Reviewerly/exHarmony huggingface dataset:
Image Steganography Using HBV And Padded RSA

This work introduces an innovative method for image steganography that merges cryptographic and steganographic techniques to meet the increasing demand for the secure sharing of sensitive data. The suggested approach utilizes a combination of Hybrid Beaufort Vigenere (HBV) Cipher and padded RSA algorithm for encryption, and an improved LSB steganography method to render the embedded data invisible to potential attackers. To further enhance key sensitivity, a key derivation function is integrated.The effectiveness of the algorithm is evaluated through multiple metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean squared error (MSE), correlation coefficient (CC) score, histogram analysis, processing time and key sensitivity. The findings indicate that the suggested method outperforms existing techniques in all examined parameters. However, it should be noted that although the research focuses on image steganography security using the proposed method, it may not be directly applicable to other steganographic techniques or media. Implementing the proposed method in secure communication systems that require sharing sensitive data can effectively ensure the confidentiality and integrity of transmitted information. The novelty of this paper stems from the integration of cryptographic and steganographic methods, providing enhanced security and resistance to various types of attacks through the combination of HBV, padded RSA, and an improved LSB steganography method. Additionally, the integration of a key derivation function further strengthens the technique’s key sensitivity.
ACE-PE: An Automated Code Evaluation Software Tool For Programming Education

An automated code evaluation tool that combines the usage of software quality metrics and object-oriented programming teaching subjects is designed and developed. The tool (called ACE-PE) gives flexibility to instructors to assess student assignments at the level of precision of specific subjects which reveals the degree of student's understanding of covered subjects, and to observe his/her own effort, as well. Provision of content-aware automated fast feedback to students to improve quality of their products and development efforts is another outcome of the proposed solution.
Strategies for a Startup Software-as-a-Service Organizations with Minimal Budget to Achieve Security and Compliance Goals

Startups deploying Software-as-a-Service (SaaS) based business models focus primarily on the software product, aiming to launch the product as quickly as possible with minimal resources. Unfortunately, many SaaS startups fail to reach their true potential due to various reasons resulting in a lack of sales or subscriptions for their platform. One of the reasons why startup SaaS organizations fail is the lack of initial focus on security. However, security is a good differentiator that helps startups be ahead of the pack and achieves a market advantage as they expand their services. So far, there has been little to no research in this area that delves deep into the mindset of these companies and helps them stay secure. This paper focuses on defining a model that assists SaaS startups in getting started on security and enabling trust-based service that could be used as a differentiating factor to improve the service deployment and revenues.
Encryption process of Blockchain based online course curriculum education system

Blockchain security is a comprehensive risk management system that includes assurance services, cybersecurity frameworks, and best practices to reduce the risks of fraud and cyberattacks. In shortened, an inherent data structure with security features is produced by blockchain technology where it is founded on cryptographic, decentralized, and consensus principles that uphold transactional trust. As our technology continuously updates and changes, new techniques and methods are appearing to secularly keep user details. The "Blockchain-based system" is among the finest. In a blockchain-based system is nearly impossible to hack or leak someone's information without permission. This is a more secure and reliable system than most of the other systems. But as this is a new and growing technology few online courses industry is using the system but not with its full function. We suggested including blockchain technology in the system of "Online courses". As a security precaution, the course modules and details will be entered into it, two keys are used throughout the entire program. The first is the public key, which is necessary for our course title and description, and the second is the private key, which is necessary for each course module and the payment system. Multiple machine learning models like Linear regression, random forest that are used to detect fraudulent transactions in the application. Every block and user details were hashed (64 bits) to keep them unique from each other. Numerous applications and software projects have been secured by blockchain technology. During our research, we experimented in a novel way and developed a fresh methodology that can be more secure than previously used applications. In this instance, our machine learning models have performed well so far, and we have also gained high accuracy from our dataset while.
Exploring Advanced Encryption and Steganography Techniques for Image Security

This paper investigates the use of modern steganography techniques for secure image transmission, utilizing four encryption algorithms: AES, DES, RSA, and ChaCha20. Two steganography techniques, LSB and Spread Spectrum, are used to encrypt and embed ciphertext into five different images. The security of the created images is evaluated using metrics such as PSNR, runtime, MSE, histogram, and information entropy. Our analysis reveals that DES is less effective than AES and ChaCha20 for encryption. Furthermore, Spread Spectrum outperforms LSB in terms of security, although LSB produces better stego-image quality. Combining different steganography and encryption techniques enhances the capacity to send photos with a high level of security and robustness. This study provides insightful knowledge on the effective use of steganography and encryption for secure image transmission in various industries, including military and healthcare imaging applications
A Lightweight Medical Image Encryption Scheme Using Chaotic Maps and Image Scrambling

Telehealth is an emerging sector that enables doctors to remotely treat patients using virtual communication. This involves transferring medical images and other clinical data over public networks, presenting a significant challenge for radiology departments to safeguard the confidentiality of such data. Previous researchers have proposed various image encryption techniques, but none have been able to maintain an acceptable performance-to-security ratio. To address this issue, this research aims to develop an image encryption scheme that maintains this ratio using a combination of two chaotic maps: Baker’s map and 2D Logistic Sine Coupling map (2D-LSCM), along with image scrambling. The proposed algorithm’s performance and security are compared to other notable algorithms to demonstrate its effectiveness.
Improved ECC-based Phase Watermarking Method for Video Copyright Protection

In this paper, we present an improvement of a previously proposed phase watermarking method for digital video protection and its investigation conducted under practically valuable conditions. In this improvement, we considered various means to increase the watermark capacity, robustness and visual imperceptibility, including chaotic filling of the frame area with watermark fragments, error-correcting encoding, and exclusion of dynamic video fragments for information extraction. The experiments were conducted in lossy compression conditions with the same or lower bitrate value. The results confirm the positive impact of the proposed improvement and the practical applicability of the phase watermarking method for video copyright protection.
Reliable Face Identification System for Criminal Investigation

Face identification technology has the potential to revolutionize criminal investigation by offering reliable and fast ways of identifying criminals. Although there is significant advancement in face recognition technology, adoption of this technology in law enforcement agencies is at infancy because of factors like system reliability and complexity. In this study, we propose a reliable face identification system (RFIS) for criminal investigation; a novel multi-faced approach for facial identification that identifies suspects by comparing live image, still picture and video obtained from various sources. The proposed system is developed by using two prominent libraries called Dlib and Haar Cascade because of their effectiveness. In addition, RFIS uses Convolutional Neural Network (CNN) architecture as the facial recognition algorithm which is trained on a dataset of criminal facial images to learn the features that distinguish a criminal’s face from non-criminal. Notable benefits of this study are increased reliability and accuracy in facial recognition system which is achieved through combining live picture recognition, still picture recognition and video image recognition. In addition, outstanding advantage of our solution is addressing the potential for bias in the algorithm by using diverse dataset that includes faces of different ages, gender, races, ethnicity and other characteristics. Furthermore, we explore the effectiveness of our solution with the respect to Dlib and Haar Cascade libraries and the results suggest that Haar Cascade library outperforms Dlib library in terms of execution time and accuracy.
Leveraging P2P- and Edge-Based Processing for Compliance Management of IoT-Devices

The Internet of Things (IoT) has connected billions of devices to the Internet. From small "smart lightbulbs" to entire aircraft, more and more items become a connected device. With that comes the challenge of keeping this new class of devices in line with configuration and security standards. As it is explored in this paper, this challenge is oftentimes not properly met. Existing approaches to compliance either do not fit into the constraints of IoT environments or struggle to cope with their scale. This paper explores the shifting workloads to the IoT nodes, leveraging peer-to-peer based approaches for verification and compliance through a proof-of-concept implementation and simulation. This paper demonstrates how a significant verification workload can effectively be shifted into the peer network and compliance verification of peers can be delegated without requiring special knowledge or software on the nodes under audit.
Development of Low-Latency and Real-Time Filipino Children Automatic Speech Recognition System using Deep Neural Network

As no studies have been made yet to use real-time speakers in performing the Filipino children automatic speech recognition system, this study aims to use the available children speech corpus to train the system using Deep Neural Network technology and use real-time children speakers to test the proposed low-latency Filipino children automatic speech recognition system with low word error rate. The 6.76 hours of raw wav files from University of the Philippines Digital Signal Processing Laboratory Filipino Children’s Speech Corpus is pre-processed, extracted, and trained to create the planned automatic speech recognition system. The time delay neural network module of Kaldi Toolkit with symmetrical time strides is used to train the acoustic model of the system while 3-gram approach is used to train the language model. As results, the proposed Filipino children automatic speech recognition system has an average latency of 0.5188 seconds while the other cloud-based speech-to-text applications such as Google Speech-To-Text has 0.7217 seconds delay, and Whisper API with 1.0420 seconds of lag. Moreover, the said system is also evaluated with different input devices having the Jabra wired headset with the lowest word error rate of 1.49%. Using the same Jabra wired headset, the accuracy of online speech-to-text applications are also computed which results to 6.17% word error rate for Google Speech-To-Text and 11.28% for Whisper API. Therefore, the proposed Filipino children automatic speech recognition system outperforms other online speech recognition applications by 1.4 times faster than Google Speech-to-Text and two times faster than Whisper API having highest accuracy of 98.51% using real-time children speakers.
Random Number Generator Based on Optimization and Chaotic System

In this study, a chaotic system-based random number generator design has been carried out. Differential Evolution optimization algorithm is used in the proposed architecture to determine the initial conditions of the chaotic system, unlike the literature. In this way, while predictability is reduced, an architecture that cannot be solved by reverse engineering has been obtained. At the same time, the entropy pool is expanded by generating different initial conditions. The chi-square hypothesis test has been used to test the performance of the initial conditions obtained as a result of 5 independent runs. The results show that the proposed architecture is successful.
Identification of Providers with Similar Risk Profiles in Healthcare Claims Using Graphs

Fraud, waste, and abuse (FWA) in healthcare claims pose challenges to the accessibility of healthcare, adds a burden to health expenditures, and diminishes the quality of care. There is a wide variety of fraudulent behavior among providers, which necessitates lengthy investigative efforts. Timely identification of providers who share traits with other high risk or potentially fraudulent providers is of great importance to help curb FWA and expedite investigations. In this paper, we experiment with a graph topology built on healthcare claim-level features and provider risk indicators. Graph neural networks are used to compute provider node embeddings from this graph. For each provider in the graph we compute pairwise similarity using vector similarity methods, and create a rank order of providers who are similar to a given provider. We compute rank-based performance metrics on the predicted rankings and compare them to subject matter specialist rankings to show the effectiveness of our models in discerning providers with similar risk profiles versus those who are dissimilar.
Feature Selection for Classification Problems by Binary Bare Bones Fireworks Algorithm

Classification represents a rather simple concept but the number of real-world applications that have it as the crucial part is enormous. Several factors determine the quality of classification such as the algorithm used for classification, tuning hyperparameters of the used algorithm and features used to describe instances that should be classified. Choosing an optimal feature set is an important part of the classification. On one hand, more features can capture more differences and similarities between instances which will lead to better classification. On the other hand, a too large set of features can lead to a poor classification model due to many unnecessary features that do not have an effect on the output. Feature selection is a combinatorial optimization problem and metaheuristics like swarm intelligence algorithms (SI) have been used for tackling such problems. In this paper, we proposed an adjusted binary bare bones fireworks algorithm for feature selection. The fitness function was modeled as a weighted sum of two objectives: reducing the number of features and minimizing classification error. The proposed algorithm is tested on the benchmark datasets and compared to other SI approaches from the literature. The proposed method successfully kept or improved classification accuracy while reducing the number of used features.
A Predictive Classification Model for Identifying Conversation-related Mobile Forensic Data in Instant Messaging Applications

The rapidly changing and diverse landscape of instant messaging applications has significantly impacted the field of mobile forensics. To tackle this problem, existing practices and research in mobile forensics often rely on experts to write reverse engineering scripts based on their experience and background knowledge to handle the various database architectures of instant messaging applications in the market. However, this approach is often insufficient due to the rapidly changing nature of these applications.Thus, we proposed a novel mobile forensics method for identifying conversation-related columns and tables in various instant messaging applications. Our method utilizes content-based features of columns combined with a matching algorithm specially developed for the data schema of instant messaging applications. It can identify conversation-related data without relying on table and column names, making it a more flexible and advanced analysis technique. In this paper, we apply the proposed method to authenticate several popular instant messaging applications, including LINE, Messenger, WhatsApp, and Kik, as well as the open-source instant messaging application Rocket.chat, which is not currently supported by existing tools.Our methodology overcomes the limitations of previous works that only supported available instant messaging applications through reverse engineering scripts. By automating the identification process, our method achieved a filtering rate of irrelevant data of at least 88% for each instant messaging application, improving the NIST-compliant procedure mobile forensics process by reducing investigator time and training requirements while increasing accuracy and efficiency.
Data Security Approach Using Blockchain Mechanism and Cryptography Algorithms

Data security is an important aspect of the digital world, and protecting sensitive or confidential data from unauthorized access, modification, or theft is required. So, moving in the same direction, in this research paper, we investigate the performance and security aspects of three widely used encryption algorithms: RC6, AES, and DES on blockchain-based storage systems. Our analysis reveals that each algorithm has its strengths and limitations. The study indicates that using cryptographic algorithms in a blockchain framework is an effective approach to enhancing data privacy across various industries. However, to implement such a system, organizations must carefully consider factors such as scalability, performance, and regulatory compliance. Despite these challenges, the potential benefits of using cryptographic algorithms in a permissioned blockchain network cannot be overlooked. The findings of this study are valuable for organizations seeking to implement a secure and privacy-focused blockchain. We have analyzed the performance of algorithms on our model on the basis of performance analysis, which consists of RAM usage, GPU usage, and CPU usage. Security analysis is made based on key sensitivity and the avalanche effect.
Fused Syntactic and Semantic Parsing for Measuring Privacy Level in Unstructured Text

As more enterprises undergo digital transformation, appropriate data protection measures based on the privacy level of the data become increasingly crucial in preventing the leakage of private information. However, developing privacy levels for text is a significant challenge due to the complexity of syntactic grammar and the concealment of sensitive words in unstructured form. To address this challenge, this study uses deep learning to mine sensitive words and combines syntactic and semantic parsing to analyze contextual information. Multi-dimensional metrics are formulated to obtain the sensitivity metric vector of the record, which is then fed into the classifier to obtain privacy level. Experiments on the CCF BDCI dataset confirm the effectiveness of the model, achieving an F1 score of 90.01%.
Approach Considering Collective Intelligence for Interactive Web Services Composition

Service-oriented computing (SOC) is a new computing paradigm that creates new semantic Web services composition. Moreover, some current interactive (WS) composition approaches have produced multiple aspects of interactivity and collaborative processes with a growing interest in collective intelligence and collaborative procedures to improve and enrich ontological knowledge. Several researchers have attempted to benefit from Web information to improve discovery, selection, and Web services composition. The emergence of the social networking paradigm and the lack of research works dealing with the importance of social networking in knowledge building have led us to think about how to exploit in an enrichment and construction ontology. In this paper, we present a novel ontology with high precision, based on the information existing on social networking sites. We propose an approach that consists of an interactive WS composition relying on an ontology constructed by mining social networking sites (Twitter and Facebook). Our approach is composed of the following three steps: First, information is extracted from social networking sites. Second, a new ontology is built on the basis of the extracted facts. Finally, a semantic WS composition is executed by providing the benefits of the constructed ontology. To validate the proposed approach, we present a method of constructing the proposed ontology and show how it can be utilized for semantic Web services composition. We also demonstrate that the constructed ontology positively affects the process.
Very fast variations of training set size reduction algorithms for instance-based classification

Reduction through Homogeneous Clustering (RHC) and its editing variant (ERHC) are effective data reduction techniques for the k-NN classifier. They are based on an iterative k-means clustering task that discovers homogeneous clusters. The centers of the resulting homogeneous clusters constitute the instances of the reduced training set. Although RHC and ERHC are quite fast compared to several well-known data reduction techniques, the iterative execution of k-means clustering renders both of them inappropriate for data reduction tasks that need to be performed quickly, especially, when run over large training datasets. The present paper proposes simple and very fast variations of the algorithms, which are appropriate for such environments. The variations are called RHC2 and ERHC2 and replace the complete execution of k-means clustering with a fast task that assigns instances to the class centers. The experimental study based on fourteen datasets, and, the corresponding statistical tests, show that the proposed RHC2 and ERHC2 variations are very fast and, at the cost of a small penalty on classification accuracy, they achieve higher reduction rates than their predecessors and other two well-known data reduction techniques. They are good candidates when fast reduction on large datasets is required.