techniques and categorise them techniques mainly into exploiting modification direction, turtle shell, central folding strategy, and Pixel value differenceing along with explaining their merits and demerits. DI-RDH methods have been examined, considering data hiding capacity and PSNR.
Digital Competence of TVET Trainers and Technology Acceptance in the Context of Developing Countries

The chapter focuses on the digital competence of TVET trainers and technology acceptance in the context of developing countries. Three objectives were addressed; namely, the level of digital competence of TVET trainers, challenges to the development of digital competence in TVET trainers, as well as factors that determine the acceptance of technologies in their teaching practice. Framed by the digital competence for TVET teachers and the technology acceptance model, the chapter objectives were addressed through a robust review of published articles, policy papers, theses, and dissertations among others, based on the focus of the chapter. Findings revealed the inadequacy of digital literacy of TVET trainers as well as unavailability and access to digital infrastructure among others constituted hindrances to the development of digital competence in TVET trainers. The chapter provides conclusions and recommendations.
Exploring Educational Practices for Non-Standard Didactic Situations in Small Schools

The small school, non-standard didactic situations (SDiNS) located in geographically and telematically disconnected territories, sees digital technology as a tool by which to “build or rebuild ties” and to enhance the relational dimension among the actors involved in the homebound education (HE) process which takes place in alternative (digital) environments from the traditional classroom. The work presents the results of the research aimed at identifying didactic situations that were implemented in the case of HE students for health problems (HESsHP) and / or for environmental obstacles (HESsEO). The teaching, organizational, and management strategies adopted by small schools and tracked the main criticalities emerged will be reported and discussed.
Survey on Task Scheduling with Ant Colony Optimization

Ant Colony Optimization(ACO) is considered as a high level problem independent algorithm that is increasingly applied to solve complex task scheduling problems in various domains. It is an algorithm that draws inspiration from nature and is frequently used to address task scheduling issues in a variety of fields. ACO algorithms discover the best routes from a starting point to a destination by modelling the behaviour of actual ant colonies. In this paper, we provide a comprehensive survey of ACO task scheduling, putting up the advantages, limitations, and challenges on this approach. We review the basic principles and variations of ACO algorithm, as well as their applications to different scheduling objectives and constraints. We also discuss the performance comparison of ACO with other scheduling algorithms and the optimization of ACO parameters. Furthermore, we identify the future research directions and potential applications of ACO task scheduling, such as hybridization with other algorithms and integration with cloud computing. Our survey provides insights into the leading edge research on ACO task scheduling and highlights the potential of this approach to address the increasing demand for efficient and effective task scheduling solutions.
TPM based Secure Boot in Embedded Systems

Today embedded systems are becoming more and more sophisticated and are being used in a wide range of technologies across an array of industries. Therefore its security has become a major concern. Security is always an important and crucial task required to keep all threats or attacks away from the system. It is an extensive and constantly evolving domain. However, there are still some unexplored security aspects that may need our attention and one of them is hardware-based security. Hardware-based security enhances security by using a combination of trusted hardware and software components. In this paper, we introduce a detailed secure boot procedure based on the Trusted Platform Module (TPM) for embedded systems, which aims to achieve hardware security. Without TPM based signature verification, the system cannot boot up, thus preventing the code from getting modified and ensuring that the system always boots up in a well-defined manner. We have tested our approach on different types of embedded systems to provide better results about boot time variations and have also discussed about system updates when the secure boot feature is enabled.
Study and Comparative Analysis of Data Hiding Methods for Animated GIFs

In this paper, we study and comparatively analyze the available data hiding methods for animated GIFs. Since there exist different data hiding techniques, and the choice of technique can greatly affect the security and robustness of the resulting stego-media. So, to provide guidance to the prospective researchers and users, an evaluation of several popular animated GIF based data hiding techniques which include LSB substitution, palette-based, frame-based, among others. Our experiments involve analyzing those data hiding techniques based on their imperceptibility, payload capacity, and resistance to common attacks such as visual inspection and statistical analysis. The findings suggest that while some techniques offer better payload capacity, others provide stronger security and resistance to detection. Finally, we conclude by summarizing the strengths and weaknesses of each technique and providing recommendations for their practical use in various data hiding applications.
Subspace-Classification Approach for Simulated Tuple Class Assignment

We developed a subspace classifier for measurement classification to provide an alternative to current deep learning approaches. Many modern neural networks cannot provide an understandable explanation for their classification. The subspace classifier provides a decomposition of the classification problem making it computationally simpler. We first use a Bayesian method in which all the class conditional probabilities for the entire measurement space can be stored in memory.Then we made experiments with simulated class conditional distributions and defined a subspace classifier that only stores the class conditional probabilities for the subspaces. This can use much larger distributions than the previous model as it uses much less memory so we expanded to cases where the measurement space is generated sequentially and everything does not have to be in the memory at the same time.For cases with distributions that fit in the memory we also compared a Bayesian approach with the subspace approach. The Bayesian subspace classifiers consistently outperformed the subspace classifiers without Bayes rule by a large margin. We also compare the subspace classifier with 3 Python Machine Learning Models, namely a Ridge Classifier, a Multi-Layer Perceptron (MLP) classifier (neural network), and a Support Vector Machine (SVM) on a set of tuples and class conditional probability distributions with 4 classes. The subspace classifier had an average probability of correct identification of 0.25172, the SVM model had an average accuracy of 0.20987, the neural network MLP classifier had an average accuracy of 0.2140 and the Ridge Classifier had an average accuracy of 0.2798 over 10,000 trials.
Enhancement of CNNHQ with an Activation Function

In this paper, we present an overview of quantum neural network (QNN) and quantum computing benefits in the Artificial Intelligence field with methods used to enhance the application of machine learning. The approach uses quantum computers to enhance Convolutional Neural Network (CNN) with Hybrid QNNs using IBM Qiskit called the Convolutional Neural Network Hybrid Qiskit (CNNHQ). This paper will compare and focus on using the quantum concepts of computation to develop and optimize the network process entirely depending on Activation functions used in the neural network approach.
Keyword Extraction From Specification Documents for Planning Security Mechanisms

Software development companies heavily invest both time and money to provide post-production support to fix security vulnerabilities in their products. Current techniques identify vulnerabilities from source code using static and dynamic analyses. However, this does not help integrate security mechanisms early in the architectural design phase. We develop VDocScan, a technique for predicting vulnerabilities based on specification documents, even before the development stage. We evaluate VDocScan using an extensive dataset of CVE vulnerability reports mapped to over 3600 product documentations. An evaluation of 8 CWE vulnerability pillars shows that even interpretable whitebox classifiers predict vulnerabilities with up to 61.1% precision and 78% recall. Further, using strategies to improve the relevance of extracted keywords, addressing class imbalance, segregating products into categories such as Operating Systems, Web applications, and Hardware, and using blackbox ensemble models such as the random forest classifier improves the performance to 96% precision and 91.1% recall. The high precision and recall shows that VDocScan can anticipate vulnerabilities detected in a product's lifetime ahead of time during the Design phase to incorporate necessary security mechanisms. The performance is consistently high for vulnerabilities with the mode of introduction: architecture and design.
Identifying Key Classes for Initial Software Comprehension: Can We Do It Better?

Key classes are excellent starting points for developers, especially newcomers, to comprehend an unknown software system. Though many unsupervised key class identification approaches have been proposed in the literature by representing software as class dependency networks (aka software networks) and using some network metrics (e.g., h-index, a-index, and coreness), they are never aware of the field where the nodes exist and the effect of the field on the importance of the nodes in it. According to the classic field theory in physics, every material particle is in a field through which they exert an impact on other particles in the field via non-contact interactions (e.g., electromagnetic force, gravity, and nuclear force). Similarly, every node in a software network might also exist in a field, which might affect the importance of class nodes in it. In this paper, we propose an approach, iFit, to identify key classes in object-oriented software systems. First, we represent software as a CSN <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WD</inf> (Weighted Directed Class-level Software Network) to capture the topological structure of software, including classes, their couplings, and the direction and strength of couplings. Second, we assume that the nodes in the CSN <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WD</inf> exist in a gravitation-like field and propose a new metric, CG (Cumulative Gravitation-like importance), to measure the importance of classes. CG is inspired by Newton's gravitational formula and uses the PageRank value computed by a biased-PageRank algorithm as the masses of classes. Finally, classes in the system are sorted in descending order according to their CG values, and a cutoff is utilized, that is, the top-ranked classes are recommended as key classes. The experiments were performed on a data set composed of six open-source Java systems from the literature. The results show that iFit is superior to the baseline approaches on 93.75% of the total cases, and is scalable to large-scale software systems. Besides, we find that iFit is neutral to the weighting mechanisms used to assign the weights for different coupling types in the CSN <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WD</inf> , that is, when applying iFit to identify key classes, we can use any one of the weighting mechanisms.
PExReport: Automatic Creation of Pruned Executable Cross-Project Failure Reports

Modern software development extensively depends on existing libraries written by other developer teams from the same or a different organization. When a developer executes the software, the execution trace may go across the boundaries of multiple software products and create cross-project failures (CPFs). Existing studies show that a stand-alone executable failure report may enable the most effective communication, but creating such a report is often challenging due to the complicated files and dependencies interactions in the software ecosystems. In this paper, to solve the CPF report trilemma, we developed PExReport, which automatically creates stand-alone executable CPF reports. PExReport leverages build tools to prune source code and dependencies, and further analyzes the build process to create a pruned build environment for reproducing the CPF. We performed an evaluation on 74 software project issues with 198 CPFs, and the evaluation results show that PExReport can create executable CPF reports for 184 out of 198 test failures in our dataset, with an average reduction of 72.97% on source classes and the classes in internal JARs.
Lejacon: A Lightweight and Efficient Approach to Java Confidential Computing on SGX

Intel's SGX is a confidential computing technique. It allows key functionalities of C/C++/native applications to be confidentially executed in hardware enclaves. However, numerous cloud applications are written in Java. For supporting their confidential computing, state-of-the-art approaches deploy Java Virtual Machines (JVMs) in enclaves and perform confidential computing on JVMs. Meanwhile, these JVM-in-enclave solutions still suffer from serious limitations, such as heavy overheads of running JVMs in enclaves, large attack surfaces, and deep computation stacks. To mitigate the above limitations, we for-malize a Secure Closed-World (SCW) principle and then propose Lejacon, a lightweight and efficient approach to Java confidential computing. The key idea is, given a Java application, to (1) separately compile its confidential computing tasks into a bundle of Native Confidential Computing (NCC) services; (2) run the NCC services in enclaves on the Trusted Execution Environment (TEE) side, and meanwhile run the non-confidential code on a JVM on the Rich Execution Environment (REE) side. The two sides interact with each other, protecting confidential computing tasks and as well keeping the Trusted Computing Base (TCB) size small. We implement Lejacon and evaluate it against OcclumJ (a state-of-the-art JVM-in-enclave solution) on a set of benchmarks using the BouncyCastle cryptography library. The evaluation results clearly show the strengths of Lejacon: it achieves compet-itive performance in running Java confidential code in enclaves; compared with OcclumJ, Lejacon achieves speedups by up to 16.2x in running confidential code and also reduces the TCB sizes by 90+% on average.
Enhancing Deep Learning-based Vulnerability Detection by Building Behavior Graph Model

Software vulnerabilities have posed huge threats to the cyberspace security, and there is an increasing demand for automated vulnerability detection (VD). In recent years, deep learning-based (DL-based) vulnerability detection systems have been proposed for the purpose of automatic feature extraction from source code. Although these methods can achieve ideal performance on synthetic datasets, the accuracy drops a lot when detecting real-world vulnerability datasets. Moreover, these approaches limit their scopes within a single function, being not able to leverage the information between functions. In this paper, we attempt to extract the function's abstract behaviors, figure out the relationships between functions, and use this global information to assist DL-based VD to achieve higher performance. To this end, we build a Behavior Graph Model and use it to design a novel framework, namely VulBG. To examine the ability of our constructed Behavior Graph Model, we choose several existing DL-based VD models (e.g., TextCNN, ASTGRU, CodeBERT, Devign, and VulCNN) as our baseline models and conduct evaluations on two real-world datasets: the balanced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{FFMpeg}+\text{Qemu}$</tex> dataset and the unbalanced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Chrome} +\text{Debian}$</tex> dataset. Experimental results indicate that VulBG enables all baseline models to detect more real vulnerabilities, thus improving the overall detection performance.