Reviewerly/exHarmony huggingface dataset:
Multi-Head Self Attention for Enhanced Object Detection in the Maritime Domain

Maritime object detection is crucial for improving the effectiveness of unmanned surface vehicles (USVs), enhancing situational awareness at sea, and strengthening coastal surveillance. Visual understanding is a promising research thematic that can improve the accuracy of ship detection in maritime environment. However, due to the particularities of the maritime environment and the lack of datasets, this field remains limited.In this paper, we propose using Multi-Head Self-Attention (MHSA) to enhance maritime object detection and address the challenges faced. In addition, we conduct a comparative analysis of publicly available maritime datasets to evaluate the performance of different models. Our findings demonstrate that the DETR (DEtection TRansformer), AACN (Attention Augmented Convolutions Networks), and BoTNet (Bottleneck Transformers) models, based on MHSA, exhibit superior detection accuracy and efficiency compared to other existing methods in the maritime domain. This suggests that MHSA is a highly effective approach for improving maritime object detection. Our analysis of the results also provides insights into potential avenues for future research on attention mechanisms in the maritime domain.
Comparison of Dynamic DES with Existing Symmetric Key Algorithms

The concept of security is fundamental to networking. The basic objective of security is to defend against various types of assaults on these networks that hold sensitive data. By altering criteria such as key size, altering the number of iterations and ultimately using confusion box as the S-box, the security of cryptographic algorithms can be increased. Data exists in different forms such as text, images, audio and video. In DES, S-boxes are used to increase the security of the data. All of these 8 S-boxes have 64 elements. Unique numbers in the 0-15 range can be found in each row. These S- boxes are generated dynamically by our suggested system depending on the key and no storage of these elements are required. The comparison of Dynamic DES with other symmetric algorithms like DES, AES and Blowfish yields better results for Dynamic DES in terms of Balance, Bit difference, Time and Memory needed for Execution.
Github Recommendation System And User Analytics

This project aims to build a recommendation system that suggests relevant repositories for users to contribute to on Github. The system will utilize the user’s past activity to group them With other repositories which have similar activity, and use appropriate machine learning techniques to form associations between users and repositories. The primary goal is to motivate users to participate in open-source projects by recommending projects that match their skills and reducing the workload of project maintainers. The proposed system will help users discover repositories that align with their interests and skill sets, and streamline the process of finding suitable projects to contribute to on Github.
Digital competencies and the 21&lt;sup&gt;st&lt;/sup&gt; century skills of university teachers in Nigeria

The study examined university teachers’ digital literacy in Nigeria’s south-south geopolitical region. The descriptive survey approach was used, and three research questions served as the study’s direction. Data were gathered via an online questionnaire. SPSS version 23 was used to calculate the mean and standard deviation after frequency counts and simple percentages were used to evaluate the data while the independent t-test was used to test the hypothesis. The online survey was distributed to 200 faculty members, and 91 of them fill it out, yielding a 46% response rate. The sample for the study consisted of 91 lecturers who answered the online survey. Two measurement and evaluation specialists and two experts in digital technology evaluated the questionnaire. Cronbach’s alpha was employed to determine the instrument’s dependability, and the result was 0.74. The results showed that university lecturers possess high levels of both fundamental and multidimensional digital competence. The survey found that among the difficulties experienced by university teachers in acquiring digital skills were a lack of opportunity for information and communication technology (ICT) training, the high cost of ICT literacy training, inadequate ICT facilities, and a lack of sponsorship. Lastly, the outcome of the hypothesis test indicates that there is no statistically substantial difference in the levels of digital competence between male and female university lecturers. Based on their findings, the researchers suggested that university administrators set up digital skills training for university faculty members as well as assistance and incentives to promote development of digital abilities. The government should be dedicated to education overall by investing adequate finances for teacher training, infrastructural development, and enhanced welfare programs to deliver excellent university education in this digital era.
GCM Variants with Robust Initialization Vectors

The complexity and isomerization of communication networks have put forth new requirements for cryptographic schemes to ensure the operation of network security protocols. Robust cryptographic schemes have been gradually favored. The robust initialization vector (RIV) instead of the synthetic initialization vector (SIV) was first introduced to support strong security and robust authenticated encryption. This paper first introduces RIV to GCM-SIV1, proposes a robust variant, GCM-RIV1, and proves that it ensures birthday-bound subtle AE (SAE) security and nonce-misuse resistance. Then, to support beyond-birthday-bound (BBB) security with graceful degradation, we introduce another, stronger security variant, GCM-RIV2, and prove that it allows gracefully degrading BBB SAE security in the faulty nonce setting. Finally, the performance of GCM-RIV1 and GCM-RIV2 is discussed and compared.
CMAF: Cross-Modal Augmentation via Fusion for Underwater Acoustic Image Recognition

Underwater image recognition is crucial for underwater detection applications. Fish classification has been one of the emerging research areas in recent years. Existing image classification models usually classify data collected from terrestrial environments. However, existing image classification models trained with terrestrial data are unsuitable for underwater images, as identifying underwater data is challenging due to their incomplete and noisy features. To address this, we propose a cross-modal augmentation via fusion ( CMAF ) framework for acoustic-based fish image classification. Our approach involves separating the process into two branches: visual modality and sonar signal modality, where the latter provides a complementary character feature. We augment the visual modality, design an attention-based fusion module, and adopt a masking-based training strategy with a mask-based focal loss to improve the learning of local features and address the class imbalance problem. Our proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/WilkinsYang/CMAF.
Modification Detection Of Medical Images using ORB Feature Extraction Algorithm and Logistic Regression

This paper presents an approach for the detection of modifications in DICOM images using the ORB algorithm combined with logistic regression. The goal is to accurately identify whether a DICOM image has been modified. Features are extracted using the ORB algorithm, and a logistic regression classifier is trained on these features. Padding techniques are used for consistent image sizes, and bagging is applied to improve the model’s performance. Experimental results demonstrate an 85 percent accuracy in detecting modifications. Future work involves exploring deep learning approaches and additional feature extraction algorithms. This approach contributes to maintaining data integrity in medical image repositories and supports image forensics in healthcare applications.
Unspervised Low-Light Inage Enhancement Based On Deep Lightness And Grey Pixel Estimation

Images captured under low-light conditions often suffer from inadequate lightness and low color contrast, resulting in reduced performance of computer vision-related applications. In this work, we propose an unsupervised network for enhancing low-light images, which enhances the lightness and color constancy of images. To achieve this, we introduce two sub-modules, LC-Net and GP-Net, which estimate high-order curves for lightness enhancement and gray pixels for color constancy, respectively. The proposed method shows promising results in improving the quality of low-light images. To further enhance the information reasoning capability of our proposed method, we adopt an attention block that combines channel and space attention as a basic unit for the network. We evaluate the performance of our method quantitatively and visually in comparison to other existing low-light image enhancement techniques through experiments, the results indicate our method can outperform other approaches greatly in terms of color fidelity and lightness enhancement.
Forecasting of Cyclone Severity using Intelligent NeuralNets subjecting to Infrared Imaging

Humanity has benefited enormously from the recent explosion in technology. Almost every day, whether it be through the use of Artificial Intelligence or other technologies, we as humans have been adopting. The Internet of Things One of these is Artificial Intelligence, a concept or technology stack that has advanced to astounding heights. From IBM’s Deep Blue invention to Google’s development of self-driving cars, Al has come to envelop people in our modem world. In this effort, artificial intelligence, namely neural networks, will be used to predict the intensity of a Cyclone. To model the same, we made use of INSAT3D Infrared Imagery. For, the work, we are employing a 3 Layer Convolutional Neural Network abbreviated as CNN, assisted together with Adam Optimizer, and a total of 50 epochs. For comparison with other Mechanisms, we make use of Root Mean Squared Error as a comparator. The research aims to aid the Disaster Management Committee and the Government Officials with much accurate prediction of the Intensity of the Cyclone in due time.
An anonymous user discovery algorithm based on the Naive Bayes algorithm

Deanonymization research is the key research content in the field of anonymous communication, which is related to the healthy development of the network. In this paper, we study a deanonymization algorithm with low false alarm rate from the perspective of network traffic analysis. The algorithm uses the network traffic analysis technology to associate the traffic of different network nodes, so as to find the communication relationship between different hosts and realize deanonymization. When conducting flow correlation, this paper uses the Kull-back-Leibler distance to measure the similarity between different flow. In order to reduce the error rate of identification, this paper uses three features and naive Bayes classification methods to conduct a secondary analysis of the associated hosts, thus reducing the false positive rate. Experiments show that the proposed deanonymization algorithm can achieve the highest recognition accuracy of more than 94%.
Implementation of K-Nearest Neighbor For Classify Hate Speech on Twitter

Twitter is a social networking platform that uses microblogging. A form of social media called microblogging enables individuals to write and publish actions or ideas without restriction. Twitter's effects are mixed, including both positive and negative ones. The result is the rise of various criminal offenses, such as hate speech. Of course, where it can take a long time to be discovered by the algorithm, it requires the assistance of a linguist to identify hate speech. The K-Nearest Neighbour approach is used in this study, with a total of 1000 data points split into 300 test points and 700 training points. with the distinction between racial, racist and neutral hate speech. The accuracy of the training data increased by 70%, 80%, and 90% according to the testing process using the confusion matrix, or 98.5%, 98.3%, and 98.5% with a value of k=7. The majority of testing data is categorised as 33% religious hate speech, 17% racial hate speech, and 50% neutral.
Span-Based Attention Decoder Framework For Aspect Sentiment Triplet Extraction

Aspect sentiment triplet extraction (ASTE) aims to extract aspect terms, opinion terms and sentiment polarity from the comment sentence. Recent ASTE models rely on the word-level interaction without considering the context representation in sentiment prediction, which limits the performances of sentiment prediction and triplet extraction. We propose a span-based attention decoder model (SBAD) by using the span-level interaction between aspect and opinion when predicting pairs sentiment. SBAD matches the selected aspect term with the opinion term one by one, and the pairs are considered to predict sentiment polarity. To further capture sentiment features from context representation, we design a two-layer multi-head attention decoder to decode the interaction among aspect, opinion and context representation. In the first layer, each pair relationship between aspect and opinion is captured. In the second layer, the sentiment features of the context representation that the pair concern is further captured. To verify the effectiveness of our model, we conduct several experiments on ASTE datasets. The results show that the proposed model significantly outperforms the strong baseline models on four ASTE datasets.
BNS: A Detection System to Find Nodes in the Bitcoin Network

Bitcoin was launched over a decade ago and has made an increasing impact on the world’s financial order, which has attracted the attention of researchers all over the world. The Bitcoin system runs on a dynamic P2P network, containing tens of thousands of nodes, including reachable nodes and unreachable nodes. In this article, a detection system, BNS (Bitcoin Network Sniffer), which could collect as many Bitcoin nodes as possible is proposed. For reachable nodes, the authors designed an algorithm, BRF (Bitcoin Reachable-Nodes Finding), based on node activity evaluation which reduces the nodes to be detected and greatly shortens the detection time. For unreachable nodes, the authors trained a decision tree model, BUF (Bitcoin Unreachable-Nodes Finding), to identify unreachable nodes based on attribute features from a large number of node addresses. Experiments showed that BNS discovered an average of 1093 more reachable nodes (6.4%) and 662 more unreachable nodes (2.3%) than the well-known website “Bitnodes” per day. It showed better performance in total nodes and efficiency. Based on the experimental results, the authors analyzed the real network size, node “churn”, and geographical distribution.
Performance Analysis of Deep Learning Models over BreakHis Dataset using Up-Sampling and Down-Sampling Techniques for Classification of Breast Cancer

It’s very challenging to determine the actual cause of breast cancer, therefore early diagnosis is crucial to lowering the disease’s fatality rate. The likelihood of survival increases by up to 8% with early cancer identification. Even seasoned radiologists, however, struggle to recognize characteristics like micro-classifications, lumps, and masses, which results in significant false positive and false negative rates. In recent years we have seen a lot of development in deep learning and image processing which gives rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. In my work, the performance of a Convolutional Neural Network (CNN) and Visual Geometry Group 16 (VGG-16) was analyzed on a breast cancer dataset using upsampling and downsampling techniques. Upsampling involves increasing the number of instances in the minority class (in this case, breast cancer cases) by duplicating them or by performing some other data augmentation technique, while downsampling involves reducing the number of instances in the majority class (non-breast cancer cases) by randomly removing some of them. The purpose of these techniques is to address imbalanced datasets, where one class significantly outnumbers the other. The results of the analysis showed that CNN performed better on the balanced datasets achieved through upsampling and downsampling.
A Machine Learning Approach to Unusual Object Detection in Faint Satellite Imagery

The images taken by by satellites contain different artifacts with varying size. These images are used as input for many applications. The analysis of satellite images are based on human interpretation and this creates limitations. Object detection from aerial images are challenging as manual detection of objects are infeasible and time consuming. It requires automatic detection of various features and objects from the satellite images. Automated detection systems are designed to run in computing systems with high computing power. A Convolution Neural Network architecture optimized for embedded systems is presented here. Bottom up strategy is adopted and the design starts from small architecture and gradually build into a larger architecture till the design objectives are met.
Detecting Fighter Jets using Convolutional Neural Networks and TransferLearning

Deep learning methods such as Convolutional Neural Networks (CNN) and Transfer Learning are utilized in this research to locate and recognize fighter jets and aircraft’s. The aforementioned methods are currently being applied to a dateset that contains twenty distinct airplanes and mine thousand plus photographs. The ”pooling” idea, Which gradually reduces the spatial dimension of the model in order to decrease the number of parameters and computations in the network, is the foundation upon which CNN is built. CNN’s are utilized extensively for the purpose of image detection across a variety of industries, including the military, agriculture, business, and other forms of face recognition technology. Transfer learning is a method of machine learning in which a model that was developed for one job is reused as the starting point for a model that is being developed for a different task. Transfer learning is not only a topic of research in the field of deep learning; it is also connected to problems such as learning several tasks at once and concept drift. The information is processed, and various Python modules, including pandas, seaborn, sci-kit learn, and others, are utilized in an effort to locate any per-trained patterns and insights. The data is divided into a train datasets and a test datasets, each of which contains 80 and 20 percent of the total data. The TensorFlow library for CNN is utilized in the construction of a model. ”Accuracy” is the metric that is employed. Additionally, a transfer learning model is constructed in order to evaluate the various accuracy results and select the most appropriate one.
Biomedical Named Entity Recognition from Malaria Literature using BioBERT

The unprecedented growth of unstructured text data in the healthcare domain makes it difficult to find and extract relevant information manually. Biomedical named entity recognition deals with the process of automatically identifying entities of clinical significance, such as symptoms, treatment, names of drugs, and organisms, from unstructured text documents such as electronic health records and radiology reports. This is the first but essential step for building many text-understanding applications such as conversational agents and information retrieval. Pre-trained language or linguistic models have recently gained popularity in natural language processing due to their inherent ability to manage contexts better. The proposed approach fine-tunes the pre-trained BioBERT model, which is one of the current state-of-the-art biomedical language models, on a large set of Malaria text to extract named entities. This work implements and compares different machine learning algorithms on different feature extraction techniques to establish the usefulness of the proposed approach. The method proposed is found to be outperforming our chosen baselines and shows better precision and f-measure on our extensive performance comparison experiments.
Integration of a Full Wavelet-Based Image Compression System on a Field-Programmable Gate Array

The increasing significance of having an efficient compression method for raw images before transmission or storage stems from their substantial storage requirements on computer hard drives. The proposed method for compressing images necessitated the implementation of a hardware design pipeline to execute a two-dimensional Distributed Arithmetic (DA) for the Discrete Wavelet Transform (DWT). Before conducting the conventional two-dimensional discrete wavelet transform (DWT) on the input image, the Distributed Arithmetic (DA) technique employs a lookup table (LUT) based FPGA architecture to construct a filter bank devoid of multipliers. In image compression, utilizing Differential Pulse-Code Modulation (DPCM) involves the computation of the Discrete Wavelet Transform (DWT) and the corresponding wavelet coefficients. The Huffman encoder, constructed by amalgamating the least probable symbols, is then provided with the modified image for compression. The efficacy of the revised DA-based wavelet is assessed by using a Simulink model before its implementation. In the context of this approach, the code is implemented using the Verilog programming language. The proposed methodology evaluates the traditional approach concerning adders, multipliers, internal memory capacity, computation time, power consumption, image dimensions, and hardware efficacy.
Non-Gradient Based Design Metrics for Underwater Image Enhancement

Non-gradient-based design metrics are objective measures used to evaluate the effectiveness of image enhancement techniques for underwater images. These metrics focus on improving various aspects of enhanced images, such as color correction, contrast enhancement, noise reduction, and overall visual quality. They address the challenges posed by underwater conditions, including color distortion, poor contrast, and noise. Some common non-gradient-based metrics include assessing color constancy and color reproduction accuracy to restore accurate color information. Contrast improvement metrics measure the effectiveness of enhancing object visibility, while noise reduction metrics evaluate the removal of noise artifacts. Visibility enhancement and image clarity metrics focus on improving the overall quality of underwater images. Color correction metrics aim to correct color imbalances, and perceptual metrics assess the perceived image quality. Robustness metrics consider performance under varying underwater conditions, while computational efficiency and energy consumption metrics evaluate algorithm practicality. A combination of these metrics ensures a comprehensive evaluation of underwater image enhancement techniques, essential for various applications, including marine research, underwater exploration, and surveillance.