Reviewerly/exHarmony huggingface dataset:
Development of Algebraic Structures and Their Application to Ensure the Security of Digital Information

Today, data are a resource that is more important than ever for any organization we could imagine of. Big data analysis, cloud technology, monitoring devices, as well as other contemporary discoveries and developments, are enabling widespread, successful data collection. Yet, privacy and security of information are vital if data are to be exploited to their maximum potential. Despite the fact that data safety and confidentiality have been intensively researched over the past 35 years, companies now face novel, severe privacy and security information issues. A number of these challenges are driven by expanding private information concerning database use as well as the desire to integrate confidentiality with information use such as cybersecurity. So, it is important to ensure that data transfer is secure and safe given the networking industry’s ongoing evolution. To keep our data secure, a growing number of cryptographic solutions are being created every day. In this research, an algebraic structure on any arbitrary non-negative integer has been designed to provide this level of security and used to show encryption and decryption algorithms utilizing various keys.
Boosting Light Field Image Super Resolution Learnt From Single-Image Prior

In recent years, many deep learning networks are proposed for light field super resolution (LFSR). LFSR problem is essentially ill-posed since unknown detail information need to be predicted. Hence LFSR networks require plentiful content information (e.g., shape, color, texture) learned from sufficiently diverse scenarios. However, due to the high collection cost, existing light field datasets are in small size and have few scenarios, which could not meet the requirement and limit the performance of LFSR networks. To solve this problem, we proposed a novel framework to significantly boost their performance. Our main idea is to introduce valuable and plentiful content information from single images into LFSR networks as prior. Specifically, first, a view synthesis method is applied to add unreal disparity into single images, increasing the dimensionality of single images, hence to solve the problem of inconsistent data modalities. Then, we design the Scenarios-Content Introduction Module (SCIM) to effectively extract content feature from synthesized data. Finally, due to the added unreal disparity in the first stage, the features have severely pseudo information. Hence the Feature Attention Module (FAM) is proposed to discriminately select valuable information and combine this information into the LFSR network. Extensive experiments on six datasets validate the effectiveness of the proposed method, leading to a maximum gain of 0.439 dB. Our method can even boost SOTA networks to achieve higher performance
Assessing Information and Communication Technology (ICT) Competencies of Non-teaching Personnel (NTP) of Deped-Schools Division of ILOCOS SUR (IMRAD)

Non-teaching Personnel (NTP) are considered the backbone of the Department of Education, and their functions are deemed vital to the operations of the Department. Numerous studies and training about Information and Communication Technology (ICT) have focused on teachers and students, but fewer studies have been done explicitly with NTP. This study aims to assess the ICT competence level among NTPs at the School Division of Ilocos Sur. A descriptive-correlational method was used to discuss NTP's level of ICT competence in different target ICT domains through a standardized survey questionnaire adopted from the National Information and Communications Technology Standards (NICS) of the Commission on Information and Communications Technology (CICT). A total of two hundred thirty-seven (237) NTPs of the Schools Division of Ilocos Sur were randomly selected as respondents for the study. Data was gathered through the utilization of Google Forms to ensure accurate and fast data gathering. Frequency, Percentage, Mean, and Pearson correlation coefficient analysis were used as a statistical tool to analyze the results using IBM Statistical Packages for Social Sciences (SPSS) Statistics 27 software. The findings revealed that the NTPs exhibit an advanced degree of ICT proficiency, as indicated by a mean score of 2.87 and a standard deviation of 0.69 in all three (3) domains. Moreover, age, length of employment, and educational attainment negatively correlate to ICT competence. However, attending seminars is positively correlated with competence. The researchers proposed a three-day training workshop on ICT Literacy for SDO-Ilocos Sur’s Non-Teaching Personnel to enhance and improve NTPs’ ICT competence.
TPAD: Temporal-Pattern-Based Neural Network Model for Anomaly Detection in Multivariate Time Series

The mixing process of battery production contains a variety of monitoring sensors. These sensors generate a large number of multivariate time series during production, which reflect the potential operating conditions of the production equipment. By accurately detecting anomalies in the equipment, battery quality can be improved. However, due to the diverse types of anomalies in the mixing process and the large number of subtle anomalies, it is challenging to construct a robust and accurate anomaly detection model. In this article, we propose TPAD, a multivariate time series anomaly detection model based on temporal pattern, which learns serial temporal-pattern-based on attention mechanism and detects anomalies by temporal pattern discrepancy, making the model adaptive to detect multiple types of anomalies. Moreover, the model improves the accuracy of detecting subtle anomalies through an adversarial training reconstruction method. Finally, we conducted experiments on two public datasets and a battery mixing process dataset from a battery factory, and the experimental results outperformed the baseline methods, with an average F1 score of over 92% on the three datasets.
Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach

Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analysing the encoded representation’s attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach’s prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy.
Disarming Attacks Inside Neural Network Models

Similar to the revolution of open source code sharing, Artificial Intelligence (AI) model sharing is gaining increased popularity. However, the fast adaptation in the industry, lack of awareness, and ability to exploit the models make them significant attack vectors. By embedding malware in neurons, the malware can be delivered covertly, with minor or no impact on the neural network’s performance. The covert attack will use the Least Significant Bits (LSB) weight attack since LSB has a minimal effect on the model accuracy, and as a result, the user will not notice it. Since there are endless ways to hide the attacks, we focus on a zero-trust prevention strategy based on AI model attack disarm and reconstruction. We proposed three types of model steganography weight disarm defense mechanisms. The first two are based on random bit substitution noise, and the other on model weight quantization. We demonstrate a 100% prevention rate while the methods introduce a minimal decrease in model accuracy based on Qint8 and K-LRBP methods, which is an essential factor for improving AI security.
SCNet: A Self-Calibrating Unsupervised Low-Light Image Enhancement Network

Image acquired by camera sensors suffers from low contrast and poor visibility under low-light conditions. Given the importance of enhancing low-light images, this article presents an innovative method known as the self-calibrating neural network (SCNet). This approach involves the extraction of a lighting adjustment feature map (LafMap) from the input image. Matrix operations are then employed on the input image to derive an intermediate image, which is subsequently optimized through self-calibration (SC) to produce the final output image. The SCNet approach has proven effective in experimentation, as indicated by the peak signal-to-noise ratio (PSNR) values, demonstrating a 6.1% increase compared with the baseline on the low-light dataset (LOL dataset). The SCNet can be trained without the necessity for reference images, overcoming the challenge of insufficient enhancement often observed in very low-light images, which is a prominent problem in existing unsupervised low-light enhancement methods.
A Deep Learning-Based Framework for Offensive Text Detection in Unstructured Data for Heterogeneous Social Media

Social media such as Facebook, Instagram, and Twitter are powerful and essential platforms where people express and share their ideas, knowledge, talents, and abilities with others. Users on social media also share harmful content, such as targeting gender, religion, race, and trolling. These posts may be in the form of tweets, videos, images, and memes. A meme is one of the mediums on social media which has an image and embedded text in it. These memes convey various views, including fun or offensiveness, that may be a personal attack, hate speech, or racial abuse. Such posts need to be filtered out immediately from social media. This paper presents a framework that detects offensive text in memes and prevents such nuisance from being posted on social media, using the collected KAU-Memes dataset 2582. The latter combines the “2016 U.S. Election” dataset with the newly generated memes from a series of offensive and non-offensive tweets datasets. In fact, this model uses the KAU-Memes dataset containing symbolic images and the corresponding text to validate the proposed model. We compare the performance of three proposed deep-learning algorithms to train and detect offensive text in memes. To the best of the authors knowledge and literature review, this is the first approach based on You Only Look Once (YOLO) for offensive text detection in memes. This framework uses YOLOv4, YOLOv5, and SSD MobileNetV2 to compare the model’s performance on the newly labeled KAU-Memes dataset. The results show that the proposed model achieved 81.74%, 84.1%, mAP, and F1-score, respectively, for SSD-MobileNet V2, and 85.20%, 84.0%, mAP, and F1-score, respectively for YOLOv4. YOLOv5 had the best performance and achieved the highest possible mAP, F1-score, precision, and recall which are 88.50%, 88.8%, 90.2%, and 87.5%, respectively, for YOLOv5.
PW-MFL: Promoting Semantic Segmentation in Resolution-Degraded Aerial Images via Pixel-Wise Mutual-Feed Learning

Due to variable imaging conditions, the resolution degradation often occurs in aerial images, which in turn impairs the performance upper bound of semantic segmentation. To solve this problem, super-resolution is placed before semantic segmentation as a pre-processing sub-task in most existing methods. The above two sub-tasks often form a unidirectional open-loop structure for relatively independent optimization, which constrains the ultimate segmentation performance improvement. To break down information barriers among them and form a more compact overall optimization, we propose an effective learning method named as Pixel-Wise Mutual-Feed Learning (PW-MFL) for segmenting images with resolution degradation. The key is to build auxiliary bidirectional connections, which contribute to the mutual pixel-wise spatial and feature information guidance during training. The feed-forward connection is realized by the Self-Attention Context Correlation (SACC) module, which enhances the intra-class semantic features of pixel positions with poor super-resolution performance by the fusion of that with superior performance. The feed-back connection is achieved by the Semantic Weighted Mapping (SWM) module, which aims to activate and adjust the detailed features of super-resolution in incorrectly segmented pixel positions via the semantic feature information. In addition, the Pixel-Aware Optimization (PAO) strategy is proposed to give more attention to optimizing specific pixel positions based on spatial information. Extensive experiments are conducted on three representative remote sensing segmentation benchmarks, ISPRS Vaihingen, ISPRS Potsdam, and iSAID datasets. The state-of-the-art segmentation level in resolution-degraded aerial images is achieved through the proposed learning method.
Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm

Utility-based computing popularly known as “cloud computing” offers several computing services to the users. Due to the proliferation in the users of cloud computing, there is an unprecedented increase in the demand for computation resources to execute cloud services. Thus, there is a requirement to investigate currently available resources like virtual machines, CPU, RAM, and storage to allocate cloud services. The allocation and QoS of cloud services are highly dependent on allocation schemes. The optimized solutions allocate resources to submitted jobs to reduce the overall cost to the end-users/service provider without degrading the performance of virtual machines. The allocation techniques also consider the harvesting of energy consumption required for running the cloud services. In this paper, we have utilized a Rock Hyrax-based optimization technique to allocate resources to the submitted jobs with reduced energy consumption. The proposed Rock Hyrax algorithm has been simulated on the CloudSim simulator for various scenarios. The performance of the proposed algorithm has been measured over various Quality of Service (QoS) parameters such as makespan, energy efficiency, response time, throughput, and cost. The gathered results validate the proposed algorithm that improves the QoS parameters by 3%-8% as compared to algorithms when both jobs and resources are considered to be dynamic in nature.
Hypothesis Margin-Based Ensemble Method for the Classification of Noisy Remote Sensing Data

The accuracy of a classifier, whether it is an ensemble or not, is directly influenced by the training data used in learning. In remote sensing, training data mislabeling is inevitable and faces a major challenge. This paper proposes a versatile data cleaning which handles the mislabeling problem by exploiting the ensemble concepts for identifying, then eliminating or correcting the mislabeled training data. A powerful ensemble method, random forest, is at the core of our filter design and helps to distinguish mislabeled data from uncorrupted data more accurately. The major contribution of this work lies on the explicit use of the hypothesis margin as a decision means to identify and eliminate or correct mislabeled training data in an ensemble learning framework. Another key development that makes our algorithm superior to existing approaches is a design that avoids rare class instances to be mistaken for class noise. This fundamental aspect makes our data cleaning system particularly suitable for remote sensing classification tasks which usually suffer from both mislabeling and imbalance problems. The effectiveness of our algorithm is demonstrated in performing mapping of land covers. The generalization performance of two major supervised noise-sensitive classifiers, boosting and K-nearest neighbors, is strengthened by effective class noise reduction. A comparative analysis is conducted with respect to random forest, deep convolutional neural networks, as well as two well-established ensemble-based class noise filters, the majority vote and the consensus vote filters. This analysis demonstrates that our approach is more accurate than deep convolutional neural networks (one-dimensional CNN, AlexNet, EfficientNet, ResNet50 and ShuffletNet) and the reference ensemble methods.
LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Recognizing the semantic categories of low-resolution (LR) aerial photos is an indispensable technique in geoscience and remote sensing. However, it is also a challenging task in practice. In this work, a semi-supervised perceptual feature selection (SPFS) pipeline is proposed for LR aerial photo categorization, focusing on selecting high quality perception-guided visual features. Specifically, by mimicking human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually or semantically salient foreground regions coupled with the background non-salient regions. This model can 1) produce the a gaze shifting path (GSP) simulating human gaze behavior, and 2) generate hierarchical deep representation for a GSP. Afterward, a semi-supervised feature selection (FS) is leveraged toward a succinct set of discriminative deep GSP features, wherein only labels of LR aerial photos are required. Based on the selected features, a classifier is trained for visual categorization. Comprehensive experimental results have validated our method’s advantage.
nMorph Framework: An Innovative Approach to Transpiler-Based Multi-Language Software Development

In the evolving landscape of software development, the nMorph framework introduces a potential advancement in multi-programming language development. This framework is designed to allow developers to code in a single language that can be transpiled into multiple target languages, aiming to maintain single design integrity while offering the potential for customization, technological adaptability, and platform independence. Architecturally, nMorph focus on the back-end, incorporating a layered design that includes components such as ORM & Database Connectors and a dedicated security layer. Central to this construct is the Haxe transpiler, which is designed to transform between languages such as Java, C#, and PHP. The framework also considered the support for connecting databases such as the Oracle, SQL Server, MySQL and PostgreSQL. Also the preparation for running in IIS, Apache, Tomcat, Glassfish and others. A controlled empirical experiment was conducted in which novice programmers used the nMorph framework in comparison with traditional programming languages in a simple banking-like transactional exercise. Results showed that participants took an average of 15.19% longer when using nMorph compared with commonly used programming languages. Despite that, with nMorph they obtained a multi-language and multi-platform software. Some other factors were analyzed about the influence of individual developer proficiency on outcomes, suggesting areas where the nMorph could be refined. This study underscores the relationship between perceived task difficulty, language proficiency, and software development phases. This study was primarily lab-based. There is potential value in further exploring real-world applications and broadening the scope of the framework to assess its broader relevance and efficacy.
Exploring the Effectiveness of Combined Cosine Similarity and Convolutional Neural Networks for Text Similarity Analysis

Text similarity is a crucial concept in information retrieval and natural language processing, playing a significant role in various applications such as document clustering, text categorization, plagiarism detection, and recommendation systems. These applications are widely used in a variety of industries, including as social media, finance, healthcare, and the financial sector. The user experience can be improved, document organisation can be improved, and search results can be improved with the use of accurate text similarity measurements. Yet, because real language is so complicated and variable, measuring text similarity is a difficult process. Thus, one of the most important study areas in the field of NLP is the development of efficient text similarity metrics. In this study, we use a CNN network to extract the features after the initial pre- processing stages and assigning weights using the TFIDF method. The output is passed onto a bidirectional LSTM model to extradite the dependencies of the text and the output is assessed using cosine similarity. Based on the evaluation results, the proposed system yielded better results than the conventional machine learning approaches. The proposed model yielded an accuracy of 92%.
Multidrone Mission Execution With EAMOS: From Text to Mission

Existing software tools for specifying and executing multidrone missions are limited to route planning or tightly coupled to specific drone hardware. We introduce EAMOS (Execution of Aerial Multidrone Missions and Operations Specification Framework), which allows us to specify missions intuitively, text-based, and provides a mission compiler, a mission middle layer, and a distributed drone execution environment. The middle layer wraps the control of individual drone-specific capabilities, such as launch, fly to position, or perform a maneuver, into a public API that transparently utilizes the capabilities of numerous drone platforms. We exploit the Go programming language to implement critical components of the framework and provide an interface for ROS-based drone platforms. EAMOS automates the mission execution on real, virtual, and even hybrid robotic setups involving real and virtual drones. We demonstrate the successful deployment of EAMOS with four missions executed on Pixhawk/PX4-equipped quadcopters and virtual drones simulated with Airsim. We assess the performance of our proposed approach by analyzing the number of nodes and arcs of the mission graphs, which are an essential artifact of our mission compilation, the utilization of ROS service calls during mission execution, and the duration of compilation, deployment, and mission execution. Overall, our experiments showed that our drones correctly behaved during mission execution as expected and specified by their mission, the generated mission artifacts were efficiently manageable, and processing times allowed for a fluent workflow.