Reviewerly/exHarmony huggingface dataset:
Various Techniques of Cryptanalysis, Their Challenges and Remedial Solutions

Cryptanalysis is a technique that extract the text or information from encrypted text. Nowadays, breaking or hacking the cryptographic algorithm is called as cryptanalysis. It is an approach to find the flaws or weakness within a cipher to make them stronger. This paper provides a comprehensive overview of various techniques of cryptanalysis used for stream ciphers, the challenges encounter in their implementation and potential solutions to address these challenges. This paper covers total nine cryptanalysis techniques which are used to extract information from stream ciphers.
Image Manipulation Detection Using Error Level Analysis

Image editing is now easy because there are so many user-friendly multimedia tools available. The photographs can be altered using a variety of methods. The process of combining two or more photos to produce a single composite image is known as “image splicing,” one of numerous methods for altering images. These edited images might be used to trick others. A deep learning-based method to detect picture splicing in the photographs is suggested in this work. The input image must first be preprocessed using a technique called “ELA” in order to extract the noise remnant from the input image by suppressing the image content. Second, the well-known MobileNet network is used as a feature extractor. The obtained features are then tested for authenticity using the Voting Classifier classifier. Research on the CASIA dataset shows that the suggested approach performs better than competing approaches already in use. The proposed method achieves an average classification accuracy of 94.24.
Key-frame extraction in Spacio-Temporal Neural Networks for Human-Action Classification

Video analysis especially related to human-actions is an crucial part of research done in domain of computer science and mathematics. Some of the areas are image and video processing, computer vision and Video understanding systems. With widespread use of mobile phone and other comb act video recording devices people capture many events that involve human as a center object This paper presents and tests various models that can extract both spacial feature's using CNN and temporal feature using RNN. By Developing a system that train a Deep learning model and optimized it to identify the type of human action/activity and ultimately classify them into categories/classes of different action performed by humans daily. A key-frame extraction approach has been implemented to increase the model's performance. Using an innovative algorithm, this technique selectively finds and classifies key-frames. By concentrating primarily on these key-frames, overall processing time is lowered without jeopardising the model's significant performance increases.
A High Quality Secure Medical Image Steganography Method

The exchange of medical images over the Internet has prompted substantial claims on the last years due to the presentation of cloud-based medical data systems according to various web services in the area of telemedicine applications. The preservation of confidential information has ever been a key indicator of the effectiveness of these systems. In this paper, an efficient data hiding technique is proposed for Digital Imaging and Communications in Medicine (DICOM) images hence security can be specified for the highly invaluable and private sensitive data of the patient. The proposed method is developed for DICOM images to embed Patient Record Information (PRI) for maintaining the fidelity of both the critical components of the image and private patient data. The proposed method is very effective in telemedicine applications as it can increase the security of the PRI during its transmission over the Internet from different remote places.
Facial Expression Recognition with Face Mask using Attention Mechanism and Metric Loss

Facial expression recognition has been widely studied and applied in various fields such as healthcare and commerce. However, with the onset of the COVID-19 pandemic, people around the world started wearing masks. Unfortunately, there have been relatively few studies focusing on facial expression recognition under mask-wearing conditions. Therefore, this paper proposes the Masked Facial Expression Classification Network (MFN). Firstly, MFN extracts global features from input images as well as captures local features to mitigate the negative effects of unnecessary information, such as masks, and to emphasize crucial facial regions like the eyes. Secondly, incorporating an attention mechanism into the feature extractor, it allows the model to focus on important regions and channels for facial expression recognition. Lastly, MFN incorporates the metric loss as a discriminative loss because there is a tendency for high similarity in feature distributions among each class for facial expression recognition. This loss aims to minimize the distance between feature vectors of the same class and maximize the distance between feature vectors of different classes. In the experiment using the dataset with individuals wearing masks, the proposed network demonstrated improved accuracy compared to other models.
Optimal Control of Automated Guided Vehicle

The Rapidly-exploring Random Tree Star (RRT*) algorithm is a popular and effective motion planning approach in robotics and artificial intelligence (AI). The RRT* algorithm's essential characteristics, benefits, and applications are highlighted in this paper. The paper begins by introducing the problem of motion planning in robotic systems and the need for efficient algorithms to generate collision-free paths in complex environments. It then introduces the RRT* algorithm as a solution to this problem, emphasizing its ability to quickly explore the configuration space and find optimal or near-optimal paths. The core ideas behind the RRT* algorithm is explained in the paper, along with the notions of rapidly exploring trees, planning based on random sampling, and incremental tree growth. It illustrates the fundamental distinctions between RRT* and the RRT algorithm, such as the application of cost-to-come measures and rewiring strategies to enhance the optimality of the created pathways.
Whale-Optimized Kernel Extreme Learning Machine for Chest X-ray Image Classification

Chest X-ray images are critical in detecting diseases such as Covid-19, pneumonia, and lung opacity. This paper presents an approach to enhance the accuracy of chest X-ray image classification by implementing the Whale Optimization Algorithm (WOA) to the Kernel Extreme Learning Machine (KELM). The initial step involves applying the Singular Value Decomposition (SVD) method on chest X-ray images for efficient feature extraction. Following this, Principal Component Analysis (PCA) and Lasso regularization techniques are applied for dimensionality reduction and feature selection respectively, which significantly lowers the likelihood of overfitting. This paper significantly improves the classification accuracy by optimizing the regularization parameter in KELM and the width parameter in the Radial Basis Function using WOA. Experimental results underscore the superiority of the WOA-KELM, recording an accuracy rate of 94.9008% in a three-class (Covid-19, pneumonia, normal) X-ray image classification task, a drastic improvement over the conventional ELM (88%) and unoptimized KELM (68.7819%).
UAV Anomaly Detection Using Fleet Data Based on DBSCAN-MSET Method

With the rapid development of artificial intelligence, unmanned aerial vehicle (UAV) has been widely applied in multiple fields for their convenience, low cost, and powerful function. Compared with single-UAV data, the UAV fleet data has more rich features, which can provide greater data collection and rich data information support to achieve more accurate anomaly detection. However, the flight paths of different UAVs vary greatly, which brings many challenges to fleet-level data analysis. And it is difficult for UAV fleet data to get exception labels to accurately build an anomaly detection model. Based on the above problems, this paper presents an anomaly detection method based on multivariate state estimation (MSET) and density-based spatial clustering of applications with noise (DBSCAN) optimization (DBSCAN- MSET). Establishing a normal state representation model from historical UAV data can effectively detect whether the following flight is abnormal. The proposed method is validated against the actual UAV data, and the results show the validity of our method.
A Baseline Modeling and Anomaly Detection Method for Inertial Navigation System Based on Integrated 1DCNN

Flight test is an essential step before aircraft delivery. Safe flight test can not only reduce the cost of aircraft research, but also has great significance for the study of aircraft performance. In order to ensure the safety of flight test, it is necessary to implement anomaly detection for aircraft systems. Inertial navigation system, as a complex system can provide accurate position, velocity, and attitude information for aircraft during flight test. It is very important to monitor the state of inertial navigation system for ensuring the safety of flight test. However, traditional single anomaly detection models perform poorly on different test data and have poor generalization capabilities. In order to improve the generalization ability of the anomaly detection model with inertial navigation data, this paper proposes a prediction-based anomaly detection method based on an integrated one-dimensional convolutional neural network (1DCNN). Firstly, multiple training sets are divided based on the bootstrap method, and then integrated 1DCNN prediction models are built for corresponding training sets. Finally, a static threshold judgment method based on the PauTa criterion is used to detect whether any abnormality occurs. This paper verifies the effectiveness of the proposed method through experiments with real inertial navigation data. The results show that compared with the traditional 1DCNN method, the proposed method significantly improves the performance of anomaly detection and has good generalization ability on different test conditions. The TPR of anomaly data detection reaches more than 97%, which proves that the method has effective anomaly data detection for inertial data.
FPGA Based Hardware Accelerator for Bio-Informatics Applications

We present the design and implementation of an FPGA-based accelerator for bioinformatics applications in this paper. To process large amounts of data generated by next generation sequencing (NGS) technologies, bioinformatics applications require high-performance computing capabilities. Because of their parallel processing capabilities and reconfigurable hardware, FPGAs are a promising solution for accelerating bioinformatics applications. To achieve high throughput and low latency, the proposed accelerator architecture employs parallelism and pipelining. The FPGA accelerator's performance is compared to software implementations on a CPU. The results of the experiments show that the FPGA accelerator outperforms the CPU implementation.
Video Summarization Using Deep 3D ConvNets with Multi-Attention

In this paper, we propose a novel method for key-shots-based video summarization by introducing 3D Convnets with Multi-Attention. The process starts by encoding the video data into time-variant frames in 3D followed by two steps of visual attention. The first step learns attention weights for features inside each frame and the second step learns attention weights for all the frames hence deciding the importance score between 0 and 1 for each frame for the target summarization. The current state-of-the-art method used 2D Convnets with self-attention hence losing the dependency of each frame on the next which results in self-attention focusing on fewer features. The keyframes and their relation with time are not maintained. The experimental studies evaluating the proposed approach on two standard video summarization datasets (i) SumMe and (ii) TVSum produced significant improvements. We report new state-of-the-art for the task of video summarization on these datasets.
Extending Functionality of ERP Systems with IoT Data

Every organization that thinks proactively looks at how to improve the functionality of its ERP system. As the concept of data driven economy has increasing importance, and ERP systems are getting better with every new relevant data they receive, extending the functionality of ERP systems with data collected by the Internet of Things (IoT) is considered as new level of increasing effectiveness and efficiency in business. The aim of this paper is to analyze areas of application of IoT in ERP systems that bring new added value and present one proof of concept (PoC) scenario of application of IoT in ERP systems. Furthermore, the desire is to consider the integration of IoT and ERP systems at a higher architectural level.
Development and Analysis of a Versatile Dataset of Speech, Real and Synthesized, of Arabic Learners

Computer-Aided Pronunciation Training (CAPT) systems are gaining popularity recently due to the advancements in deep neural networks (DNN) and machine learning and the availability of databases of speech of language learners. Unfortunately, research in Arabic CAPT systems suffer from the lack of CAPT datasets compared to other languages. In this paper, we present the details of and the ideas used in the development of a versatile dataset of speech, real and synthesized, of Arabic Learners. To develop the dataset, we utilized an existing Arabic speech corpus, King Saud University Speech Database (KSU-SD). KSU-SD's main application was a speaker recognition system, but it was designed to be useful in other applications such as CAPT systems. KSU-SD includes a large number of speakers from diverse nationalities (Saudis, Arabs, and Non-Arabs). KSU-SD contains the recording of about 60 non-native speakers from more than 20 nationalities, hence we selected it to build the non-native Arabic-CAPT corpus. The developed corpus consists of transcribed, segmented, and annotated speech, which makes it suitable for building Arabic CAPT systems. In addition to presenting the details of developing the dataset, we also present an analysis of the text and speech errors of the dataset. The dataset was verified by many CAPT systems.
Securable Personal Healthcare Record in Cloud Storage

Personal Health Records are now receiving more attention as a result of the expansion of cloud computing (PHR). The privacy and security of patients' medical records must be protected even while e-health systems have the potential to enhance patient care. A number of cryptographic techniques, including encryption and distributed data processing, have been proposed to safeguard the security of private health information. But, because each recipient needs a different decryption key, sharing encrypted material with many parties becomes difficult. Due to the fact that the decryption private key can only be given to one individual, even (t, n) threshold privacy-preserving techniques have their restrictions. Our suggested solution offers a decentralized identity-based approach to decryption that makes it simple to open encrypted data without needing to create new secret keys. Using attacks on adaptively generated cypher text, we further show that our distributed identity-based decryption technique is private. To further ensure privacy protection without the requirement for private key regeneration, we add a (t, n) threshold to our distributed decryption system. With the goal of protecting privacy and secrecy, our solution handles the issue of sharing encrypted personal health information with several parties.
Decentralised Society Using Soulbound Tokens

Today's world revolves around Web 3.0 technology to form a decentralized network as it addresses issues such as privacy, connection, and security that were at stake in Web 2.0. This paper proposes a system that can be used by Educational Institutions, Students, and Recruiting Organizations to provide an environment that can store and verify the credibility of students. The proposed system is built using the concept of Web 3.0 and utilizes technologies such as Soul Bound Tokens(SBTs) that underlie Block Chain technology. On successful implementation of the system, the results observed are; it provides a secure environment to store the certifications or data of students that cannot be forged, reproduced, or removed from the chain, also addresses concerns regarding the privacy of crucial data by proposing the off-chain data storing method which shall enhance the provenance of the student's data. Finally, this system emphasizes building an environment that stores students' data and processes them in a decentralized manner which shall resolve the concerns of Web 2.0.
Cluster GAN-based model for Signature Generation and Verification

Signatures serve as a popular method of personal identity verification in various sectors such as finance and government. Signature verification systems utilize biometric samples to differentiate between authentic and falsified signatures. Although deep learning presents a promising avenue for creating such systems, the requirement of large signature datasets presents a challenge. Current datasets are restricted in size due to privacy concerns. To overcome this limitation, a model that employs cluster generative adversarial networks is proposed. This model utilizes unconditional GANs to expand the signature dataset, which in turn can be used for deep learning-based signature verification. This model can also be potentially utilized in other domains that require privacy.
English Conversation Partner Finder Using MLP Based Fluency Classifier and Linguamatch Algorithm

The ability to communicate fluently in a language is crucial in today's globalized world. Non-native speakers often face challenges in achieving fluency and finding opportunities for language practice. In this research work, we propose a novel approach to address these challenges by developing a fluency detection model and a linguistic matching algorithm. The fluency detection model utilizes machine learning techniques to assess the fluency level of non-native speakers. By analyzing a large dataset of audio recordings, the trained model can accurately classify fluency levels as low, intermediate, or high. This model can provide valuable insights into an individual's language proficiency and serve as a tool for self-assessment and improvement. Complementing the fluency detection model, there is also a linguistic matching algorithm that aims to connect non-native speakers with native speakers for language practice. By considering the user's interests and fluency level, the algorithm suggests suitable language partners, facilitating meaningful language exchanges and enhancing the learning experience. Through our research, the proposed work aims to empower non-native speakers by providing them with a comprehensive toolset for fluency assessment and language practice.
Fuzzy Observer of Induction Motor Torque and Speed Based on Dynamic Filters

The paper deals with the design of a fuzzy observer of the motor torque and angular speed of an induction motor (IM). It is based only on directly measurable stator quantities, such as the supply frequency, the stator voltage, and current components. Appropriately designed simple stable dynamic filters were used to construct the static part of the fuzzy observer. Based on them, a consistent database of their input, and output signal samples was obtained, which allows design a static fuzzy system using the MATLAB’s Anfisedit tool. The quality of the designed observer was verified by simulations in the MATLAB/Simulink environment. The described method is general-no a priori knowledge of the modeled system order nor any access to its internal state variables is required.
Modulated Signal Open-Set Identification of Complex Convolutional Neural Networks Combined with Incremental Learning

This paper proposes a modulated signal open set recognition model based on CVCNN (Complex Value Convolutional Neural Network) combined with incremental learning to address the limitations of traditional modulated signal open set recognition in distinguishing unknown classes. The study utilized CVCNN as a feature extractor to extract semantic features of samples and identify unknown signals based on the distance center measure between the semantic signals. The findings indicate that the CVCNN model can accurately identify trained samples, reject untrained unknown classes, and distinguish between different unknown classes. Additionally, the complex convolutional neural network proposed in this study is well-suited for feature extraction and recognition of IQ (Inphase and quadrature) signals.
Enhancing Image Security Using Legacy-Based Encryption With Chaotic Tent Map and Memristor

The development and utilization of effective image encryption techniques is seeing an unprecedented need with the advancements in multimedia production and exchange over unsecured networks. In a simultaneous manner, chaotic functions are increasingly being used by scholars to effectuate chaos in a deterministic manner, to develop pseudo-random bit-streams for the construction of encryption keys and substitution boxes. This work proposes the utilization of a 3-stage image encryption scheme. The Tent chaotic map is used in the first stage, followed by a legacy cellular automata S-box in the second stage, and finally a Memristor chaotic system in the third stage. Performance evaluation shows superior entropy performance and comparable values to the literature in terms of other metrics.
Unveling Chess Algorithms Using Reinforcement Learning and Traditional Chess Approaches in AI

In the history of Artificial Intelligence, chess has been the subject of the many researches. Chess bots are now capable of beating grandmasters using reinforcement learning which is a branch of Artificial Intelligence that focuses on developing intelligent systems capable of making optimal decisions through engagement with the environment via trial-and-error. Alpha Zero, Stockfish 8, and Q-learning are the three prominent methods used in chess bots. These methods have revolutionized the field of artificial intelligence in chess, showcasing remarkable capabilities and advancements. This paper provides a comparative study of these approaches by examining the contrast in their underlying approaches, performance, training methodologies, and limitations.
Improved Brain MRI Segmentation for Early Detection of Alzheimer's Disease using Overlaying Analysis and Thresholding

Alzheimer's disease (AD) with memory loss and cognitive decline as symptoms of the neuro-degenerative condition, damages the brain. The novel approach is proposed for detecting regions of the brain affected by AD using overlaying analysis and thresholding techniques. The current overlaying analysis methods lack accurate thresholding, which leads to a loss of pixel data, indicating highly varied segregation of brain regions. The usage of efficiently identifying disparities in pixel values between multiple digital images through computational analysis is highlighted in the proposed work. We also discuss the challenges and limitations of current image analysis methods, including issues related to misaligned scans and under-thresholding. A new function to find absolute pixel differences on brain Magnetic Resonance Imaging (MRI) scans, segregate the brain into multiple affected regions, and identify those affected by Very Mild Dementia Alzheimer's is applied. The segregated regions are then overlapped and compared with healthy brain regions to observe differences in structural and functional properties. The approach can accurately identify affected regions of the brain and provide valuable information for the diagnosis and prognosis of AD. The model contributes to the ongoing efforts to develop effective and non-invasive methods for detecting and tracking the progression of AD and highlights the potential of observing MRI scan differences using overlapping analysis as a powerful tool in neuroimaging research.